A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods

Stefanos Gkikas

REVIEW 4 major objections 6 minor 3 cited by

A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The thesis claims that automatic pain assessment can reach clinically useful accuracy by fusing facial video and biosignals with pretrained transformer models, reporting 82.74% binary and 39.77% five-level accuracy on BioVid.

desk verdict Solid transformer-based pain-assessment work, but Chapter 6's synthetic-thermal fusion gain is an artifact risk: the generated 'thermal' frames can't add information beyond RGB. read the letter →

arxiv 2505.05396 v2 pith:SUJA6MWA submitted 2025-05-08 cs.AI cs.HCcs.LG

Stefanos Gkikas This is my paper · ORCID

classification cs.AIcs.HCcs.LG

keywords automaticpainassessmentdeeplearningtransformermultimodalfusionfoundationmodelsyntheticthermalimagingdemographicfactorsBioViddataset

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 thesis argues that automatic pain assessment can be made accurate enough for clinical use by building deep-learning pipelines that combine behavioral and physiological modalities. It develops and tests several such pipelines, including transformer-based video analysis and a multimodal video-plus-heart-rate system that reaches 82.74% accuracy on binary pain classification and 39.77% on five-level classification on the BioVid benchmark. The thesis also examines how age and gender affect pain signals, generates synthetic thermal videos to supplement RGB data, and proposes a foundation model, PainFormer, pretrained across 14 tasks, as a reusable embedding extractor. A sympathetic reader would take the central claim to be that general-purpose, pretrained architectures plus multimodal fusion are the right path toward objective, continuous pain monitoring.

What carries the argument

The load-bearing mechanism is the pretrained transformer and vision-MLP encoder used as a universal embedding extractor. PainFormer, the foundation model, is built on multi-task learning over 14 tasks and datasets totaling roughly 10.9 million samples; its learned embeddings are fed into an Embedding-Mixer, a cross- and self-attention module that performs final pain classification. In the earlier multimodal pipeline, a Spatial Module pretrained first on face recognition then emotion recognition, a Heart Rate Encoder, an augmentation network, and a Temporal Module with self- and cross-attention combine video and heart-rate embeddings. The repeated pattern is to pretrain broadly, extract embeddings, and fuse them through attention, so the same machinery can switch between RGB, synthetic thermal, depth, ECG, EMG, GSR, and fNIRS inputs.

What would settle it

Take a held-out set of subjects from BioVid, train the fusion pipeline once with synthetic thermal generated from RGB, and once with a second RGB stream processed by the same architecture; if the synthetic-thermal version does not beat the RGB-RGB control at comparable parameter counts, the claimed multimodal gain is not due to thermal-specific pain information. Also, compare synthetic thermal embeddings against real thermal recordings from a dataset such as MIntPAIN; agreement would support the complementarity story, while disagreement would weaken it.

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Extended reading notes

Core claim

The central discovery claimed is that a single family of transformer and vision-MLP architectures, pretrained on large general facial and biosignal datasets and fine-tuned for pain, can reach leading performance across modalities. In the multimodal setting, fusing facial video and heart-rate embeddings in a transformer framework achieved 82.74% accuracy for binary no-pain versus severe-pain classification and 39.77% for the five-level task on BioVid, with only 9.62 million parameters. The thesis further claims that age and gender are computationally usable factors: ECG-based models trained separately on demographic subgroups, or with multi-task auxiliary heads, outperform models that ignore them. And it claims that synthetic thermal video generated from RGB frames can improve fusion results, and that a foundation model trained on 10.9 million samples across 14 tasks yields high-quality embeddings for video, ECG, EMG, GSR, and fNIRS alike.

Load-bearing premise

The claim that fusing RGB with synthetic thermal video helps assumes the generated thermal images carry pain-relevant information not already present in the RGB frames; if the GAN only re-encodes RGB appearance, the accuracy gain is an artifact of the generative model rather than evidence of complementary sensors.

Editorial extensions

If this is right

If these results hold, video-only and video-plus-physiology systems could provide continuous, objective pain monitoring for patients who cannot self-report.
Demographic conditioning becomes a practical ingredient rather than a confound: subgroup-specific or multi-task models improve ECG-based pain estimation.
Synthetic thermal generation could sidestep the scarcity of real thermal pain data and offers a privacy angle, since thermal-style images obscure identity.
A single foundation model pretrained across many tasks may reduce the need for task-specific architectures in pain assessment.
Transformer-based fusion at roughly 9.6 million parameters suggests real-time inference on modest hardware is plausible.

