A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities

Abu Saleh Musa Miah; Jungpil Shin; Taro Suzuki

REVIEW 3 major objections 5 minor 299 references

A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This review maps Parkinson's disease detection research across six data modalities and claims to be the first to compare them side by side, with benchmark tables of datasets and reported accuracies.

desk verdict Broad PD-detection survey whose benchmark tables are not trustworthy: a handwriting paper is cited as MRI preprocessing and the discussion contains sign-language content, so the paper does not deliver its promised comprehensive reference. read the letter →

arxiv 2505.00525 v1 pith:VIOUFLH3 submitted 2025-05-01 eess.IV cs.CVcs.LG

Abu Saleh Musa Miah , taro Suzuki , Jungpil Shin This is my paper · ORCID

classification eess.IVcs.CVcs.LG

keywords Parkinson'sdiseasedetectionmachinelearningdeepmultimodalfusionbenchmarkdatasetsgaitanalysisspeechEEG

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a survey that tries to establish a complete map of machine-learning and deep-learning approaches to detecting Parkinson's disease across six data modalities: MRI, video-based pose and gait, wearable sensors, handwriting, speech, and EEG, plus multimodal combinations. It claims to be the first review to cover all six modalities side by side, and it provides benchmark tables that list datasets, methods, and reported accuracies for each modality. If the map is reliable, a researcher could use it to choose a modality, a dataset, and a starting model without re-searching the literature. The paper's own conclusion is that single-modality systems are mature but fragmented, and that multimodal fusion and continuous, real-time recognition are the open problems.

What carries the argument

The central object is the modality-structured benchmark table: each table gathers datasets, class counts, sample sizes, feature-extraction method, classifier, reported accuracy, and stated limitations for one modality. These tables do the paper's main work by turning a scattered literature into rows that can be scanned across modalities, and they are also where the paper's reliability claims live.

What would settle it

Pick one table, such as the MRI preprocessing table, and look up each cited source: a handwriting-study citation appearing in that section would fail this check, and finding a comparable share of misplaced references would show the tables cannot be trusted as a modality guide.

Watch

Extended reading notes

Core claim

The central claim is that Parkinson's disease detection research is best understood modality by modality, and that a side-by-side view across MRI, gait pose, gait sensors, handwriting, speech, EEG, and multimodal fusion reveals both a common trajectory and modality-specific bottlenecks. The authors argue that this is the first systematic review to cover six data modalities at once, and they support the claim with benchmark tables that compile datasets, sample sizes, classifiers, feature-extraction approaches, and reported accuracies for each track. Read on its own terms, the paper establishes a working reference map of the field, identifies the shift from handcrafted features to deep learning as the dominant trend, and points to small datasets and the absence of robust multimodal frameworks as the main barriers to clinical use.

Load-bearing premise

The load-bearing premise is that the paper correctly sorted each of its 347 sources into the right modality and faithfully transcribed their datasets and accuracies; if that sorting or transcription is wrong, the cross-modal map misleads rather than guides.

Editorial extensions

If this is right

A new researcher can use the modality tables to select a dataset and a baseline model without repeating the literature search.
The reported accuracies should be read as study-specific, not comparable across rows, because datasets and protocols differ.
Multimodal fusion is consistently named as the route to higher diagnostic accuracy, but the paper finds no robust benchmark framework for it yet.
Across every modality, the dominant pattern is a shift from handcrafted features to deep learning, with small datasets the main brake on progress.
Continuous, real-time recognition of Parkinson's symptoms remains an open problem across all modalities.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 'first to cover six modalities' claim is conditional on the 2014-2024 window and on the chosen databases; a different search window could yield earlier or different multimodal surveys.
If the benchmark tables were cleaned and released as structured data, they could become a living registry that researchers update when new Parkinson's results appear.
The presence of sign-language and gesture-recognition material in the discussion and abbreviations suggests part of the text was adapted from another survey; that makes the surrounding synthesis less reliable even where the tables are accurate.
A reader who wants to use this map should re-verify any specific accuracy figure against the original paper before citing it in a comparison.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: nice idea, poor execution. The paper compiles a wide range of AI-based PD detection literature into per-modality tables, which would be a useful starting point if the entries were reliable. But they are not, and there are structural signs that the article-selection and attribution pipeline is broken.

What it does well: it pulls together roughly 347 references across MRI, video/pose, sensor, handwriting, speech, EEG, and multimodal fusion, with tables listing datasets, methods, and reported accuracies. Coverage extends to 2024-2025, and the authors' self-citations are not egregious for a survey.

Where it falls apart: in Section II-B, Table 2 lists Pereira et al. [88] as an MRI preprocessing method using the HandPD dataset. Reference [88] is a handwriting-dynamics paper (SIBGRAPI 2016), and the same reference later appears as "Perai" in the handwriting section. A handwriting study cannot be an MRI preprocessing method; this is not a one-cell typo but evidence that papers are being assigned to categories without sufficient checking.

The Discussion (Section X) wanders into "gesture localization within realistic, uncut, and extended videos", "gesture captioning", and "life log devices", none of which are about PD. The abbreviations list includes ADDSL (Danish Sign Language), HSL (Hong Kong Sign Language), and FPHA (First Person Hand Action) — all unrelated to Parkinson's disease. This suggests the text may have been recycled from a gesture-recognition survey without proper adaptation, violating the paper's own exclusion criteria.

The novelty claim is also thin. The paper says it is the first to cover six modalities, but prior surveys (Loh 2021, Rana 2022, Shaban 2023, Zhao 2025) already span multiple overlapping modalities. Adding one or two modalities to a multimodal survey is incremental, not a first.

The reader's REJECT verdict is fair. The problems are pervasive, not isolated; if the categorization system can put a handwriting study in the MRI section, the benchmark tables cannot be trusted as a reference. The unrelated discussion further suggests the paper needs substantial reworking, not light revision.

Who might still use it: someone doing a quick, shallow scan for dataset names, with the understanding that every entry must be verified against the original source. As a citable reference for a research paper, it is not safe.

My recommendation: desk reject. This is not ready for peer review as a reliable survey; the authors would need to redo the article selection and attribution process from the ground up.

Referee Report

3 major / 5 minor

Summary. This manuscript is a survey of Parkinson's disease (PD) recognition systems across multiple data modalities: MRI, video/pose-based gait analysis, sensor-based gait, handwriting, speech, EEG, other single modalities, and multimodal fusion. Based on 347 articles from 2014–2024, the paper proposes to be the first comprehensive review of PD detection across these modalities, and it provides benchmark tables of datasets and accuracy figures for each modality, along with per-modality discussions of preprocessing, state-of-the-art methods, limitations, and future trends. The central claim is that the paper is a reliable and comprehensive cross-modal reference for researchers developing PD diagnostic systems.

Significance. If the claims were accurate, a cross-modal survey with consolidated benchmark tables would be a useful resource for a community that currently lacks such a reference. The paper is organized by modality, includes a large number of references, and attempts to cover data collection, preprocessing, classifiers, and accuracy. However, the value of a survey rests entirely on the correctness of its paper selection, modality classification, and citation attributions. The manuscript contains several concrete errors that undermine this foundation: a handwriting-dynamics paper is listed as an MRI preprocessing method, non-PD sign-language and gesture-recognition content appears in the discussion and abbreviations, and the stated contribution list promises coverage of modalities (RGB, depth, audio, EMG) that are not actually treated as separate sections. These are not cosmetic issues; they directly invalidate the central claim of a comprehensive, accurate, and reliable review. There is no code, proof, or other machine-checkable artifact to offset these attribution problems.

major comments (3)

[Section II-B, Table 2] Table 2 lists Pereira et al. [88] as an MRI preprocessing method with 'Visual Rhythm approaches' on dataset HandPD and CNN classification. Reference [88] is C.R. Pereira et al., 'Deep learning-aided Parkinson's disease diagnosis from handwritten dynamics' (SIBGRAPI 2016), which is a handwriting-dynamics study, not an MRI study. The same reference appears in Table 10 (handwriting datasets) as 'Perai [88]' with NewHandPD. A handwriting study cannot be an MRI preprocessing method, and the inconsistent author-name rendering compounds the problem. This error is load-bearing because the paper's stated contribution includes 'Creation of Benchmark Dataset Tables' with accurate citations for each modality; if a table intended for MRI preprocessing is populated with non-MRI papers, the tables cannot be trusted as a reference.
[Section X (Discussion) and Abbreviations] The Discussion chapter discusses 'gesture localization within realistic, uncut, and extended videos', 'gesture captioning', and 'life log devices', none of which are PD-specific topics. The Abbreviations list contains ADDSL (Annotated Dataset for Danish Sign Language), HSL (Hong Kong Sign Language), and FPHA (First Person Hand Action), none of which are PD datasets or PD-related terms. This content is inconsistent with the paper's own inclusion/exclusion criteria in Section I-C, which exclude papers that 'only mention PD briefly or indirectly'. The presence of sign-language and general gesture-recognition material indicates that the manuscript has not been properly filtered for PD relevance, and it directly contradicts the claim of a comprehensive and accurate PD-focused survey.
[Section I-E (Contribution) and Abstract] The Contributions section states that 'For the first time, this study systematically examines the advancements in multiple data modalities used in PD detection systems, including RGB, skeleton, depth, audio, EMG, EEG, and multimodal fusion.' The abstract similarly promises coverage of these modalities. However, the actual body of the paper does not contain dedicated sections for RGB, depth, audio, or EMG as standalone modalities; the modality sections are MRI, video/pose, sensor, handwriting, speech, EEG, other single modalities, and multimodal fusion. The claim of what is covered is therefore not supported by the manuscript's content, and the promised scope is not delivered.

minor comments (5)

[Section I-G (Organization)] The organization paragraph lists sections I, III, IV, V, VI, VII, IX, and XI, but omits Section VIII ('Other Single Modalities'), which does appear in the body. The reading of the paper's structure would be easier if all sections were listed.
[Table 2] Several rows in Table 2 list datasets that are not MRI datasets for PD, such as Noor et al. [89] with ADNI, OASIS, and MIRIAD (Alzheimer's datasets). If these are included as MRI-based PD preprocessing work, the relevance to PD should be explicitly justified, and if not, the rows should be removed.
[Abbreviations] Many abbreviations listed (e.g., RTDPDS, SMKD, SSC-DNN, HDCAM) do not appear to be used in the text, while other abbreviations used in the text (e.g., MDS-UPDRS) are inconsistently expanded. The list should be pruned and aligned with the actual content.
[Section I-B (Existing PD Detection Survey Papers)] The description of Pereira et al. [59] as a review of 'sleep dysfunction in PD' does not match the reference list entry, which is titled 'A survey on computer-assisted Parkinson's disease diagnosis'. Please verify and correct this description.
[Various tables] The tables contain inconsistent formatting and incomplete entries, such as missing years (e.g., Table 1 rows for 'DS et al [21]' and 'PPMI et al. [78],[79]'), inconsistent author name spellings (e.g., 'Perai' vs. 'Pereira'), and unexpanded abbreviations (e.g., 'DMFEN'). A careful proofreading pass is needed throughout.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the review's synthesis is assembled from external literature; the only circularity-adjacent signal is minor self-citation that is not load-bearing.

full rationale

This paper is a narrative literature review, not a derivation. It contains no fitted parameters, no mathematical model whose prediction is computed from its own inputs, and no benchmark claimed to be predicted from first principles. The central contribution—modality-by-modality summaries and benchmark tables—is compiled from external cited studies. The only self-citation signals are Shin et al. [129] in Table 5 and the text of Section III-C, and Shin et al. [199] in Table 11 and the figure reference in Section V-A. These entries are presented as ordinary literature items and are not load-bearing: removing them would not alter any conclusion, gap analysis, or future-direction claim. The manuscript does contain serious attribution and scope errors—for example, the handwriting study of Pereira et al. [88] appears as an MRI preprocessing method in Table 2, and the discussion/abbreviations include sign-language and gesture-recognition content unrelated to Parkinson's disease. These are correctness and reliability failures in the survey's selection and citation pipeline, but they are not circularity: no step reduces to the paper's own inputs by definition or by self-citation chain. Accordingly, no circular step is identified; the score reflects only the presence of minor, non-load-bearing self-citation.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is a literature review with no new model, derivation, or experiment, so there are no fitted parameters, mathematical axioms, or invented entities. The assumptions that matter are about the reliability of the literature selection and citation attribution, which are captured in weakest_assumption and the red_flags.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities." pith.science (2026). https://pith.science/paper/VIOUFLH3

@misc{pith2026250500525,
  author       = {Pith},
  title        = {Pith review of: A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VIOUFLH3}},
  note         = {Machine review of arXiv:2505.00525}
}

read the original abstract

Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally 1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 347 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.