Reading between the lines

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

Editorial inference: the claimed fusion gain from synthetic thermal should be tested against a control where the thermal channel is replaced by a second RGB stream processed identically, to rule out that the gain comes from extra parameters rather than modality complementarity.
Editorial inference: the large demographic differences in sensitivity imply pain-assessment models should be audited for fairness across age and sex groups before deployment.
Editorial inference: if synthetic thermal truly adds pain-relevant information beyond RGB, then thermal cameras may not be necessary in clinical settings; a generator could supply the thermal channel from ordinary video.
Editorial inference: PainFormer's multi-task pretraining recipe is a template that could transfer to other clinical sensing tasks with scarce labeled data.

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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

The short version: this is a substantial PhD thesis with real strengths - the transformer video models, the video+HR fusion, and the PainFormer foundation model are all serious contributions - but its headline 'multimodal' claim from Chapter 6 is built on a circularity that the thesis never addresses, and the reported accuracy numbers mostly lack error bars. On the positive side, the author has done a lot of careful engineering: a full transformer pipeline for video pain estimation, a two-stage pretrained spatial-temporal model that fuses video and heart rate, and PainFormer, a vision foundation model pretrained on 14 tasks/10.9M samples and then evaluated across RGB, thermal, depth, ECG, EMG, GSR, and fNIRS. The comparisons on BioVid with LOSO validation are extensive, and the runtime/FLOPs reporting is better than most papers in this area. The systematic review (Ch.3) is genuinely useful. The demographic analysis (Ch.4) is a rare attempt to bring age and gender into computational pain assessment, even if the conclusions are tentative. The soft spot is real and load-bearing for one chapter. In Ch.6, synthetic thermal videos are generated by a GAN from the same RGB frames, then fused with RGB. Since the thermal frame is a deterministic (learned) function of the RGB frame, it cannot contribute pain-relevant information not already present in RGB - data-processing inequality. The accuracy gain from fusion is therefore most plausibly an artifact of the generator acting as an inductive bias or an implicit augmentation, not evidence of a complementary modality. The blur analysis shows only that the synthetic thermal branch uses low-frequency structure, which is exactly what a smooth re-encoding would do. The thesis never validates against real thermal on the same subjects, and the MIntPAIN comparison is cross-dataset, which cannot resolve the question. This weakens Sec. 8.1.4's claim that synthetic thermal offers a privacy-preserving contribution: if the generator is trained on RGB, you still need the RGB to generate it. Minor but worth noting: most accuracy numbers come without confidence intervals or significance tests, and the demographic subgroup claims in Ch.4 are drawn from groups of very different sizes and a few percentage point differences. I don't suspect foul play; the core BioVid results are plausible, but they're under-stated in robustness. Who should read it: anyone in affective computing or clinical pain monitoring who wants a benchmark map and a collection of transformer architectures. It deserves a serious referee if the author turns selected chapters into journal papers - but Ch.6 should be revised with a control transform (e.g., edge maps or other deterministic RGB transforms) and real-thermal validation before the multimodal claim is made. Cite it if you need the PainFormer baseline; skip the synthetic-thermal narrative.

Referee Report

4 major / 6 minor

Summary. This PhD thesis develops deep-learning pipelines for automatic pain assessment from multimodal data, including facial video, ECG, EMG, GSR, fNIRS, and synthetic thermal video. The work is organized into four technical threads: (i) systematic review of deep learning for pain assessment (Ch. 3); (ii) demographic subgroup analysis of ECG-based pain estimation (Ch. 4); (iii) efficient transformer-based unimodal and multimodal video/HR architectures evaluated on BioVid (Ch. 5); (iv) synthetic thermal imagery via GANs as an additional modality (Ch. 6); and (v) general-purpose models, including the PainFormer foundation model evaluated on BioVid and AI4Pain (Ch. 7). The central claims are state-of-the-art accuracy on BioVid (82.74% binary, 39.77% multi-level for the multimodal transformer in Ch. 5) and leading-edge results across modalities and data representations (Sec. 7.3, Fig. 7.7). The thesis also emphasizes demographic sensitivity, interpretability via attention maps, and the practicality of compact models with reported parameter and FLOPS counts.

Significance. If the reported results are correct, the thesis would make a strong empirical contribution to automatic pain assessment: the Ch. 5 transformer pipeline outperforms or matches prior BioVid results while being parameter-efficient, and PainFormer is one of the first attempts at a foundation model for pain. The systematic review in Ch. 3 is thorough and well structured, and the thesis is commendably transparent in reporting LOSO protocols, per-module parameters, FLOPS, and many comparison tables against prior work. However, the significance is substantially qualified by the synthetic-thermal fusion in Ch. 6: because the synthetic frames are deterministic transforms of the same RGB frames, the claimed multimodal gain cannot be interpreted as complementary sensor information. In addition, the demographic subgroup claims in Ch. 4 rely on small, unbalanced groups without statistical testing, and the headline SOTA claims in Ch. 5 are reported without confidence intervals or significance tests. These are fixable issues, but they affect the interpretation of the thesis's strongest claims.

major comments (4)

[§6.3, Fig. 6.1, Tables 6.3–6.6] The central multimodal claim in Chapter 6 is not supported. Fig. 6.1 shows that synthetic thermal frames are generated per-frame from the RGB videos by a GAN; by the data-processing inequality, any pain-relevant information in these synthetic frames is bounded by the information already present in the RGB frames. The accuracy gains in Tables 6.3–6.6 therefore cannot be attributed to complementary thermal physiology; they could equally arise from the generator acting as an implicit augmentation or inductive bias. The thesis does not validate the synthetic thermal stream against real thermal imaging on the same subjects (Table 6.8 compares BioVid synthetic-thermal results with MIntPAIN real-thermal results on a different dataset and different subjects, which cannot settle this). A control-transform experiment, e.g., replacing the GAN output with a fixed smooth per-frame transform of the RGB input while keeping the same fusion architecture, would establish whether the gain is specific to thermal-like content. Without such a control, the claim that fusing RGB and synthetic thermal provides a genuine multimodal benefit should be withdrawn or substantially rephrased.
[§4.2.3 and §4.3.3, Tables 4.2–4.5 and 4.8–4.11] The demographic conclusions are drawn from accuracy differences of a few percentage points between groups of very different sizes, with no confidence intervals or significance tests. For example, the Gender-Age scheme splits the 87 BioVid subjects into six groups, so groups are small (roughly 12–16 subjects each), yet Table 4.11 reports differences such as 71.67% vs. 60.67% as evidence that 'Females 20-35' are most pain-sensitive and 'Males 51-65' least. The variance of LOSO accuracy with such small groups is large, and statements like 'notable differences... emerged' are not statistically grounded. Please report per-fold variability, confidence intervals, and appropriate tests (e.g., McNemar or permutation tests) before claiming that pain perception differs by age and gender in these data.
[§5.3.3, Tables 5.9 and 5.10] The headline state-of-the-art claims in Chapter 5 are based on accuracy gaps of roughly 1–3 percentage points over prior methods (e.g., 82.74% vs. prior results in Table 5.10), but no confidence intervals or significance tests are provided. On a dataset of 87 subjects with 100 samples each and LOSO evaluation, such differences may be within natural variation. Please report the distribution of per-subject accuracies, confidence intervals, or a statistical comparison to the closest competing methods to support the 'state-of-the-art' claim. This does not necessarily change the results, but it is needed for the claim to be load-bearing.
[§6.3.3, Table 6.8] Table 6.8 compares BioVid synthetic-thermal results with MIntPAIN real-thermal results, but the two datasets differ in subjects, pain induction, and recording hardware. This comparison is used implicitly to suggest that synthetic thermal approximates real thermal, yet the text does not explain why such a cross-dataset comparison is valid. At minimum, the comparison should be explicitly framed as indirect and not as validation of synthetic thermal as a proxy. Without a same-subject comparison or a demonstrated physiological correspondence, the claim that synthetic thermal is 'effective' as a thermal modality is unsupported.

minor comments (6)

[§1.3, contribution 6] The text reads 'leveraging RBG and synthetic thermal videos'; the 'RBG' should be 'RGB'.
[§2.3] There is a typographical error: 'dysfunction l pain' should be 'dysfunctional pain'.
[§4.2.3] The sentence 'Additionally, Table 7 compares our results' refers to a table that is numbered 4.6 in the actual text; the in-text citation numbering is inconsistent.
[§4.2.1] The text says 'refer to Figure 1' but the actual figure is numbered 4.1; please update the cross-reference.
[Fig. 7.7] The caption of Fig. 7.7 contains a substantive claim ('achieving leading-edge results across various modalities and data representations'); this claim should appear in the main text with statistical support, not only in a figure caption.
[General] There are several spacing inconsistencies for the Pan-Tompkins algorithm (e.g., 'Pan-Tompkinsalgorithm' and 'Pan-Tompkinsalgorithm'); please unify the formatting.