Figures

Figures reproduced from arXiv: 2505.00525 by the authors.

**Figure 2.** FIGURE 2: Article types: journal, conference, and others. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** FIGURE 3: Year-wise peer-reviewed publications used in the [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 6.** Figure 6: FIGURE 6: The basic flowgraph of the PD research work. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 7.** Figure 7: FIGURE 7: Modality-based dataset names included in the [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: FIGURE 8: Pose extraction (a) hand and face skeleton joints [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

**Figure 9.** Figure 9: FIGURE 9: Example of sensor based pd recognition working [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]

**Figure 10.** Figure 10: FIGURE 10: Pen tablet device used to collect hand writing [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]

**Figure 11.** Figure 11: FIGURE 11: Handwriting based pd recognition flowgrap [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

299 extracted references · 76 canonical work pages

[88]

Pereira, S.A

C.R. Pereira, S.A. Weber, C. Hook, G.H. Rosa, and J.P. Papa. Deep learning-aided parkinson’s disease diagnosis from handwritten dynamics. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 340–346. IEEE, 2016

2016
[1]

Parkinson’s disease from an ayurveda per- spective: Opportunities and challenges for further research

Satyajit Pandurang Kulkarni. Parkinson’s disease from an ayurveda per- spective: Opportunities and challenges for further research. Translational Models of Parkinson’s Disease and Related Movement Disorders, pages 345–360, 2025

2025
[2]

Parkinson’s disease: mechanisms and models

William Dauer and Serge Przedborski. Parkinson’s disease: mechanisms and models. Neuron, 39(6):889–909, 2003

2003
[3]

Assessment of parkinson disease manifestations

Joel S Perlmutter. Assessment of parkinson disease manifestations. Current protocols in neuroscience, 49(1):10–1, 2009

2009
[4]

Accuracy of clinical di- agnosis of parkinson disease: a systematic review and meta-analysis

Giovanni Rizzo, Massimiliano Copetti, Simona Arcuti, Davide Martino, Andrea Fontana, and Giancarlo Logroscino. Accuracy of clinical di- agnosis of parkinson disease: a systematic review and meta-analysis. Neurology, 86(6):566–576, 2016

2016
[5]

A hybrid system for parkinson’s disease diagnosis using machine learning techniques

Rohit Lamba, Tarun Gulati, Hadeel Fahad Alharbi, and Anurag Jain. A hybrid system for parkinson’s disease diagnosis using machine learning techniques. International Journal of Speech Technology, pages 1–11, 2022

2022
[6]

Patient-centered brain transcriptomic and multimodal imaging determinants of clinical progression, physical activity, and treatment needs in parkinson’s disease

Quadri Adewale, Ahmed Faraz Khan, Sue-Jin Lin, Tobias R Baumeister, Yashar Zeighami, Felix Carbonell, Daniel Ferreira, and Yasser Iturria- Medina. Patient-centered brain transcriptomic and multimodal imaging determinants of clinical progression, physical activity, and treatment needs in parkinson’s disease. npj Parkinson’s Disease, 11(1):29, 2025

2025
[7]

S. Fahn, R. Elton, and Members of the UPDRS Development Commit- tee. The unified parkinson’s disease rating scale. In S. Fahn, C.D. Marsden, D.B. Calne, and M. Goldstein, editors,Recent Developments in Parkinson’s Disease, volume 2, pages 153–163. McMellam Health Care Information, Florham Park, 1987

1987

Show all 299 references

[8]

Application of deep learning models for automated identification of parkinson’s disease: A review (2011-2021)

Hui Wen Loh, Wanrong Hong, Chui Ping Ooi, Subrata Chakraborty, Prabal Datta Barua, Ravinesh C Deo, Jeffrey Soar, Elizabeth E Palmer, and U Rajendra Acharya. Application of deep learning models for automated identification of parkinson’s disease: A review (2011-2021). Sensors, ...

2011
[9]

Mukherjee, Z

J. Mukherjee, Z. Y . Yang, T. Brown, J. Roemer, and M. Cooper. 18F- Desmethoxyfallypride: a fluorine-18 labeled radiotracer with properties similar to carbon-11 raclopride for pet imaging studies of dopamine d2 receptors. Life Sciences, 59(8):669–678, 1996

1996
[10]

Gründer, T

G. Gründer, T. Siessmeier, M. Piel, I. Vernaleken, H. Buchholz, Y . Zhou, C. Hiemke, D. Wong, F. Rösch, and P. Bartenstein. Quantifica- tion of d2-like dopamine receptors in the human brain with 18F- Desmethoxyfallypride. Journal of Nuclear Medicine, 44(1):109–116, 2003

2003
[11]

Pahuja and T

G. Pahuja and T. Nagabhushan. A novel GA-ELM approach for parkinson’s disease detection using brain structural T1-weighted MRI data. In Second International Conference on Cognitive Computing and Information Processing (CCIP), pages 1–6, 2016

2016
[12]

Pahuja and B

G. Pahuja and B. Prasad. Deep learning architectures for parkinson’s disease detection by using multi-modal features. Computers in Biology and Medicine, 146:105610, 2022

2022
[13]

P. Liu, H. Wang, S. Zheng, F. Zhang, and X. Zhang. Parkinson’s disease diagnosis using neostriatum radiomic features based on T2-weighted magnetic resonance imaging. Frontiers in Neurology, 11:248, 2020

2020
[14]

Parkinson’s disease: Deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation

Koichiro Yasaka, Koji Kamagata, Takashi Ogawa, Taku Hatano, Haruka Takeshige-Amano, Kotaro Ogaki, Christina Andica, Hiroyuki Akai, Akira Kunimatsu, Wataru Uchida, et al. Parkinson’s disease: Deep learning with a parameter-weighted structural connectome matrix for diagnosis and...

2021
[15]

Y . Wang, N. He, C. Zhang, Y . Zhang, C. Wang, P. Huang, Z. Jin, Y . Li, Z. Cheng, and Y . Liu. An automatic interpretable deep learning pipeline for accurate parkinson’s disease diagnosis using quantitative susceptibility mapping and T1-weighted images. Technical Report 12, W...

2023
[16]

A. S. Talai, J. Sedlacik, K. Boelmans, and N. D. Forkert. Utility of multi-modal MRI for differentiating parkinson’s disease and progressive supranuclear palsy using machine learning. Frontiers in Neurology, 12:648548, 2021

2021
[17]

Shinde, S

S. Shinde, S. Prasad, Y . Saboo, R. Kaushick, J. Saini, P. K. Pal, and M. Ingalhalikar. Predictive markers for parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage: Clinical, 22:101748, 2019

2019
[18]

V . M. Ramírez, V . Kmetzsch, F. Forbes, and M. Dojat. Deep learning models to study the early stages of parkinson’s disease. In IEEE 17th International Symposium on Biomedical Imaging (ISBI), pages 1534– 1537, 2020. VOLUME 4, 2016 37

2020
[19]

https://www.ucl.ac.uk/drc/researchclinical-trials/ minimal-interval-resonance-imaging-alzheimers-disease-miriad, 2024

Minimal interval resonance imaging in alzheimer’s disease (miriad). https://www.ucl.ac.uk/drc/researchclinical-trials/ minimal-interval-resonance-imaging-alzheimers-disease-miriad, 2024. Accessed: 2024-02-01

2024
[20]

Machine learning for neurodegenerative disorder diagnosis—survey of practices and launch of benchmark dataset

Athanasios Tagaris, Dimitrios Kollias, Andreas Stafylopatis, Georgios Tagaris, and Stefanos Kollias. Machine learning for neurodegenerative disorder diagnosis—survey of practices and launch of benchmark dataset. International Journal on Artificial Intelligence Tools, 27(03):18...

2018
[21]

london heath sciences center parkinson’s disease dataset (lhscpd)

Mohamad Abbass, Greydon Gilmore, Alaa Taha, Ryan Chevalier, Mag- dalena Jach, Terry M. Peters, Ali R. Khan, and Jonathan C. Lau. "london heath sciences center parkinson’s disease dataset (lhscpd)", 2023

2023
[22]

Moon, H.-J

S. Moon, H.-J. Song, V .D. Sharma, K.E. Lyons, R. Pahwa, A.E. Akin- wuntan, and H. Devos. Classification of parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: A data-driven approach. ...

2020
[23]

El Maachi, G.-A

I. El Maachi, G.-A. Bilodeau, and W. Bouachir. Deep 1d-convnet for accurate parkinson disease detection and severity prediction from gait. Expert Syst. Appl., 143:113075, 2019

2019
[24]

W. Zeng, F. Liu, Q. Wang, Y . Wang, L. Ma, and Y . Zhang. Parkin- son’s disease classification using gait analysis via deterministic learning. Neurosci. Lett., 633:268–278, 2016

2016
[25]

Muniz, H

A. Muniz, H. Liu, K. Lyons, R. Pahwa, W. Liu, F. Nobre, and J. Nadal. Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimu- lation in parkinson disease on ground reaction force during gait....

2010
[26]

Pfister, T.T

F.M.J. Pfister, T.T. Um, D.C. Pichler, J. Goschenhofer, K. Abedinpour, M. Lang, S. Endo, A.O. Ceballos-Baumann, S. Hirche, and B. et al. Bischl. High-resolution motor state detection in parkinson’s disease using convolutional neural networks. Sci. Rep., 10:5860, 2020

2020
[27]

Eskofier, S.I

B.M. Eskofier, S.I. Lee, J.-F. Daneault, F.N. Golabchi, G. Ferreira- Carvalho, G. Vergara-Diaz, S. Sapienza, G. Costante, J. Klucken, and T. et al. Kautz. Recent machine learning advancements in sensor-based mobility analysis: Deep learning for parkinson’s disease assessment. ...

2016
[28]

Ricci, G

M. Ricci, G. Di Lazzaro, A. Pisani, N.B. Mercuri, F. Giannini, and G. Saggio. Assessment of motor impairments in early untreated parkin- son’s disease patients: The wearable electronics impact.IEEE J. Biomed. Health Inform., 24:120–130, 2019

2019
[29]

Talitckii, E

A. Talitckii, E. Kovalenko, A. Anikina, O. Zimniakova, M. Semenov, E. Bril, A. Shcherbak, D.V . Dylov, and A. Somov. Avoiding misdiagnosis of parkinson’s disease with the use of wearable sensors and artificial intelligence. IEEE Sens. J., 21:3738–3747, 2020

2020
[30]

Wahid, R

F. Wahid, R. K. Begg, C. J. Hass, S. Halgamuge, and D. C. Ackland. Clas- sification of parkinson’s disease gait using spatial-temporal gait features. IEEE Journal of Biomedical and Health Informatics, 19(6):1794–1802, 2015

2015
[31]

C. S. Tucker, S. Conrad, I. Behoora, H. B. Nembhard, M. Lewis, N. W. Sterling, and X. Huang. Machine learning classification of medication adherence in patients with movement disorders using non-wearable sen- sors. Computers in Biology and Medicine, 66:120–134, 2015

2015
[32]

Procházka, O

A. Procházka, O. Vysata, M. Valis, O. Tupa, M. Schätz, and V . Marík. Bayesian classification and analysis of gait disorders using image and depth sensors of microsoft kinect. Digital Signal Processing, 47:169– 177, 2015

2015
[33]

D. I. Margolin and A. M. Wing. Agraphia and micrographia: Clinical manifestations of motor programming and performance disorders. Acta Psychologica, 54:263–283, 1983

1983
[34]

J. G. Phillips, G. E. Stelmach, and N. Teasdale. What can indices of handwriting quality tell us about parkinsonian handwriting? Human Movement Science, 10:301–314, 1991

1991
[35]

Drotár, J

P. Drotár, J. Mekyska, I. Rektorová, L. Masarová, Z. Smékal, and M. Faundez-Zanuy. Analysis of in-air movement in handwriting: A novel marker for parkinson’s disease. Comput. Methods Programs Biomed., 117:405–411, 2014

2014
[36]

C. R. Pereira, D. R. Pereira, F. A. da Silva, C. Hook, S. A. T. Weber, L. A. M. Pereira, and J. P. Papa. A step towards the automated diagnosis of parkinson’s disease: analyzing handwriting movements. In IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS...

2015
[37]

Shaban, B

S. Shaban, B. Nazari, A. Dehghani, B. Abbasi, and A. Abbasi. Automated detection of parkinson’s disease using an optimal 13-layer deep neural network structure with eeg signals. Biomedical Signal Processing and Control, 66:102464, 2021

2021
[38]

Naseer, M

A. Naseer, M. Rani, S. Naz, M.I. Razzak, M. Imran, and G. Xu. Refining parkinson’s neurological disorder identification through deep transfer learning. Neural Comput. Appl., 32:839–854, 2020

2020
[39]

Kamran, S

I. Kamran, S. Naz, I. Razzak, and M. Imran. Handwriting dynamics as- sessment using deep neural network for early identification of parkinson’s disease. Futur. Gener. Comput. Syst., 117:234–244, 2021

2021
[40]

A. Frid, A. Kantor, D. Svechin, and L.M. Manevitz. Diagnosis of parkinson’s disease from continuous speech using deep convolutional networks without manual selection of features. In Proceedings of the 2016 IEEE International Conference on the Science of Electrical Engineering ...