Circularity Check

1 steps flagged · score 6.0 of 10

Partial circularity: the synthetic-thermal 'modality' in Chapter 6 is a GAN transform of the same RGB frames, so the reported fusion gain does not demonstrate multimodal sensor fusion; the rest of the thesis is largely independent empirical benchmarking.

renaming known result [Section 1.3 (contribution 6) and Section 6.3, Fig. 6.1, Tables 6.5-6.6]
"This paper introduced synthetic thermal videos generated by Generative Adversarial Networks, which are integrated into the pain recognition process to assess their effectiveness. The framework employs a Vision-MLP and Transformer-based module, leveraging RBG and synthetic thermal videos in unimodal and multimodal settings."

Figure 6.1 shows the thermal frames are produced by a Generator G from the RGB frames, so the synthetic-thermal stream is a deterministic learned transform of the same sensor input. By the data-processing inequality, any pain-relevant information in the synthetic thermal frames is bounded by the information in the RGB frames; it cannot be an independent modality.

full rationale

The only step that reduces by construction is the synthetic-thermal fusion claim in Chapter 6: the 'thermal' input is generated from the RGB input, so the multimodal framing is not supported by an independent sensor channel. The rest of the thesis is a compendium of externally benchmarked empirical results: BioVid video and heart-rate experiments (Section 5.3) compare against 29 prior studies, the AI4Pain video+fNIRS pipeline (Section 7.2) is evaluated against the challenge baseline, and PainFormer (Section 7.3) is benchmarked against more than 60 methods on BioVid and AI4Pain. Those comparisons use standard LOSO or challenge splits and external pretraining datasets, so the central 'state-of-the-art' claims for RGB/biosignal pipelines are self-contained empirical claims rather than derivations from the thesis's own assumptions. The self-citations in the thesis are the normal reuse of the author's own published papers as chapters and do not carry an unverified theorem or forbidden uniqueness argument. The synthetic-thermal overclaim is partial: it affects the interpretation of one 'modality' in the claimed multimodal generality, but the thesis also contains genuine multimodal combinations (video+HR, video+fNIRS) whose gains are not forced by construction. Score 6 reflects this partial, Chapter-6-specific circularity rather than a thesis-wide collapse.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claims rest on a small set of domain assumptions about the BioVid and AI4Pain benchmarks, plus the ad hoc assumption that synthetic thermal images are a meaningful new modality. The free parameters are mostly standard hyperparameters, but the age bin boundaries and fusion choices are fitted to the evaluation data and could inflate reported performance.

free parameters (3)

Number of input frames for video transformer = Varies (e.g., 8, 16, 32; optimal chosen by accuracy in Table 5.3)
The number of frames is a hyperparameter tuned on the validation set; the choice affects performance and runtime.
Age group boundaries = 20-35, 36-50, 51-65
The division of subjects into three age bins is an arbitrary modeling choice defined in Section 4.2.2. Different boundaries could change the demographic conclusions.
Fusion method (addition vs concatenation) = Chosen by achieved accuracy on validation (Section 7.2.2)
The thesis tests both fusion strategies and reports the better one, which is a model selection based on the same data used for evaluation.

assumptions (3)

domain assumption BioVid stimulus levels (NP, P1-P4) are a valid ground truth for pain intensity.
The entire thesis relies on the BioVid dataset's calibrated heat stimuli as a proxy for pain. This is a standard assumption in the field, but it limits generalization to real clinical pain.
ad hoc to paper Synthetic thermal images generated from RGB are a valid proxy for real thermal imaging.
Chapter 6 introduces synthetic thermal images without validating them against real thermal data. The assumption that GAN-generated thermal carries independent pain information is load-bearing for the multimodal fusion claims.
domain assumption Labeled pain expressions in experimentally induced pain generalize to clinical pain.
The thesis acknowledges in Section 3.7 that existing pain datasets lack real-world clinical contexts, yet the conclusion that the methods are 'applicable in real clinical settings' assumes this generalization.