2016
[41]

Tsanas, M.A

A. Tsanas, M.A. Little, P.E. McSharry, and L.O. Ramig. Accurate telemonitoring of parkinson’s disease progression by non-invasive speech tests. Nat. Preced., 57:884–893, 2009

2009
[42]

Rasheed, A.A

J. Rasheed, A.A. Hameed, N. Ajlouni, A. Jamil, A. Ozyavas, and Z. Or- man. Application of adaptive back-propagation neural networks for parkinson’s disease prediction. In Proceedings of the 2020 International Conference on Data Analytics for Business and Industry: Way Towardsa...

2020
[43]

H. Gunduz. Deep learning-based parkinson’s disease classification using vocal feature sets. IEEE Access, 7:115540–115551, 2019

2019
[44]

Karabayir, S.M

I. Karabayir, S.M. Goldman, S. Pappu, and O. Akbilgic. Gradient boosting for parkinson’s disease diagnosis from voice recordings. BMC Med. Inform. Decis. Mak., 20:228, 2020

2020
[45]

Y .N. Zhang. Can a smartphone diagnose parkinson disease? a deep neural network method and telediagnosis system implementation. Park. Dis., 2017:6209703

2017
[46]

T. J. Hirschauer, H. Adeli, and J. A. Buford. Computer-aided diagnosis of parkinson’s disease using enhanced probabilistic neural network.Journal of Medical Systems, 39(11):1–12, 2015

2015
[47]

Vanegas, M.F

M.I. Vanegas, M.F. Ghilardi, S.P. Kelly, and A. Blangero. Machine learning for eeg-based biomarkers in parkinson’s disease. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 2661–2665. IEEE, 2018

2018
[48]

S.L. Oh, Y . Hagiwara, U. Raghavendra, R. Yuvaraj, N. Arunkumar, M. Murugappan, and U.R. Acharya. A deep learning approach for parkinson’s disease diagnosis from eeg signals. Neural Computing and Applications, 32:10927–10933, 2018

2018
[49]

Wagh and Y

N. Wagh and Y . Varatharajah. Eeg-gcnn: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network. In Proceedings of the 2020 Machine Learning for Healthcare Conference (MLHC), pages 367–378. PMLR, 2020

2020
[50]

M. Koch, V . Geraedts, H. Wang, M. Tannemaat, and T. Back. Automated machine learning for eeg-based classification of parkinson’s disease patients. In Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), pages 4845–4852. IEEE, 2019

2019
[51]

X. Shi, T. Wang, L. Wang, H. Liu, and N. Yan. Hybrid convolutional recurrent neural networks outperform cnn and rnn in task-state eeg detec- tion for parkinson’s disease. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA AS...

2019
[52]

S. Lee, R. Hussein, and M.J. McKeown. A deep convolutional-recurrent neural network architecture for parkinson’s disease eeg classification. In Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pages 112–114. IEEE, 2019

2019
[53]

Khare, V

S.K. Khare, V . Bajaj, and U.R. Acharya. Detection of parkinson’s disease using automated tunable q wavelet transform technique with eeg signals. Biocybernetics and Biomedical Engineering, 41:679–689, 2021

2021
[54]

Khare, V

S.K. Khare, V . Bajaj, and U.R. Acharya. Pdcnnet: An automatic frame- work for the detection of parkinson’s disease using eeg signals. IEEE Sensors Journal, 21:17017–17024, 2021

2021
[55]

H. Loh, C. Ooi, E. Palmer, P. Barua, S. Dogan, T. Tuncer, M. Baygin, and U. Acharya. Gaborpdnet: Gabor transformation and deep neural network for parkinson’s disease detection using eeg signals.Electronics, 10:1740
[56]

A. T. Connolly, W. F. Kaemmerer, S. Dani, S. R. Stanslaski, E. Panken, M. D. Johnson, and T. Denison. Guiding deep brain stimulation contact selection using local field potentials sensed by a chronically implanted 38 VOLUME 4, 2016 This paper is currently under review for poss...

2016
[57]

Parkinson’s desease detection by multimodal analysis combining handwriting and speech signals

Catherine Taleb. Parkinson’s desease detection by multimodal analysis combining handwriting and speech signals. PhD thesis, Institut Poly- technique de Paris, 2020

2020
[58]

A systematic review of the literature on disorders of sleep and wakefulness in parkinson’s disease from 2005 to 2015

Lama M Chahine, Amy W Amara, and Aleksandar Videnovic. A systematic review of the literature on disorders of sleep and wakefulness in parkinson’s disease from 2005 to 2015. Sleep medicine reviews, 35:33–50, 2017

2005
[59]

A survey on computer- assisted parkinson’s disease diagnosis.Artificial intelligence in medicine, 95:48–63, 2019

Clayton R Pereira, Danilo R Pereira, Silke AT Weber, Christian Hook, Victor Hugo C De Albuquerque, and Joao P Papa. A survey on computer- assisted parkinson’s disease diagnosis.Artificial intelligence in medicine, 95:48–63, 2019

2019
[60]

Recent survey on parkinson disease diagnose using deep learning mechanism

P Anju, Aleena Varghese, Ashly Roy, Salini Suresh, Emily Joy, and R Sunder. Recent survey on parkinson disease diagnose using deep learning mechanism. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pages 340–343. IEEE, 2020

2020
[61]

Advances in parkinson’s disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects

Laureano Moro-Velazquez, Jorge A Gomez-Garcia, Julian D Arias- Londoño, Najim Dehak, and Juan I Godino-Llorente. Advances in parkinson’s disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects. Biomedical Signal Processing an...

2021
[62]

Exploiting image processing and ai for neurological disorder diagnosis: A focus on alzheimer’s and parkinson’s diseases

Jyoti Kumari, Santi Kumari Behera, and Prabira Kumar Sethy. Exploiting image processing and ai for neurological disorder diagnosis: A focus on alzheimer’s and parkinson’s diseases. In Driving Global Health and Sustainable Development Goals With Smart Technology, pages 213–240....

2025
[63]

Detection and assessment of parkinson’s disease based on gait analysis: A survey

Yao Guo, Jianxin Yang, Yuxuan Liu, Xun Chen, and Guang-Zhong Yang. Detection and assessment of parkinson’s disease based on gait analysis: A survey. Frontiers in aging neuroscience, 14:916971, 2022

2022
[64]

Imperative role of machine learning algorithm for detection of parkinson’s disease: review, challenges and recommendations

Arti Rana, Ankur Dumka, Rajesh Singh, Manoj Kumar Panda, Neeraj Priyadarshi, and Bhekisipho Twala. Imperative role of machine learning algorithm for detection of parkinson’s disease: review, challenges and recommendations. Diagnostics, 12(8):2003, 2022

2003
[65]

Deep learning for parkinson’s disease diagnosis: a short survey

Mohamed Shaban. Deep learning for parkinson’s disease diagnosis: a short survey. Computers, 12(3):58, 2023

2023
[66]

Enhancing parkinson’s disease diagnosis through deep learning-based classification of 3d mri images

Shivani Desai, Hitesh Chhinkaniwala, Smit Shah, and Pranshav Gajjar. Enhancing parkinson’s disease diagnosis through deep learning-based classification of 3d mri images. Procedia Computer Science, 235:201– 213, 2024

2024
[67]

Identification of depression subtypes in parkinson’s disease patients via structural mri whole-brain radiomics: An unsupervised ma- chine learning study

Zihan Zhang, Jiaxuan Peng, Qiaowei Song, Yuyun Xu, Yuguo Wei, and Zhenyu Shu. Identification of depression subtypes in parkinson’s disease patients via structural mri whole-brain radiomics: An unsupervised ma- chine learning study. CNS Neuroscience & Therapeutics, 31(2):e70182, 2025

2025
[68]

Individ- ualized diagnosis of parkinson’s disease based on multivariate magnetic resonance imaging radiomics and clinical indexes

Qianqian Ye, Chenhui Lin, Fangyi Xiao, Tao Jiang, Jialong Hou, Yi Zheng, Jiaxue Xu, Jiani Huang, Keke Chen, Jinlai Cai, et al. Individ- ualized diagnosis of parkinson’s disease based on multivariate magnetic resonance imaging radiomics and clinical indexes. Frontiers in Aging ...

2025
[69]

The role of explainable artificial intel- ligence in disease prediction: a systematic literature review and future research directions

Razan Alkhanbouli, Hour Matar Abdulla Almadhaani, Farah Alhosani, and Mecit Can Emre Simsekler. The role of explainable artificial intel- ligence in disease prediction: a systematic literature review and future research directions. BMC Medical Informatics and Decision Making, ...

2025
[70]

Artificial intelligence-enabled detection and assessment of parkinson’s disease using multimodal data: A survey

Aite Zhao, Yongcan Liu, Xinglin Yu, Xinyue Xing, and Huiyu Zhou. Artificial intelligence-enabled detection and assessment of parkinson’s disease using multimodal data: A survey. Information Fusion, page 103175, 2025

2025
[71]

Optimal dose and type of exercise improve walking velocity in adults with parkinson’s disease: a system- atic review and bayesian network meta-analysis

Shaoqing Xie, Yuan Yuan, Junyu Wang, Ying Bai, Tao Wang, Bopeng Qiu, Yong Yang, and Shu-Cheng Lin. Optimal dose and type of exercise improve walking velocity in adults with parkinson’s disease: a system- atic review and bayesian network meta-analysis. Scientific Reports, 15(1)...

2025
[72]

Boostering diagnosis of frontotemporal lobar degeneration with ai-driven neuroimaging–a systematic review and meta- analysis

Qiong Wu, Dimitra Kiakou, Karsten Mueller, Wolfgang Köhler, and Matthias L Schroeter. Boostering diagnosis of frontotemporal lobar degeneration with ai-driven neuroimaging–a systematic review and meta- analysis. NeuroImage: Clinical, page 103757, 2025

2025
[73]

Analysis of voice, speech, and language biomarkers of parkinson’s dis- ease collected in a mixed reality setting

Milosz Dudek, Daria Hemmerling, Marta Kaczmarska, Joanna Stepien, Mateusz Daniol, Marek Wodzinski, and Magdalena Wojcik-Pedziwiatr. Analysis of voice, speech, and language biomarkers of parkinson’s dis- ease collected in a mixed reality setting. Sensors, 25(8):2405, 2025

2025
[74]

Activity and behavioral recognition using sensing technology in persons with parkinson’s disease or dementia: An umbrella review of the literature

Lydia D Boyle, Lionel Giriteka, Brice Marty, Lucas Sandgathe, Kristoffer Haugarvoll, Ole Martin Steihaug, Bettina S Husebo, and Monica Pa- trascu. Activity and behavioral recognition using sensing technology in persons with parkinson’s disease or dementia: An umbrella review o...

2025
[75]

Review on computational methods for the detection and classification of parkinson’s disease

Komal Singh, Manish Khare, Ashish Khare, and Neena Kohli. Review on computational methods for the detection and classification of parkinson’s disease. Computers in Biology and Medicine, 187:109767, 2025

2025
[76]

Bridging micro and macro: accurate registration of the bigbrain dataset with the mni pd25 and icbm152 atlases

Yiming Xiao, Jonathan C Lau, Taylor Anderson, Jordan DeKraker, D Louis Collins, Terry Peters, and Ali R Khan. Bridging micro and macro: accurate registration of the bigbrain dataset with the mni pd25 and icbm152 atlases. BioRxiv, page 561118, 2019

2019
[77]

A review of machine learning and deep learning algorithms for parkinson’s disease detection using handwriting and voice datasets

Md Ariful Islam, Md Ziaul Hasan Majumder, Md Alomgeer Hussein, Khondoker Murad Hossain, and Md Sohel Miah. A review of machine learning and deep learning algorithms for parkinson’s disease detection using handwriting and voice datasets. Heliyon, 2024

2024
[78]

X. Wu, T. Zheng, and B. Zhang. Exosomes in parkinson’s disease. Neuroscience Bulletin, 33(3):331–338, 2017

2017
[80]

A large public dataset of annotated clinical mris and metadata of patients with acute stroke

Chin-Fu Liu, Richard Leigh, Brenda Johnson, Victor Urrutia, Johnny Hsu, Xin Xu, Xin Li, Susumu Mori, Argye E Hillis, and Andreia V Faria. A large public dataset of annotated clinical mris and metadata of patients with acute stroke. Scientific Data, 10(1):548, 2023

2023
[81]

Parkinson’s image detection and classification based on deep learning

Hui Li, Zixuan Yang, Weimin Qi, Xinchen Yu, Jiaying Wu, and Haining Li. Parkinson’s image detection and classification based on deep learning. BMC Medical Imaging, 24(1):187, 2024

2024
[82]

A. Bhan, S. Kapoor, M. Gulati, and A. Goyal. Early diagnosis of parkin- son’s disease in brain mri using deep learning algorithm. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pages 1467–1470, 2021

2021
[83]

Sangeetha, K

S. Sangeetha, K. Baskar, P. Kalaivaani, and T. Kumaravel. Deep learning- based early parkinson’s disease detection from brain mri image. In 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), pages 490–495, 2023

2023
[84]

Zhang, Y

X. Zhang, Y . Yang, H. Wang, S. Ning, and H. Wang. Deep neural net- works with broad views for parkinson’s disease screening. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1018–1022, 2019

2019
[85]

A. Bhan, S. Kapoor, and M. Gulati. Diagnosing parkinson’s disease in early stages using image enhancement, roi extraction and deep learn- ing algorithms. In 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), pages 521–525, 2021

2021
[86]

Camacho, M

M. Camacho, M. Wilms, P. Mouches, H. Almgren, R. Souza, R. Camici- oli, Z. Ismail, O. Monchi, and N. D. Forkert. Explainable classification of parkinson’s disease using deep learning trained on a large multi-center database of t1-weighted mri datasets. NeuroImage: Clinical, 38...