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Cite this review

Pith. "Pith review of A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods." pith.science (2026). https://pith.science/paper/SUJA6MWA

@misc{pith2026250505396,
  author       = {Pith},
  title        = {Pith review of: A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SUJA6MWA}},
  note         = {Machine review of arXiv:2505.05396}
}

read the original abstract

From the original abstract: This thesis initially aims to study the pain assessment process from a clinical-theoretical perspective while exploring and examining existing automatic approaches. Building on this foundation, the primary objective of this Ph.D. project is to develop innovative computational methods for automatic pain assessment that achieve high performance and are applicable in real clinical settings. A primary goal is to thoroughly investigate and assess significant factors, including demographic elements that impact pain perception, as recognized in pain research, through a computational standpoint. Within the limits of the available data in this research area, our goal was to design, develop, propose, and offer automatic pain assessment pipelines for unimodal and multimodal configurations that are applicable to the specific requirements of different scenarios. The studies published in this Ph.D. thesis showcased the effectiveness of the proposed methods, achieving state-of-the-art results. Additionally, they paved the way for exploring new approaches in artificial intelligence, foundation models, and generative artificial intelligence.

Figures

Figures reproduced from arXiv: 2505.05396 by the authors.

**Figure 2.2.** Pain classification [48]: (A) Nociceptive pain, which results from detecting potentially harmful stimuli and serves a protective function. (B) Inflammatory pain is linked to tissue damage and immune cell infiltration, increasing pain sensitivity during healing. (C) Pathological pain is a disease state caused by either nervous system damage (neuropathic) or abnormal nervous system function (dysfunctional). “that i… view at source ↗

**Figure 3.1.** The number of studies utilizing these specific datasets. Note that various studies [PITH_FULL_IMAGE:figures/full_fig_p051_3_1.png] view at source ↗

Figures from the paper (31 more)

**Figure 4.1.** Figure 4.1: The PQRST waveform. ECG Low Pass Filter High Pass Filter Differentiation Adaptive Thresholds Moving Window Integration Squaring QRS Complex Band-Pass Filter ECG Low Pass Filter High Pass Filter Differentiation Adaptive Thresholds Moving Window Integration Squaring QR…

**Figure 4.2.** Figure 4.2: The flowchart of the Pan-Tompkins algorithm’s pre-processing procedure. [PITH_FULL_IMAGE:figures/full_fig_p081_4_2.png]

**Figure 4.3.** Figure 4.3: The signal preprocessing using the Pan-Tompkins algorithm. Classification Methods For the classification phase, three widely recognized classifiers were utilized: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with a linear kernel, and SVM with a R…

**Figure 4.6.** Figure 4.6: Results for the Gender-Age Scheme. 4.3 ECG Analysis with Multitask Neural Networks In this section, we build on previous analysis 4.2 that explored variations in pain manifestation across different demographic groups using ECG signals. It expands this investigation …

**Figure 4.7.** Figure 4.7: The proposed MTL network: The sizes of the extracted vectors for the network are [PITH_FULL_IMAGE:figures/full_fig_p092_4_7.png]

**Figure 4.8.** Figure 4.8: Results for the proposed Schemes. vectors enhanced with demographic attributes. Initially, the feature vectors, which originally consisted of six features (see 4.3.1), were augmented by adding either one additional feature (i.e., the subject’s gender or age) or two …

**Figure 4.9.** Figure 4.9: Comparison of performances utilizing various neural networks approaches. [PITH_FULL_IMAGE:figures/full_fig_p098_4_9.png]

**Figure 5.1.** Figure 5.1: The application of face alignment illustrates landmarks in 2D (left) and 3D (right) space. [PITH_FULL_IMAGE:figures/full_fig_p103_5_1.png]

**Figure 5.2.** Figure 5.2: An overview of our proposed transformer-based framework for automatic pain as [PITH_FULL_IMAGE:figures/full_fig_p106_5_2.png]

**Figure 5.3.** Figure 5.3: The impact of the number of input frames on accuracy (left) and on runtime in [PITH_FULL_IMAGE:figures/full_fig_p108_5_3.png]

**Figure 5.4.** Figure 5.4: Relevance Maps. maps that no universal facial expressions are unique to pain. However, there is a noticeable concentration on areas like the mouth and eyes. Comparison with existing methods In this section, we present a comparison of our results achieved using a tran…

**Figure 5.6.** Figure 5.6: Comparison of mean accuracy and inference period for unimodal and multimodal [PITH_FULL_IMAGE:figures/full_fig_p124_5_6.png]

**Figure 5.7.** Figure 5.7: Regions highlighted in yellow and red denote areas of significant attention. (a) (1st row) Sequence of original frames. (2nd row) Derived from the Spatial-Module after initial stage pretraining. (3rd row) Derived from the Spatial-Module post second stage pretraining…