2023
[87]

Mathys et al

C. Mathys et al. C. Rubbert. Machine-learning identifies parkinson’s disease patients based on resting-state between-network functional con- nectivity. The British Journal of Radiology, 92:20180886, 2019

2019
[89]

M. B. T. Noor, N. Z. Zenia, M. S. Kaiser, S. A. Mamun, and M. Mahmud. Application of deep learning in detecting neurological disorders from magnetic resonance images: A survey on the detection of alzheimer’s disease, parkinson’s disease, and schizophrenia. Brain Informatics, 7...

2020
[90]

I. K. Veetil, E. Gopalakrishnan, V . Sowmya, and K. Soman. Parkinson’s disease classification from magnetic resonance images (mri) using deep transfer learned convolutional neural networks. In 2021 IEEE 18th India Council International Conference (INDICON), pages 1–6, 2021

2021
[91]

Ç. B. Erda¸ s and E. Sümer. A fully automated approach involving neuroimaging and deep learning for parkinson’s disease detection and severity prediction. PeerJ Computer Science, 9:e1485, 2023

2023
[92]

De- tection of parkinson’s disease from 3t t1 weighted mri scans using 3d convolutional neural network

Sabyasachi Chakraborty, Satyabrata Aich, and Hee-Cheol Kim. De- tection of parkinson’s disease from 3t t1 weighted mri scans using 3d convolutional neural network. Diagnostics, 10(6):402, 2020

2020
[93]

Prasuhn, M

J. Prasuhn, M. Heldmann, T.F. Münte, and N. Brüggemann. A machine learning-based classification approach on parkinson’s disease diffusion tensor imaging datasets. Neurology Research and Practice, 2:46, 2020. VOLUME 4, 2016 39

2020
[94]

Multi-modality radiomics of conventional t1 weighted and diffusion tensor imaging for differentiat- ing parkinson’s disease motor subtypes in early-stages

Mehdi Panahi and Mahboube Sadat Hosseini. Multi-modality radiomics of conventional t1 weighted and diffusion tensor imaging for differentiat- ing parkinson’s disease motor subtypes in early-stages. September 2024

2024
[95]

Automatic diagnosis of parkinson’s disease using artificial intelligence base on routine t1-weighted mri

Chang Li, Dongming Hui, Faqi Wu, Yuwei Xia, Feng Shi, Mingguang Yang, Jinrui Zhang, Chao Peng, Junbang Feng, and Chuanming Li. Automatic diagnosis of parkinson’s disease using artificial intelligence base on routine t1-weighted mri. Frontiers in Medicine, 10:1303501, 2024

2024
[96]

A novel technique for classifying parkinson’s disease using structural mri scans.Multimedia Tools and Applications, 82(29):46011–46036, 2023

Ketna Khanna, Sapna Gambhir, and Mohit Gambhir. A novel technique for classifying parkinson’s disease using structural mri scans.Multimedia Tools and Applications, 82(29):46011–46036, 2023

2023
[97]

Altunisik et al

E. Altunisik et al. E. Kaplan. Novel nested patch-based feature ex- traction model for automated parkinson’s disease symptom classification using mri images. Computer Methods and Programs in Biomedicine, 224:107030, 2022

2022
[98]

Sahu and R

L. Sahu and R. Sharma et al. Efficient detection of parkinson’s disease using deep learning techniques over medical data. Expert Systems, 39:e12787, 2022

2022
[99]

Advanced parkinson’s disease detection: A comprehensive artificial intelligence approach utilizing clinical assess- ment and neuroimaging samples

Nusrat Islam, Md Shaiful Alam Turza, Shazzadul Islam Fahim, and Rashedur M Rahman. Advanced parkinson’s disease detection: A comprehensive artificial intelligence approach utilizing clinical assess- ment and neuroimaging samples. International Journal of Cognitive Computing in...

2024
[100]

Explainable classification of parkinson’s disease using deep learning trained on a large multi-center database of t1-weighted mri datasets

Milton Camacho, Matthias Wilms, Pauline Mouches, Hannes Almgren, Raissa Souza, Richard Camicioli, Zahinoor Ismail, Oury Monchi, and Nils D Forkert. Explainable classification of parkinson’s disease using deep learning trained on a large multi-center database of t1-weighted mri...

2023
[101]

A fully automated approach in- volving neuroimaging and deep learning for parkinson’s disease detection and severity prediction

Ça ˘gatay Berke Erda¸ s and Emre Sümer. A fully automated approach in- volving neuroimaging and deep learning for parkinson’s disease detection and severity prediction. PeerJ Computer Science, 9:e1485, 2023

2023
[102]

Deep learning approach to classify parkinson’s disease from mri samples

Nanziba Basnin, Nazmun Nahar, Fahmida Ahmed Anika, Moham- mad Shahadat Hossain, and Karl Andersson. Deep learning approach to classify parkinson’s disease from mri samples. In International conference on brain informatics, pages 536–547. Springer, 2021

2021
[103]

Utility of multi-modal mri for differentiating of parkinson’s disease and progressive supranuclear palsy using machine learning

Aron S Talai, Jan Sedlacik, Kai Boelmans, and Nils D Forkert. Utility of multi-modal mri for differentiating of parkinson’s disease and progressive supranuclear palsy using machine learning. Frontiers in Neurology, 12:648548, 2021

2021
[104]

Ahmadlou and H

M. Ahmadlou and H. Adeli. Enhanced probabilistic neural network with local decision circles: a robust classifier. Integrated Computer-Aided Engineering, 17(3):197–210, 2010

2010
[105]

Kumaran and S

R. Kumaran and S. Shanthini. A hospital application involving deep learning architecture for detecting parkinson’s disease. In 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), page 1–5, 2022

2022
[106]

Kollia, A.-G

I. Kollia, A.-G. Stafylopatis, and S. Kollias. Predicting parkinson’s disease using latent information extracted from deep neural networks. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2019

2019
[107]

Sivaranjini and C

S. Sivaranjini and C. Sujatha. Deep learning based diagnosis of parkin- son’s disease using convolutional neural network. NeuroImage: Clinical, 22:101748, 2019

2019
[108]

Choi and S

H. Choi and S. Ha et al. Refining diagnosis of parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging. NeuroImage: Clinical, 16:586–594, 2017

2017
[109]

Khairnar and P

S. Khairnar and P. Yawalkar. Early-stage detection of parkinson’s disease. Industrial Engineering Journal, 52, 2023

2023
[110]

Mri image-based parkinson’s disease classification using deep maxout fuzzy efficientnet

S Sangeetha and B Umarani. Mri image-based parkinson’s disease classification using deep maxout fuzzy efficientnet. Biomedical Signal Processing and Control, 103:107416, 2025

2025
[111]

Sivaranjini and C

S. Sivaranjini and C. Sujatha. Deep learning based diagnosis of parkin- son’s disease using convolutional neural network. Multimedia tools and applications, 79:15467–15479, 2020

2020
[112]

The cmu motion of body (mobo) database

Ralph Gross and Jianbo Shi. The cmu motion of body (mobo) database. Technical report, Robotics Institute, Pittsburgh, PA, 2001

2001
[113]

J. Zhang. Post-hoc annotation of joints for the cmu mobo database
[114]

A framework for evaluating the effect of view angle, clothing and carrying condition on gait recog- nition

Shiqi Yu, Daoqiang Tan, and Tieniu Tan. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recog- nition. In Proceedings of the 18th International Conference on Pattern Recognition (ICPR), pages 441–444. IEEE, 2006

2006
[115]

Robust view transformation model for gait recognition

Shuai Zheng, Junge Zhang, Kaiqi Huang, Ran He, and Tieniu Tan. Robust view transformation model for gait recognition. In 2011 18th IEEE international conference on image processing, pages 2073–2076. IEEE, 2011

2011
[116]

Mocap database hdm05

Meinard Müller, Thorsten Röder, Michael Clausen, Bernd Eberhardt, Björn Krüger, and Andreas Weber. Mocap database hdm05. Technical Report 2, Institut für Informatik II, Universität Bonn, 2007

2007
[117]

Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion

Leonid Sigal, Alexandru O Balan, and Michael J Black. Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International journal of computer vision, 87(1):4–27, 2010

2010
[118]

A multimodal dataset of real world mobility activities in parkinson’s disease

Catherine Morgan, Emma L Tonkin, Alessandro Masullo, Ferdian Jovan, Arindam Sikdar, Pushpajit Khaire, Majid Mirmehdi, Ryan McConville, Gregory JL Tourte, Alan Whone, et al. A multimodal dataset of real world mobility activities in parkinson’s disease. Scientific data, 10(1):918, 2023

2023
[119]

Loureiro and P

J. Loureiro and P. L. Correia. Using a skeleton gait energy image for pathological gait classification. In Proceedings of the 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pages 503–507, Buenos Aires, Argentina, November 16–20 2020

2020
[120]

Fuselgnet: Fusion of local and global information for detection of parkinson’s disease

Ming Chen, Tao Ren, Pihai Sun, Jianfei Wu, Jinfeng Zhang, and Aite Zhao. Fuselgnet: Fusion of local and global information for detection of parkinson’s disease. Information, 14(2):119, 2023

2023
[121]

Albuquerque, J

P. Albuquerque, J. P. Machado, T. T. Verlekar, P. L. Correia, and L. D. Soares. Remote gait type classification system using markerless 2d video. Diagnostics, 11(10):1824, 2021

2021
[122]

K. Jun, Y . Lee, S. Lee, D.W. Lee, and M.S. Kim. Pathological gait classification using kinect v2 and gated recurrent neural networks. IEEE Access, 8:139881–139891, 2020

2020
[123]

Fp-gcn: Frequency pyramid graph convolutional network for enhancing pathological gait classifica- tion

Xiaoheng Zhao, Jia Li, and Chunsheng Hua. Fp-gcn: Frequency pyramid graph convolutional network for enhancing pathological gait classifica- tion. Sensors, 24(11):3352, 2024

2024
[124]

Khokhlova, C

M. Khokhlova, C. Migniot, A. Morozov, O. Sushkova, and A. Di- panda. Normal and pathological gait classification lstm model. Artificial Intelligence in Medicine, 94:54–66, 2019. [CrossRef] [PubMed]

2019
[125]

Sun and Z.-G

H.-J. Sun and Z.-G. Zhang. Transformer-based severity detection of parkinson’s symptoms from gait. In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pages 1–5. IEEE, 2022

2022
[126]

Pd-net: Quantitative motor function evaluation for parkinson’s disease via automated hand gesture analysis

Yifei Chen, Haoyu Ma, Jiangyuan Wang, Jianbao Wu, Xian Wu, and Xi- aohui Xie. Pd-net: Quantitative motor function evaluation for parkinson’s disease via automated hand gesture analysis. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, page...

2021
[127]

Vision-based finger tapping test in patients with parkinson’s disease via spatial-temporal 3d hand pose estimation

Zhilin Guo, Weiqi Zeng, Taidong Yu, Yan Xu, Yang Xiao, Xuebing Cao, and Zhiguo Cao. Vision-based finger tapping test in patients with parkinson’s disease via spatial-temporal 3d hand pose estimation. IEEE Journal of Biomedical and Health Informatics, 26(8):3848–3859, 2022

2022
[128]

Objective and automatic classification of parkinson disease with leap motion controller

Abdul H Butt, E Rovini, C Dolciotti, G De Petris, P Bongioanni, MC Carboncini, and F Cavallo. Objective and automatic classification of parkinson disease with leap motion controller. Biomedical engineering online, 17(1):1–21, 2018

2018
[129]

Classification of hand- movement disabilities in parkinson’s disease using a motion-capture device and machine learning

Jungpil Shin, Masahiro Matsumoto, Md Maniruzzaman, Md Al Mehedi Hasan, Koki Hirooka, Yuki Hagihara, Naoki Kotsuki, Satomi Inomata- Terada, Yasuo Terao, and Shunsuke Kobayashi. Classification of hand- movement disabilities in parkinson’s disease using a motion-capture device an...

2024
[130]

Kim and Y

W. Kim and Y . Kim. Abnormal gait recognition based on integrated gait features in machine learning. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1683–1688. IEEE, 2021

2021
[131]

T. Khan, A. Zeeshan, and M. Dougherty. A novel method for automatic classification of parkinson gait severity using front-view video analysis. Technology and Health Care, 29(4):643–653, 2021

2021
[132]

Shetty and Y

S. Shetty and Y . Rao. Svm based machine learning approach to identify parkinson’s disease using gait analysis. In2016 International Conference on Inventive Computation Technologies (ICICT), volume 2, pages 1–5. IEEE, 2016

2016
[133]

Abdulhay, N

E. Abdulhay, N. Arunkumar, K. Narasimhan, E. Vellaiappan, and V . Venkatraman. Gait and tremor investigation using machine learning techniques for the diagnosis of parkinson’s disease. Future Generation Computer Systems, 83:366–373, 2018

2018
[134]

J. M. Chavez and W. Tang. A vision-based system for stage classification of parkinsonian gait using machine learning and synthetic data. Sensors, 22(12):4463, 2022. 40 VOLUME 4, 2016 This paper is currently under review for possible publication in IEEE Access

2022
[135]

Muñoz-Ospina, D

B. Muñoz-Ospina, D. Alvarez-Garcia, H. J. C. Clavijo-Moran, J. A. Valderrama-Chaparro, M. García-Peña, C. A. Herrán, C. C. Urcuqui, A. Navarro-Cadavid, and J. Orozco. Machine learning classifiers to evaluate data from gait analysis with depth cameras in patients with parkinson...

2022
[136]

L. Gong, J. Li, M. Yu, M. Zhu, and R. Clifford. A novel computer vision based gait analysis technique for normal and parkinson’s gaits classifi- cation. In 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Int...

2020
[137]

Gait based human parkinson’s disease detection using fused features with multi-kernel sup- port vector machine

Arun Kumar Jhapate and Hemang Shrivastava. Gait based human parkinson’s disease detection using fused features with multi-kernel sup- port vector machine. International Journal of Information Technology, 17(3):1387–1395, 2025

2025
[138]

Exploring motion boundaries in an end-to-end net- work for vision-based parkinson’s severity assessment

Amirhossein Dadashzadeh, Alan Whone, Michal Rolinski, and Ma- jid Mirmehdi. Exploring motion boundaries in an end-to-end net- work for vision-based parkinson’s severity assessment. arXiv preprint arXiv:2012.09890, 2020

2012 arXiv
[139]

B. Chen, C. Chen, J. Hu, Z. Sayeed, J. Qi, H. F. Darwiche, B. E. Little, S. Lou, M. Darwish, and C. Foote. Computer vision and machine learning-based gait pattern recognition for flat fall prediction. Sensors, 22(20):7960, 2022

2022
[140]

A. Zhao, L. Qi, J. Dong, and H. Yu. Dual channel lstm based multi- feature extraction in gait for diagnosis of neurodegenerative diseases. Knowledge-Based Systems, 145:91–97, 2018

2018
[141]

H. Tian, X. Ma, H. Wu, and Y . Li. Skeleton-based abnormal gait recognition with spatio-temporal attention enhanced gait-structural graph convolutional networks. Neurocomputing, 473:116–126, 2022

2022
[142]

J. Ajay, C. Song, A. Wang, J. Langan, Z. Li, and W. Xu. A pervasive and sensor-free deep learning system for parkinsonian gait analysis. In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 108–111. IEEE, 2018

2018
[143]

Q. Zou, Y . Wang, Q. Wang, Y . Zhao, and Q. Li. Deep learning-based gait recognition using smartphones in the wild. IEEE Transactions on Information Forensics and Security, 15:3197–3212, 2020

2020
[144]

A. Zhao, J. Li, J. Dong, L. Qi, Q. Zhang, N. Li, X. Wang, and H. Zhou. Multimodal gait recognition for neurodegenerative diseases. IEEE transactions on cybernetics, 52(9):9439–9453, 2021

2021
[145]

Balaji, D

E. Balaji, D. Brindha, V . K. Elumalai, and R. Vikrama. Automatic and non-invasive parkinson’s disease diagnosis and severity rating using lstm network. Applied Soft Computing, 108:107463, 2021

2021
[146]

K. V . S. S. Kumar, I. Sirisha, K. Vathsalya, and K. K. V . V . Vamsi. Parkinson disease diagnosis and severity rating prediction based on gait analysis using deep learning. International Research Journal on Advanced Science Hub, 5(05):418–425, 2023

2023
[147]

D. M. D. Nguyen, M. Miah, G.-A. Bilodeau, and W. Bouachir. Trans- formers for 1d signals in parkinson’s disease detection from gait. In2022 26th International Conference on Pattern Recognition (ICPR), pages 5089–5095, 2022

2022
[148]

Cheriet, V

M. Cheriet, V . Dentamaro, M. Hamdan, D. Impedovo, and G. Pirlo. Multi-speed transformer network for neurodegenerative disease assess- ment and activity recognition. Computer Methods and Programs in Biomedicine, 230:107344, 2023

2023
[149]

Naimi, W

S. Naimi, W. Bouachir, and G.-A. Bilodeau. Hct: Hybrid convnet- transformer for parkinson’s disease detection and severity prediction from gait. In 2023 International Conference on Machine Learning and Applications (ICMLA), pages 814–819. IEEE, 2023

2023
[150]

Wu and A

H. Wu and A. Zhao. Multi-level fine-tuned transformer for gait recognition. In 2022 International Conference on Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI), pages 83–89. IEEE, 2022

2022
[151]

H. Wu, Y . Liu, H. Yang, Z. Xie, X. Chen, M. Wen, and A. Zhao. An attention-based temporal network for parkinson’s disease severity rating using gait signals. KSII Transactions on Internet and Information Systems (TIIS), 17(10):2627–2642, 2023

2023
[152]

Mazilu, U

S. Mazilu, U. Blanke, D. Roggen, J. M. Hausdorff, G. Tröster, E. Gazit, and J. M. Hausdorff. Engineers meet clinicians: augmenting parkin- son’s disease patients to gather information for gait rehabilitation. In Proceedings of the 4th Augmented Human International Conference, ...

2013
[153]

Mazilu, U

S. Mazilu, U. Blanke, D. Roggen, J. M. Hausdorff, and G. Tröster. Engineers meet clinicians: augmenting parkinson’s disease patients to gather information for gait rehabilitation. In Proceedings of the 4th Augmented Human International Conference, pages 124–127, 2013

2013
[154]

Harms, O

H. Harms, O. Amft, R. Winkler, J. Schumm, M. Kusserow, and G. Tröster. Ethos: miniature orientation sensor for wearable human motion analysis. In IEEE Sensors, pages 1037–1042, 2010

2010
[155]

Goldberger, L

A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. C. Ivanov, R. Mark, and H. E. Stanley. Physiobank, physiotoolkit, and physionet: Com- ponents of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220, 2000. Online

2000
[156]

Q. Ye, Y . Xia, and Z. Yao. Classification of gait patterns in patients with neurodegenerative disease using adaptive neuro-fuzzy inference system. Comput. Math. Methods Med., 2018:9831252, 2018. [CrossRef] [PubMed]

2018
[157]

The smart-insole dataset: Gait analysis using wearable sensors with a focus on elderly and parkinson’s patients

Chariklia Chatzaki, Vasileios Skaramagkas, Nikolaos Tachos, Georgios Christodoulakis, Evangelia Maniadi, Zinovia Kefalopoulou, Dimitrios I Fotiadis, and Manolis Tsiknakis. The smart-insole dataset: Gait analysis using wearable sensors with a focus on elderly and parkinson’s pa...

2021
[158]

Daphnet freezing of gait [dataset]

Daniel Roggen, Meir Plotnik, and Jeffrey Hausdorff. Daphnet freezing of gait [dataset]. UCI Machine Learning Repository, 2010

2010
[159]

Physionet pdgait database, 2024

NIGMS. Physionet pdgait database, 2024. Accessed: 2025-04-19

2024
[160]

Physionet ndds database, 2024

NIGMS. Physionet ndds database, 2024. Accessed: 2025-04-19

2024
[161]

Salomon, E

A. Salomon, E. Gazit, P. Ginis, B. Urazalinov, H. Takoi, T. Yamaguchi, S. Goda, D. Lander, J. Lacombe, A. Sinha, A. Nieuwboer, L. Kirsch, R. Holbrook, B. Manor, and J. Hausdorff. A machine learning contest enhances automated freezing of gait detection and reveals time-of-day e...

2024
[162]

A public data set of walking full-body kinematics and kinetics in individuals with parkinson’s disease

Thiago Kenzo Fujioka Shida, Thaisy Moraes Costa, Claudia Eu- nice Neves de Oliveira, Renata de Castro Treza, Sandy Mikie Hondo, Emanuele Los Angeles, Claudionor Bernardo, Luana dos Santos de Oliveira, Margarete de Jesus Carvalho, and Daniel Boari Coelho. A public data set of w...

2023
[163]

A dataset of overground walking full-body kinematics and kinetics in individuals with parkinson’s disease

Daniel Boari. A dataset of overground walking full-body kinematics and kinetics in individuals with parkinson’s disease. figshare. Dataset, 2021. Accessed: 28/04/2025

2021
[164]

T. D. Pham and H. Yan. Tensor decomposition of gait dynamics in parkinson’s disease. IEEE Trans. Biomed. Eng., 65:1820–1827, 2018. [CrossRef]

2018
[165]

Mittra and V

Y . Mittra and V . Rustagi. Classification of subjects with parkinson’s disease using gait data analysis. In Proceedings of the 2018 International Conference on Automation and Computational Engineering (ICACE), pages 84–89, Greater Noida, India, 2018. [CrossRef]

2018
[166]

Klomsae, S

A. Klomsae, S. Auephanwiriyakul, and N. Theera-Umpon. String grammar unsupervised possibilistic fuzzy c-medians for gait pattern classification in patients with neurodegenerative diseases.Comput. Intell. Neurosci., 2018:1869565, 2018. [CrossRef]

2018
[167]

Djuri ´c-Jovi´ci´c, M

M. Djuri ´c-Jovi´ci´c, M. Beli ´c, I. Stankovi ´c, S. Radovanovi ´c, and V . S. Kosti´c. Selection of gait parameters for differential diagnostics of patients with de novo parkinson’s disease. Neurol. Res., 39:853–861, 2017. [CrossRef]

2017
[168]

Cuzzolin, M

F. Cuzzolin, M. Sapienza, P. Esser, S. Saha, M. M. Franssen, J. Collett, and H. Dawes. Metric learning for parkinsonian identification from imu gait measurements. Gait Posture, 54:127–132, 2017. [CrossRef]

2017
[169]

J. P. Felix, F. H. Vieira, Á. A. Cardoso, M. V . Ferreira, R. A. Franco, M. A. Ribeiro, S. G. Araújo, H. P. Corrêa, and M. L. Carneiro. A parkinson’s disease classification method: An approach using gait dynamics and de- trended fluctuation analysis. In Proceedings of the 2019...

2019
[170]

Andrei, A.-M

A.-G. Andrei, A.-M. Taut ˘an, and B. Ionescu. Parkinson’s disease detection from gait patterns. In Proceedings of the 2019 E-Health and Bioengineering Conference (EHB), pages 1–4, Iasi, Romania, 2019. [CrossRef]

2019
[171]

C. Gao, H. Sun, T. Wang, M. Tang, N. I. Bohnen, M. L. Müller, T. Herman, N. Giladi, A. Kalinin, C. Spino, and et al. Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in parkinson’s disease. Sci. Rep., 8:71...

2018
[172]

R. Z. U. Rehman, S. Del Din, Y . Guan, A. J. Yarnall, J. Q. Shi, and L. Rochester. Selecting clinically relevant gait characteristics for classification of early parkinson’s disease: A comprehensive machine learning approach. Sci. Rep., 9:17269, 2019. [CrossRef] [PubMed]. VOLU...