**Figure 6.1.** Figure 6.1: Illustration of the procedure for creating thermal images, featuring the architecture [PITH_FULL_IMAGE:figures/full_fig_p132_6_1.png]

**Figure 6.2.** Figure 6.2: Representation of the proposed framework, illustrating its components and their [PITH_FULL_IMAGE:figures/full_fig_p133_6_2.png]

**Figure 6.3.** Figure 6.3: Gradual blurring of RGB and synthetic thermal facial images: a series displaying [PITH_FULL_IMAGE:figures/full_fig_p139_6_3.png]

**Figure 6.4.** Figure 6.4: Distributions of 3D embeddings for NP (no pain) and P4 (very severe pain) classes [PITH_FULL_IMAGE:figures/full_fig_p140_6_4.png]

**Figure 7.** Figure 7: (a) [PITH_FULL_IMAGE:figures/full_fig_p171_7.png]

**Figure 7.** Figure 7: (b) [PITH_FULL_IMAGE:figures/full_fig_p172_7.png]

**Figure 7.** Figure 7: (b) [PITH_FULL_IMAGE:figures/full_fig_p173_7.png]

**Figure 7.1.** Figure 7.1: PainViT: (a) Hierarchical arrangement of the PainViT blocks, each layer having varying depths, showcasing how token resolution decreases at each stage; (b) Composition of the Token-Mixer module, featuring elements like depthwise convolution (DWConv) and batch norma…

**Figure 7.2.** Figure 7.2: Waveform illustrations for various data types: [PITH_FULL_IMAGE:figures/full_fig_p178_7_2.png]

**Figure 7.3.** Figure 7.3: Attention maps from the PainViT–2 [PITH_FULL_IMAGE:figures/full_fig_p178_7_3.png]

**Figure 7.4.** Figure 7.4: Overview of primary models and their components outlined in this research: [PITH_FULL_IMAGE:figures/full_fig_p179_7_4.png]

**Figure 7.5.** Figure 7.5: Examples of different vision modalities in frame samples: [PITH_FULL_IMAGE:figures/full_fig_p179_7_5.png]

**Figure 7.6.** Figure 7.6: Examples of different visual representations for biosignals: concat GSR [PITH_FULL_IMAGE:figures/full_fig_p180_7_6.png]

**Figure 7.7.** Figure 7.7: An overview of the presented framework. PainFormer, the foundational model, excels in deriving high-quality embeddings from a diverse array of behavioral and physiological modalities. The evaluation of RGB, thermal, and depth videos, alongside various representation…

**Figure 7.8.** Figure 7.8: Attention maps from the PainFormer: (a)(1st row) frames from RGB, thermal, and depth video modalities; (a)(2nd row) corresponding attention maps; (b)(1st row) attention maps for ECG and EMG; (b)(2nd row) attention maps for EDA and fNIRS modalities [PITH_FULL_IMAGE:f…

**Figure 1.** Figure 1: Attention maps generated by the Spatial-Module. Yellow and red colors signify intense focus on specific areas. (1st row) Sequence of original frames. (2nd row) Derived from the Spatial-Module after initial stage pretraining. (3rd row) Derived from the Spatial-Module po…

**Figure 2.** Figure 2: Attention maps generated by the Spatial-Module. Yellow and red colors signify intense focus on specific areas. (1st row) Sequence of original frames. (2nd row) Derived from the Spatial-Module after initial stage pretraining. (3rd row) Derived from the Spatial-Module po…

**Figure 3.** Figure 3: Additional attention maps from the PainViT–2 (refer to Section 7.2) [PITH_FULL_IMAGE:figures/full_fig_p233_3.png]

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

An Exploratory Analysis of Pain Localization via Explainable Computational Modeling
cs.CV 2026-07 conditional novelty 5.0 of 10

On the AI4Pain 2026 dataset, Extra Trees with 115 hand-crafted features (macro-F1 0.539) beats deep sequence models (0.465), and pain localization (0.552) is far harder than pain detection (0.815).
ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment
cs.CV 2026-07 conditional novelty 5.0 of 10

Reorganizing facial video into four channel-concatenated quadrants before tokenization yields 56.00% test accuracy on AI4Pain video-only pain classification, the highest reported under that benchmark protocol.
A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities
cs.CV 2026-07 conditional novelty 4.0 of 10

A unified tokenizer maps facial video and fNIRS into one token space; the segment-latent transformer hits 57.33% test accuracy on AI4Pain pain recognition.

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