2019
[173]

A new machine learning based approach to predict freezing of gait

Natasa Kleanthous, Abir Jaafar Hussain, Wasiq Khan, and Panos Liatsis. A new machine learning based approach to predict freezing of gait. Pattern Recognition Letters, 140:119–126, 2020

2020
[174]

Alkhatib, M

R. Alkhatib, M. O. Diab, C. Corbier, and M. El Badaoui. Machine learning algorithm for gait analysis and classification on early detection of parkinson. IEEE Sensors Letters, 4(6):1–4, 2020

2020
[175]

S. J. Priya, A. J. Rani, M. S. P. Subathra, M. A. Mohammed, R. Dama˘sevi˘cius, and N. Ubendran. Local pattern transformation based feature extraction for recognition of parkinson’s disease based on gait signals. Diagnostics, 11:1395, 2021. [CrossRef]

2021
[176]

M. S. Baby, A. J. Saji, and C. S. Kumar. Parkinson’s disease classi- fication using wavelet transform based feature extraction of gait data. In Proceedings of the 2017 International Conference on Circuit Power and Computing Technologies (ICCPCT), pages 1–6, Kollam, India, 2017...

2017
[177]

S. V . Perumal and R. Sankar. Gait and tremor assessment for patients with parkinson’s disease using wearable sensors. ICT Express, 2:168– 174, 2016. [CrossRef]

2016
[178]

N. K. Orphanidou, A. Hussain, R. Keight, P. Lishoa, J. Hind, and H. Al- Askar. Predicting freezing of gait in parkinson’s disease patients using machine learning. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–8. IEEE, 2018

2018
[179]

Borzì, L

L. Borzì, L. Sigcha, and G. Olmo. Context recognition algorithms for energy-efficient freezing-of-gait detection in parkinson’s disease. Sensors, 23(9):4426, 2023

2023
[180]

Balaji, D

E. Balaji, D. Brindha, and R. Balakrishnan. Supervised machine learning based gait classification system for early detection and stage classification of parkinson’s disease. Applied Soft Computing, 94:106494, 2020

2020
[181]

A. Zhao, L. Qi, J. Li, J. Dong, and H. Yu. A hybrid spatiotemporal model for detection and severity rating of parkinson’s disease from gait data. Neurocomputing, 315:1–8, 2018

2018
[182]

B. Li, Z. Yao, J. Wang, S. Wang, X. Yang, and Y . Sun. Improved deep learning technique to detect freezing of gait in parkinson’s disease based on wearable sensors. Electronics, 9:1919, 2020. [CrossRef]

1919
[183]

On the bias, fairness, and bias mitigation for a wearable-based freezing of gait detection in parkinson’s disease

Timothy Odonga, Christine D Esper, Stewart A Factor, J Lucas McKay, and Hyeokhyen Kwon. On the bias, fairness, and bias mitigation for a wearable-based freezing of gait detection in parkinson’s disease. arXiv preprint arXiv:2502.09626, 2025

2025
[184]

Ana Lisa Taylor Tavares, Gregory SXE Jefferis, Mandy Koop, Bruce C Hill, Trevor Hastie, Gary Heit, and Helen M Bronte-Stewart. Quan- titative measurements of alternating finger tapping in parkinson’s dis- ease correlate with updrs motor disability and reveal the improvement in...

2005
[185]

Gait disorder assessment based on a large-scale clinical trial: Wifi vs

Alireza Parsay, Mert Torun, Philip R Delio, and Yasamin Mostofi. Gait disorder assessment based on a large-scale clinical trial: Wifi vs. video vs. doctor’s visual inspection. arXiv preprint arXiv:2502.05328, 2025

2025 arXiv
[186]

Sign language recognition using graph and general deep neural network based on large scale dataset

Abu Saleh Musa Miah, Md Al Mehedi Hasan, Satoshi Nishimura, and Jungpil Shin. Sign language recognition using graph and general deep neural network based on large scale dataset. IEEE Access, 2024

2024
[187]

Dynamic hand gesture recognition using multi-branch attention based graph and general deep learning model

Abu Saleh Musa Miah, Md Al Mehedi Hasan, and Jungpil Shin. Dynamic hand gesture recognition using multi-branch attention based graph and general deep learning model. IEEE Access, 2023

2023
[188]

Dynamic fall detection using graph-based spatial temporal convolution and attention network

Rei Egawa, Abu Saleh Musa Miah, Koki Hirooka, Yoichi Tomioka, and Jungpil Shin. Dynamic fall detection using graph-based spatial temporal convolution and attention network. Electronics, 12(15):3234, 2023

2023
[189]

Skeleton-based hand gesture recognition using geometric features and spatio-temporal deep learning approach

Abu Saleh Musa Miah, Jungpil Shin, Md Al Mehedi Hasan, Yusuke Fujimoto, and Asai Nobuyoshi. Skeleton-based hand gesture recognition using geometric features and spatio-temporal deep learning approach. In 2023 11th European Workshop on Visual Information Processing (EUVIP), pag...

2023
[190]

Hand gesture recognition for multi-culture sign language using graph and general deep learning network

Abu Saleh Musa Miah, Md Al Mehedi Hasan, Yoichi Tomioka, and Jungpil Shin. Hand gesture recognition for multi-culture sign language using graph and general deep learning network. IEEE Open Journal of the Computer Society, 2024

2024
[191]

Deep learning-based video face-based digital markers for early detection and analysis of Parkinson disease

Anas Filali Razzouki. Deep learning-based video face-based digital markers for early detection and analysis of Parkinson disease. PhD thesis, Institut Polytechnique de Paris, 2025

2025
[192]

Sensor- based human activity recognition based on multi-stream time-varying features with eca-net dimensionality reduction

Abu Saleh Musa Miah, Yong Seok Hwang, and Jungpil Shin. Sensor- based human activity recognition based on multi-stream time-varying features with eca-net dimensionality reduction. IEEE Access, 2024

2024
[193]

Real-time gesture mapping in pd environment using neural networks

Arshia Cont, Thierry Coduys, and Cyrille Henry. Real-time gesture mapping in pd environment using neural networks. In NIME, pages 39– 42, 2004

2004
[194]

Machine learning-based differential diagnosis of parkinson’s disease using kinematic feature extraction and selection

Masahiro Matsumoto, Abu Saleh Musa Miah, Nobuyoshi Asai, and Jung- pil Shin. Machine learning-based differential diagnosis of parkinson’s disease using kinematic feature extraction and selection. IEEE Access, 13:54090–54104, 2025

2025
[195]

H. L. Teulings and G. E. Stelmach. Control of stroke size, peak acceleration, and stroke duration in parkinsonian handwriting. Human Movement Science, 10:315–334, 1991

1991
[196]

Rosenblum, M

S. Rosenblum, M. Samuel, S. Zlotnik, I. Erikh, and I. Schlesinger. Hand- writing as an objective tool for parkinson’s disease diagnosis. Journal of Neurology, 260:2357–2361, 2013

2013
[197]

Drotar, J

P. Drotar, J. Mekyska, I. Rektorová, L. Masarová, Z. Smékal, and M. Faundez-Zanuy. Evaluation of handwriting kinematics and pressure for differential diagnosis of parkinson’s disease. Artificial Intelligence in Medicine, pages 39–46, 2016

2016
[198]

Raudmann, P

M. Raudmann, P. Taba, and K. Medijainen. Handwriting speed and size in individuals with parkinson’s disease compared to healthy controls: the possible effect of cueing. Acta Kinesiologiae Universitatis Tartuensis, 20:28–36, 2014

2014
[199]

Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning

Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka, Md Al Mehedi Hasan, and Md Maniruzzaman. Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning. arXiv preprint arXiv:2412.17849, 2024

2024 arXiv
[200]

Parkinson’s disease detection from online handwriting based on beta-elliptical approach and fuzzy perceptual de- tector

Mohammed F Allebawi, Thameur Dhieb, Mohamed Neji, Nouha Farhat, Emna Smaoui, Tarek M Hamdani, Mariem Damak, Chokri Mhiri, Bilel Neji, Taha Beyrouthy, et al. Parkinson’s disease detection from online handwriting based on beta-elliptical approach and fuzzy perceptual de- tector....

2024
[201]

M. E. Isenkul, B. E. Sakar, and O. Kursun. Improved spiral test using digitized graphics tablet for monitoring parkinson’s disease. In The 2nd International Conference on e-Health and Telemedicine (ICEHTM-2014), pages 171–175, 2014

2014
[202]

K. Mader. Parkinson’s drawing. Online, 2019

2019
[203]

Zham, D.K

P. Zham, D.K. Kumar, P. Dabnichki, S. Poosapadi Arjunan, and S. Raghav. Distinguishing different stages of parkinson’s disease us- ing composite index of speed and pen-pressure of sketching a spiral. Frontiers in Neurology, 8, 2017

2017
[204]

Prashanth, S.D

R. Prashanth, S.D. Roy, P.K. Mandal, and S. Ghosh. High-accuracy detection of early parkinson’s disease through multimodal features and machine learning. Int. J. Med. Inform., 90:13–21, 2016

2016
[205]

W. R. Adams. High-accuracy detection of early parkinson’s disease using multiple characteristics of finger movement while typing. PLOS ONE, 12:1–20, Nov 2017

2017
[206]

Giancardo

L. Giancardo. Computer keyboard interaction as an indicator of early parkinson’s disease. Scientific Reports, 6:34468, Oct 2016

2016
[207]

V . J., B. A., F. M., C. M. van Alen, L. Plagwitz, and T. W. Machine learning in the parkinson’s disease smartwatch (pads) dataset. npj Parkinson’s Disease, 9:6, Jan 2024

2024
[208]

Handwriting-based adhd detection for children having asd using machine learning approaches.IEEE Access, 2023

Jungpil Shin, Md Maniruzzaman, Yuta Uchida, Md Al Mehedi Hasan, Akiko Megumi, and Akira Yasumura. Handwriting-based adhd detection for children having asd using machine learning approaches.IEEE Access, 2023

2023
[209]

Taylor and J.W

J.C. Taylor and J.W. Fenner. Comparison of machine learning and semi- quantification algorithms for (i123)fp-cit classification: The beginning of the end for semi-quantification? EJNMMI Phys., 4:29, 2017

2017
[210]

Oliveira, D.B

F.P. Oliveira, D.B. Faria, D.C. Costa, M. Castelo-Branco, and J.M.R. Tavares. Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of parkinson’s disease based on [123i]fp-cit spect images. Eur. J. Nucl. Med. Mol. Imaging, 45:105...

2018
[211]

De Souza, S.S

J.W. De Souza, S.S. Alves, E.D.S. Rebouças, J.S. Almeida, and P.P. RebouçasFilho. A new approach to diagnose parkinson’s disease using a structural cooccurrence matrix for a similarity analysis. Comput. Intell. Neurosci., 2018:7613282, 2018

2018
[212]

Hsu, H.C

S.Y . Hsu, H.C. Lin, T.B. Chen, W.C. Du, Y .H. Hsu, Y .C. Wu, P.W. Tu, Y .H. Huang, and H.Y . Chen. Feasible classified models for parkinson disease from 99mtc-trodat-1 spect imaging. Sensors, 19:1740, 2019

2019
[213]

˙I. Kurt, S. Ulukaya, and O. Erdem. Classification of parkinson’s disease using dynamic timewarping. In Proceedings of the 2019 27th Telecommunications Forum (TELFOR), pages 1–4, 2019

2019
[214]

Mabrouk, B

R. Mabrouk, B. Chikhaoui, and L. Bentabet. Machine learning based classification using clinical and datscan spect imaging features: A study 42 VOLUME 4, 2016 This paper is currently under review for possible publication in IEEE Access. on parkinson’s disease and swedd.IEEE Tra...

2016
[215]

Maass, B

F. Maass, B. Michalke, D. Willkommen, A. Leha, C. Schulte, L. Tönges, B. Mollenhauer, C. Trenkwalder, D. Rückamp, M. Börger, et al. Ele- mental fingerprint: Reassessment of a cerebrospinal fluid biomarker for parkinson’s disease. Neurobiol. Dis., 134:104677, 2020

2020
[216]

Mucha, J

J. Mucha, J. Mekyska, M. Faundez-Zanuy, K. Lopez-De-Ipina, V . Zvon- cak, Z. Galaz, T. Kiska, Z. Smekal, L. Brabenec, and I. Rektorova. Advanced parkinson’s disease dysgraphia analysis based on frac- tional derivatives of online handwriting. In Proceedings of the 2018 10th Int...

2018
[217]

Cibulka, M

M. Cibulka, M. Brodnanova, M. Grendar, M. Grofik, E. Kurca, I. Pil- chova, O. Osina, Z. Tatarkova, D. Dobrota, and M. Kolisek. Snps rs11240569, rs708727, and rs823156 in slc41a1 do not discriminate between slovak patients with idiopathic parkinson’s disease and healthy control...

2019
[218]

J. Shi, M. Yan, Y . Dong, X. Zheng, Q. Zhang, and H. An. Multiple kernel learning based classification of parkinson’s disease with multi- modal transcranialsonography. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biolo...

2018
[219]

Segovia, J.M

F. Segovia, J.M. Górriz, J. Ramírez, F.J. Martínez-Murcia, and D. Castillo-Barnes. Assisted diagnosis of parkinsonism based on the striatal morphology. Int. J. Neural Syst., 29:1950011, 2019

2019
[220]

S. Nõmm, K. Bardõš, A. Toomela, K. Medijainen, and P. Taba. Detailed analysis of the luria’s alternating seriestests for parkinson’s disease diag- nostics. In Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 1347–1352, 2018

2018
[221]

Challa, V .S

K.N.R. Challa, V .S. Pagolu, G. Panda, and B. Majhi. An improved approach for prediction of parkinson’s disease using machine learn- ing techniques. In Proceedings of the 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pag...

2016
[222]

Memedi, A

M. Memedi, A. Sadikov, V . Groznik, J. Žabkar, M. Možina, F. Bergquist, A. Johansson, D. Haubenberger, and D. Nyholm. Automatic spiral analysis for objective assessment of motor symptoms in parkinson’s disease. Sensors, 15:23727–23744, 2015

2015
[223]

Detecting neurodegenerative diseases using frame-level handwriting embeddings

Sarah Laouedj, Yuzhe Wang, Jesus Villalba, Thomas Thebaud, Laure- ano Moro-Velazquez, and Najim Dehak. Detecting neurodegenerative diseases using frame-level handwriting embeddings. arXiv preprint arXiv:2502.07025, 2025

2025 arXiv
[224]

A novel feature extraction method based on dynamic handwriting for parkinson’s disease detection.PloS one, 20(1):e0318021, 2025

Huimin Lu, Guolian Qi, Dalong Wu, Chenglin Lin, Songzhe Ma, Yingqi Shi, and Han Xue. A novel feature extraction method based on dynamic handwriting for parkinson’s disease detection.PloS one, 20(1):e0318021, 2025

2025
[225]

Wenzel, F

M. Wenzel, F. Milletari, J. Krüger, C. Lange, M. Schenk, I. Apostolova, S. Klutmann, M. Ehrenburg, and R. Buchert. Automatic classification of dopamine transporter spect: Deep convolutional neural networks can be trained to be robust with respect to variable image characterist...

2019
[226]

M. Peker. A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and svm. Journal of Medical Systems, 40:116, 2016

2016
[227]

Khatamino, ˙I

P. Khatamino, ˙I. Cantürk, and L. Özyılmaz. A deep learning-cnn based system for medical diagnosis: An application on parkinson’s disease handwriting drawings. In Proceedings of the 2018 6th International Conference on Control Engineering & Information Technology (CEIT), pages...

2018
[228]

Park-net: A deep model for early detection of parkinson’s disease through automatic analysis of handwriting

Akram Bennour and Tahar Mekhaznia. Park-net: A deep model for early detection of parkinson’s disease through automatic analysis of handwriting. SN Computer Science, 5(7):1–12, 2024

2024
[229]

Moshkova, A

A. Moshkova, A. Samorodov, N. V oinova, A. V olkov, E. Ivanova, and E. Fedotova. Parkinson’s disease detection by using machine learning algorithms and hand movement signal from leapmotion sensor. In 2020 26th Conference of Open Innovations Association (FRUCT), volume 1, pages...

2020
[230]

L. Tong, J. He, and L. Peng. Cnn-based pd hand tremor detection using inertial sensors. IEEE Sensors Letters, 5(7):1–4, 2021

2021
[231]

Zhao and J

A. Zhao and J. Li. Two-channel lstm for severity rating of parkinson’s disease using 3d trajectory of hand motion. Multimedia Tools and Applications, 81(23):33851–33866, 2022

2022
[232]

R. Guo, H. Li, C. Zhang, and X. Qian. A tree-structure-guided graph convolutional network with contrastive learning for the assessment of parkinsonian hand movements. Medical Image Analysis, 81:102560, 2022

2022
[233]

X. Peng, Y . Zhao, Z. Li, X. Wang, F. Nan, Z. Zhao, Y . Yang, and P. Yang. Multi-scale and multi-level feature assessment framework for classification of parkinson’s disease state from short-term motor tasks. IEEE Transactions on Biomedical Engineering, 2024

2024
[234]

Y . Zhao, X. Wang, X. Peng, Z. Li, F. Nan, M. Zhou, J. Qi, Y . Yang, Z. Zhao, and L. Xu. Selecting and evaluating key mds-updrs activities using wearable devices for parkinson’s disease self-assessment. IEEE Journal of Selected Areas in Sensors, 2024

2024
[235]

Gazda, M

M. Gazda, M. Hires, and P. Drotar. Multiple-fine-tuned convolutional neural networks for parkinson’s disease diagnosis from offline handwrit- ing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1):78–89, 2021

2021
[236]

N. Wang, X. Niu, Y . Yuan, Y . Sun, R. Li, G. You, and A. Zhao. A coordi- nate attention enhanced swin transformer for handwriting recognition of parkinson’s disease. IET Image Processing, 17(9):2686–2697, 2023

2023
[237]

C. Ma, P. Zhang, L. Pan, X. Li, C. Yin, A. Li, R. Zong, and Z. Zhang. A feature fusion sequence learning approach for quantitative analysis of tremor symptoms based on digital handwriting. Expert Systems with Applications, 203:117400, 2022

2022
[238]

Chiga-net: A geneti- cally optimized neural network with refined deeply extracted features using χ2 statistical score for trustworthy parkinson’s disease detection

Liaqat Ali, Man-Fai Leung, Muhammad Asghar Khan, Redhwan Nour, Yakubu Imrana, and Athanasios V Vasilakos. Chiga-net: A geneti- cally optimized neural network with refined deeply extracted features using χ2 statistical score for trustworthy parkinson’s disease detection. Neuroc...

2025
[239]

Towards parkinson’s dis- ease detection through analysis of everyday handwriting

Jeferson David Gallo-Aristizabal, Daniel Escobar-Grisales, Cris- tian David Ríos-Urrego, Jesús Francisco Vargas-Bonilla, Adolfo M García, and Juan Rafael Orozco-Arroyave. Towards parkinson’s dis- ease detection through analysis of everyday handwriting. Diagnostics, 15(3):381, 2025

2025
[240]

J. A. Logemann, H. B. Fisher, B. Boshes, and E. R. Blonsky. Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of parkinson patients. Journal of Speech and Hearing Disorders, 43:47–57, 1978

1978
[241]

A. K. Ho, R. Iansek, C. Marigliani, J. L. Bradshaw, and S. Gates. Speech impairment in a large sample of patients with parkinson’s disease. Behavioural Neurology, 11:327–643, 1999

1999
[242]

B. E. Sakar, M. E. Isenkul, C. O. Sakar, A. Sertbas, F. Gurgen, S. Delil, H. Apaydin, and O. Kursun. Collection and analysis of a parkinson speech dataset with multiple types of sound recordings. IEEE Journal of Biomedical and Health Informatics, 17:828–834, 2013

2013
[243]

J. R. Orozco-Arroyave, J. D. Arias-Londoño, J. F. Vargas-Bonilla, M. C. Gonzalez-Rátiva, and E. Nöth. New spanish speech corpus database for the analysis of people suffering from parkinson’s disease. In N. Cal- zolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mari...

2014
[244]

M. Little. Parkinsons [dataset]. UCI Machine Learning Repository, 2007

2007
[245]

New spanish speech corpus database for the analysis of people suffering from parkinson’s disease

Juan Rafael Orozco-Arroyave, Julián David Arias-Londoño, Jesús Fran- cisco Vargas-Bonilla, María Claudia Gonzalez-Rátiva, and Elmar Nöth. New spanish speech corpus database for the analysis of people suffering from parkinson’s disease. In Lrec, pages 342–347, 2014

2014
[246]

Fox and Lorraine O

Cynthia M. Fox and Lorraine O. Ramig. V ocal sound pressure level and self perception of speech and voice in men and women with idiopathic parkinson disease. American Journal of Speech-Language Pathology, 6:85–94, 1997

1997
[247]

F. J. Jiménez-Jiménez et al. Acoustic voice analysis in untreated patients with parkinson’s disease. Parkinsonism and Related Disorders, 3:111– 116, 1997

1997
[248]

R. J. Holmes, J. M. Oates, D. J. Phyland, and A. J. Hughes. V oice characteristics in the progression of parkinson’s disease. International Journal of Language and Communication Disorders, 35:407–418, 2000

2000
[249]

Skodda and U

S. Skodda and U. Schlegel. Speech rate and rhythm in parkinson’s disease. Movement Disorders, 23:985–992, 2008

2008
[250]

Rusz et al

J. Rusz et al. Imprecise vowel articulation as a potential early marker of parkinson’s disease: Effect of speaking task. The Journal of the Acoustical Society of America, 134:2171–2181, 2013. VOLUME 4, 2016 43

2013
[251]

Bandini et al

A. Bandini et al. Automatic identification of dysprosody in idiopathic parkinson’s disease. Biomedical Signal Processing and Control, 17:47– 54, 2015

2015
[252]

J. C. Vasquez-Correa, T. Arias-Vergara, J. R. Orozco-Arroyave, J. F. Vargas-Bonilla, J. D. Arias-Londoño, and E. Nöth. Automatic de- tection of parkinson’s disease from continuous speech recorded in non-controlled noise conditions. In Proceedings of the 16th Annual Conference ...

2015
[253]

X-vectors: New quantitative biomarkers for early parkinson’s disease detection from speech, 2020

Quentin Jeancolas et al. X-vectors: New quantitative biomarkers for early parkinson’s disease detection from speech, 2020. Retrieved from https: //arxiv.org/pdf/2007.03599

2020 arXiv
[254]

Suitability of dysphonia measurements for telemonitoring of parkinson’s disease

Max Little, Patrick McSharry, Emily Hunter, Jennifer Spielman, and Lor- raine Ramig. Suitability of dysphonia measurements for telemonitoring of parkinson’s disease. IEEE Transactions on Biomedical Engineering, 56:1015–1022, 2009

2009
[255]

Sakar, G

C.O. Sakar, G. Serbes, A. Gunduz, H.C. Tunc, H. Nizam, B.E. Sakar, M. Tutuncu, T. Aydin, M.E. Isenkul, and H. Apaydin. A comparative analysis of speech signal processing algorithms for parkinson’s disease classification and the use of the tunable-factor wavelet transform.Appli...

2019
[256]

Naranjo, C

L. Naranjo, C. J. Pérez, Y . Campos-Roca, and J. Martín. Addressing voice recording replications for parkinson’s disease detection. Expert Systems with Applications, 46:286–292, 2016

2016
[257]

Olcay, S

K. Olcay, S. Betul, I. M., S. C., S. Ahmet, and G. Fikret. Parkinson’s Speech with Multiple Types of Sound Recordings. https://doi.org/10. 24432/C5NC8M, 2014. UCI Machine Learning Repository

2014
[258]

Jaeger, D

H. Jaeger, D. Trivedi, and M. Stadtschnitzer. Mobile Device V oice Recordings at King’s College London (MDVR-KCL) from both early and advanced Parkinson’s disease patients and healthy controls, 2019. Dataset, May 2019

2019
[259]

Dimauro and F

G. Dimauro and F. Girardi. Italian Parkinson’s V oice and Speech, 2019. Dataset

2019
[260]

S. C., S. Gorkem, G. Aysegul, N. Hatice, and S. Betul. Parkinson’s Disease Classification. https://doi.org/10.24432/C5MS4X, 2018. UCI Machine Learning Repository

2018 doi
[261]

Little, P

M. Little, P. McSharry, S. Roberts, D. Costello, and I. Moroz. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Nature Precedings, 23:1–1, 2007

2007
[262]

Mathur, S

S. Mathur, S. DeWitte, I. Robledo, T. Isaacs, and J. Stamford. Rising to the challenges of clinical trial improvement in parkinson’s disease. Journal of Parkinson’s Disease, 5(2):263–268, 2015

2015
[263]

Benba, A

A. Benba, A. Jilbab, A. Hammouch, and S. Sandabad. V oiceprints analysis using mfcc and svm for detecting patients with parkinson’s disease. In Proceedings of the IEEE 2015 International Conference on Electrical and Information Technologies (ICEIT), pages 300–304, Marrakech, M...

2015
[264]

Bhattacharya and M.P.S

I. Bhattacharya and M.P.S. Bhatia. Svm classification to distin- guish parkinson disease patients. In Proceedings of the 1st Amrita ACM-WCelebration on Women in Computing in India, page 14, Tamil- nadu, India
[265]

Mathur, V

R. Mathur, V . Pathak, and D. Bandil. Parkinson disease prediction using machine learning algorithm. In Emerging Trends in Expert Applications and Security, pages 357–363. Springer, Singapore, 2019

2019
[266]

Almeida, P.P

J.S. Almeida, P.P. RebouçasFilho, T. Carneiro, W. Wei, R. Damaševiˇcius, R. Maskeli ¯unas, and V .H.C. de Albuquerque. Detecting parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognit. Lett., 125:55–62, 2019

2019
[267]

Alqahtani, F.H

E.J. Alqahtani, F.H. Alshamrani, H.F. Syed, and S.O. Olatunji. Clas- sification of parkinson’s disease using nnge classification algorithm. In Proceedings of the 2018 21st Saudi Computer Society National Computer Conference (NCC), pages 1–7, Riyadh, Saudi Arabia, April 2018

2018
[268]

Avuçlu and A

E. Avuçlu and A. Elen. Evaluation of train and test performance of machine learning algorithms and parkinson diagnosis with statistical measurements. Med. Biol. Eng. Comput., 58:2775–2788, 2020

2020
[269]

Z.K. Senturk. Early diagnosis of parkinson’s disease using machine learning algorithms. Medical Hypotheses, 138:109603, 2020

2020
[270]

S. Aich, H. Kim, K. Younga, K.L. Hui, A.A. Al-Absi, and M. Sain. A supervised machine learning approach using different feature selection techniques on voice datasets for prediction of parkinson’s disease. In Proceedings of the 2019 21st International Conference on Advanced Co...

2019
[271]

Haq, J.P

A.U. Haq, J.P. Li, M.H. Memon, A. Malik, T. Ahmad, A. Ali, S. Nazir, I. Ahad, and M. Shahid. Feature selection based on l1-norm support vector machine and effective recognition system for parkinson’s disease using voice recordings. IEEE Access, 7:37718–37734, 2019

2019
[272]

Y . Wu, P. Chen, Y . Yao, X. Ye, Y . Xiao, L. Liao, M. Wu, and J. Chen. Dysphonic voice pattern analysis of patients in parkinson’s disease us- ing minimum interclass probability risk feature selection and bagging ensemble learning methods. Computational and Mathematical Metho...

2017
[273]

Sheibani, E

R. Sheibani, E. Nikookar, and S.E. Alavi. An ensemble method for diagnosis of parkinson’s disease based on voice measurements. Journal of Medical Signals and Sensors, 24:221–226, 2019

2019
[274]

Montaña, Y

D. Montaña, Y . Campos-Roca, and J. Pérez Carlos. A diadochokinesis- based expert system considering articulatory features of plosive conso- nants for early detection of parkinson’s disease. Computer Methods and Programs in Biomedicine, 154:89–97, 2018

2018
[275]

Kuresan, D

H. Kuresan, D. Samiappan, and S. Masunda. Fusion of wpt and mfcc feature extraction in parkinson’s disease diagnosis. Technology and Health Care, 27:363–372, 2019

2019
[276]

Yaman, F

O. Yaman, F. Ertam, and T. Tuncer. Automated parkinson’s disease recognition based on statistical pooling method using acoustic features. Medical Hypotheses, 135:109483, 2020

2020
[277]

Marar, D

S. Marar, D. Swain, V . Hiwarkar, N. Motwani, and A. Awari. Predicting the occurrence of parkinson’s disease using various classification mod- els. In Proceedings of the 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pages 1–5, Bhopal, In...

2018
[278]

Moharkan, H

Z.A. Moharkan, H. Garg, T. Chodhury, and P. Kumar. A classi- fication based parkinson detection system. In Proceedings of the 2017 International Conference on Smart Technologies for Smart Nation (SmartTechCon), pages 1509–1513, Bengaluru, India, 2017

2017
[279]

Sztahó, I

D. Sztahó, I. Valálik, and K. Vicsi. Parkinson’s disease sever- ity estimation on hungarian speech using various speech tasks. In Proceedings of the 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), pages 1–6, Timisoara, Romania, 2019

2019
[280]

Tracy, Y

J.M. Tracy, Y . Özkanca, D.C. Atkins, and R.H. Ghomi. Investigating voice as a biomarker: Deep phenotyping methods for early detection of parkinson’s disease. Journal of Biomedical Informatics, 104:103362, 2020

2020
[281]

Are you tuned in to others’ mind? a cross-modal evaluation of affective theory of mind in people with parkinson’s disease

Elisa Menozzi, Daniela Ballotta, Francesco Cavallieri, Stefania Tocchini, Sara Contardi, Valentina Fioravanti, Franco Valzania, Paolo F Nichelli, and Francesca Benuzzi. Are you tuned in to others’ mind? a cross-modal evaluation of affective theory of mind in people with parkin...

2025
[282]

Pd-net: Parkinson’s disease detection through fusion of two spectral features using attention-based hybrid deep neural network

Munira Islam, Khadija Akter, Md Azad Hossain, and M Ali Akber De- wan. Pd-net: Parkinson’s disease detection through fusion of two spectral features using attention-based hybrid deep neural network. Information, 16(2):135, 2025

2025
[283]

Sheikh, Yacouba Kaloga, Md

Shakeel A. Sheikh, Yacouba Kaloga, Md. Sahidullah, and Ina Kodrasi. Graph neural networks for parkinson’s disease detection. In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5, 2025

2025
[284]

J. C. Vásquez-Correa, N. Garcia-Ospina, J. R. Orozco-Arroyave, M. Cer- nak, and E. Nöth. Phonological posteriors and gru recurrent units to assess speech impairments of patients with parkinson’s disease. In P. Sojka, A. Horák, I. Kope ˇcek, and K. Pala, editors, Text, Speech, ...

2018
[285]

Hernandez, P

A. Hernandez, P. A. Pérez-Toro, E. Nöth, J. R. Orozco-Arroyave, A. K. Maier, and S. H. Yang. Cross-lingual self-supervised speech representations for improved dysarthric speech recognition. ArXiv, abs/2204.01670, 2022

2022 arXiv
[286]

Bhati, L

S. Bhati, L. M. Velazquez, J. Villalba, and N. Dehak. Lstm siamese network for parkinson’s disease detection from speech. In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pages 1–5. IEEE, 2019

2019
[287]

J. C. Vásquez-Correa, J. R. Orozco-Arroyave, and E. Nöth. Convolu- tional neural network to model articulation impairments in patients with parkinson’s disease. In Interspeech, pages 314–318, Stockholm, 2017

2017
[288]

Zhang, A

H. Zhang, A. Wang, D. Li, and W. Xu. Deepvoice: A voiceprint- based mobile health framework for parkinson’s disease identification. In 2018 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pages 214–217. IEEE, 2018. 44 VOLUME 4, 2016 This paper is...

2018
[289]

A. Zhao, N. Wang, X. Niu, M. Chen, and H. Wu. A triplet multimodel transfer learning network for speech disorder screening of parkinson’s disease. International Journal of Intelligent Systems, 2024(1):8890592, 2024

2024
[290]

Hemmerling, M

D. Hemmerling, M. Wodzinski, and J. R. Orozco-Arroyave et al. Vision transformer for parkinson’s disease classification using multilingual sus- tained vowel recordings. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),...

2023
[291]

Nijhawan, M

R. Nijhawan, M. Kumar, S. Arya, N. Mendirtta, S. Kumar, S. Towfek, D. S. Khafaga, H. K. Alkahtani, and A. A. Abdelhamid. A novel artificial-intelligence-based approach for classification of parkinson’s disease using complex and large vocal features. Biomimetics, 8(4):351, 2023

2023
[292]

H. Fang, C. Gong, C. Zhang, Y . Sui, and L. Li. Parkinsonian chinese speech analysis towards automatic classification of parkinson’s disease. In Machine Learning for Health, pages 114–125. PMLR, 2020

2020
[293]

Mehra, V

S. Mehra, V . Ranga, and R. Agarwal. A deep learning approach to dysarthric utterance classification with bilstm-gru, speech cue filtering, and log mel spectrograms. The Journal of Supercomputing, pages 1–28, 2024

2024
[294]

Jeong, S

S.-M. Jeong, S. Kim, E. C. Lee, and H. J. Kim. Exploring spectrogram- based audio classification for parkinson’s disease: A study on speech clas- sification and qualitative reliability verification. Sensors, 24(14):4625, 2024

2024
[295]

Klempí ˇr, D

O. Klempí ˇr, D. P ˇríhoda, and R. Krupi ˇcka. Evaluating the performance of wav2vec embedding for parkinson’s disease detection. Measurement Science Review, 23(6):260–267, 2023

2023
[296]

Explainable artificial intelligence to diagnose early parkinson’s disease via voice analysis

Matthew Shen, Pouria Mortezaagha, and Arya Rahgozar. Explainable artificial intelligence to diagnose early parkinson’s disease via voice analysis. Scientific Reports, 15(1):11687, 2025

2025
[297]

A lightweight cnn-based ensemble approach for early detecting parkinson’s disease with enhanced features

Dip Kumar Saha and Tushar Deb Nath. A lightweight cnn-based ensemble approach for early detecting parkinson’s disease with enhanced features. International Journal of Speech Technology, pages 1–15, 2025

2025
[298]

A. H. Maitín, J. L. Ooijevaar, W. Desmet, and B. Weyn. A systematic review of parkinson’s disease classification methods based on electroen- cephalogram signals. Sensors, 20(23):6791, 2020

2020
[299]

Eeg classification for mi-bci using csp with averaging covariance matrices: An experimental study

Abu Saleh Musa Miah, Md Rabiul Islam, and Md Khademul Islam Molla. Eeg classification for mi-bci using csp with averaging covariance matrices: An experimental study. In 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC...

2019
[300]

Eeg-based hand gesture classification using machine learning approach

Abu Saleh Musa Miah, Md Hadiuzzaman, Mohammed Sowket Ali, and Taher Muhammad Mahdee. Eeg-based hand gesture classification using machine learning approach. BAUST JOURNAL, page 19, 2022

2022

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [88]

Pereira, S.A

C.R. Pereira, S.A. Weber, C. Hook, G.H. Rosa, and J.P. Papa. Deep learning-aided parkinson’s disease diagnosis from handwritten dynamics. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 340–346. IEEE, 2016

2016

[2] [1]

Parkinson’s disease from an ayurveda per- spective: Opportunities and challenges for further research

Satyajit Pandurang Kulkarni. Parkinson’s disease from an ayurveda per- spective: Opportunities and challenges for further research. Translational Models of Parkinson’s Disease and Related Movement Disorders, pages 345–360, 2025

2025

[3] [2]

Parkinson’s disease: mechanisms and models

William Dauer and Serge Przedborski. Parkinson’s disease: mechanisms and models. Neuron, 39(6):889–909, 2003

2003

[4] [3]

Assessment of parkinson disease manifestations

Joel S Perlmutter. Assessment of parkinson disease manifestations. Current protocols in neuroscience, 49(1):10–1, 2009

2009

[5] [4]

Accuracy of clinical di- agnosis of parkinson disease: a systematic review and meta-analysis

Giovanni Rizzo, Massimiliano Copetti, Simona Arcuti, Davide Martino, Andrea Fontana, and Giancarlo Logroscino. Accuracy of clinical di- agnosis of parkinson disease: a systematic review and meta-analysis. Neurology, 86(6):566–576, 2016

2016

[6] [5]

A hybrid system for parkinson’s disease diagnosis using machine learning techniques

Rohit Lamba, Tarun Gulati, Hadeel Fahad Alharbi, and Anurag Jain. A hybrid system for parkinson’s disease diagnosis using machine learning techniques. International Journal of Speech Technology, pages 1–11, 2022

2022

[7] [6]

Patient-centered brain transcriptomic and multimodal imaging determinants of clinical progression, physical activity, and treatment needs in parkinson’s disease

Quadri Adewale, Ahmed Faraz Khan, Sue-Jin Lin, Tobias R Baumeister, Yashar Zeighami, Felix Carbonell, Daniel Ferreira, and Yasser Iturria- Medina. Patient-centered brain transcriptomic and multimodal imaging determinants of clinical progression, physical activity, and treatment needs in parkinson’s disease. npj Parkinson’s Disease, 11(1):29, 2025

2025

[8] [7]

S. Fahn, R. Elton, and Members of the UPDRS Development Commit- tee. The unified parkinson’s disease rating scale. In S. Fahn, C.D. Marsden, D.B. Calne, and M. Goldstein, editors,Recent Developments in Parkinson’s Disease, volume 2, pages 153–163. McMellam Health Care Information, Florham Park, 1987

1987