Pith. sign in

REVIEW 2 major objections 1 minor 42 references

Short-time, Wavelet-inspired Mouse Submovement Detection

T0 review · 2 major / 1 minor · reviewed 2026-05-09 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A wavelet-inspired technique with self-weighted loss refinement accurately locates and parameterizes overlapping submovements in one-dimensional speed time series.

desk verdict Wavelet-inspired submovement detector with self-weighted refinement is a reasonable engineering step but rests on unverified synthetic data fidelity. read the letter →

arxiv 2604.20673 v1 submitted 2026-04-22 cs.HC

Auejin Ham , Ben Boudaoud This is my paper · ORCID
classification cs.HC
keywords submovementdetectionwavelet-inspiredmethodspeedtimeseriesmousemovementhuman-computerinteractionmotorcontrolparameterizationsyntheticdata
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

Submovements are the short ballistic bursts that make up most of human pointing and aiming motions, yet they overlap in time and arise from intertwined perception, planning, and execution cycles. The paper introduces a wavelet-inspired method that works on speed time series to find these components and fit their parameters. It adds a self-weighted loss step that spots and fixes regions where a simple wavelet fit is weak. The authors test the approach on about 6,400 synthetic 1-2 second trials of first-person shooter aim data that carry known ground truth, and they compare results against dual-threshold and persistence-based segmentation. If the claim holds, researchers gain a practical way to decompose real human motor signals for better models of interaction.

What carries the argument

Wavelet-inspired detection applied to speed time series, augmented by a self-weighted loss refinement step that identifies and corrects poor-quality fit regions.

What would settle it

Apply the same detection pipeline to real (non-synthetic) mouse or aim recordings and compare the extracted submovements against independent human annotations or concurrent physiological signals of motor bursts.

Watch

Extended reading notes

Core claim

We propose and evaluate a wavelet-inspired technique to accurately locate and parameterize submovements from one-dimensional speed time series; the method adds a self-weighted loss refinement step to improve regions of poor fit that challenge simpler wavelet transforms, and we demonstrate its accuracy on roughly 6,400 synthetic egocentric camera aim trials with known ground truth modeled from real data of 13 users.

Load-bearing premise

The synthetic data, modeled from real user recordings, faithfully reproduces the overlapping and cyclical character of actual human submovements so that the ground-truth labels remain valid for measuring detection accuracy.

Editorial extensions

If this is right

  • The method separates overlapping submovements more reliably than dual-threshold or persistence 1D segmentation on short aim trials.
  • It supplies parameterized descriptions of each submovement directly from speed profiles.
  • The approach supports quantitative study of the cyclical perception-motor loops that drive pointing behavior.
  • Results highlight specific challenges in fit quality that future refinements can target.

Reading between the lines

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

  • The same pipeline could be tested on real recorded movements to check whether accuracy transfers beyond the synthetic model.
  • Real-time versions might allow interfaces to adapt to a user's current submovement state during ongoing motion.
  • The technique may extend to two-dimensional trajectories or other input modalities such as touch or gesture.
  • Integration with existing motor-control models could yield better predictions of task completion times in HCI.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a wavelet-inspired method for detecting and parameterizing submovements in 1D speed time series from mouse aim movements. It incorporates a self-weighted loss refinement to address poor fits in overlapping regions, a limitation of basic wavelet approaches. The method is evaluated on ~6,400 synthetic 1-2s trials modeled from real data of 13 users, with known ground truth, and compared to dual-threshold and persistence segmentation techniques.

Significance. This work has potential significance for HCI research on motor control and interaction, as accurate submovement extraction can inform interface design and analysis of user behavior. The provision of synthetic data with ground truth for evaluation is a notable strength, enabling direct measurement of performance against known parameters. The self-weighted refinement step represents a thoughtful extension to handle real-world complexities in human motion. If the synthetic model is shown to be faithful, the approach could offer improvements over existing segmentation methods.

major comments (2)
  1. [Evaluation] The synthetic data generation process is described only at a high level as 'modeled from a similarly sized real data set of 13 users' without providing the specific modeling equations, parameters for overlap handling, duration distributions, or cyclical components. This information is essential to validate that the ground truth is representative of real overlapping submovements.
  2. [Evaluation] No specific quantitative metrics (such as localization error, parameterization accuracy, or F1 scores), error bars, or details on handling of overlapping submovements in the evaluation metrics are reported for the 6,400 trials. This absence makes it challenging to substantiate the claim of accuracy and to compare meaningfully with the baselines.
minor comments (1)
  1. [Abstract] The abstract introduces the 'self-weighted loss refinement step' without a brief inline description or reference to its formulation in the methods, which may leave some readers unclear on its novelty.
Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Based on the abstract alone, no free parameters, axioms, or invented entities are explicitly stated. The method relies on an unspecified wavelet transform and a self-weighted loss, but their precise definitions, any fitted scales, or background assumptions are not provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Short-time, Wavelet-inspired Mouse Submovement Detection." pith.science (2026). https://pith.science/paper/2604.20673

@misc{pith2026260420673,
  author       = {Pith},
  title        = {Pith review of: Short-time, Wavelet-inspired Mouse Submovement Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.20673}},
  note         = {Machine review of arXiv:2604.20673}
}
read the original abstract

Submovements are ballistic components of human motion constituting a large part of motor interaction and arising from the cyclical and overlapping cognitive processes of perception, motor planning, and motor execution. Extracting submovements is challenging as the motions tend to overlap, or start before the previous ends. We propose and evaluate use of a wavelet-inspired technique to accurately locate and parameterize submovements from one-dimensional speed time series. Our method employs a self-weighted loss refinement step to identify and improve regions of poor quality of fit, a challenge for simpler wavelet transforms. We demonstrate the accuracy of our method by presenting analysis of ~6,400 1-2s trials of synthetic egocentric camera (first-person shooter) aim data for which we know ground truth, modeled from a similarly sized real data set of 13 users. We compare our method to dual-threshold and the persistence 1D segmentation techniques and note challenges and opportunities for future improvements.

Figures

Figures reproduced from arXiv: 2604.20673 by the authors.

Figure 1
Figure 1. An example decomposition of a 3 submovement motion using a simple velocity-threshold based detector. (Left) the az [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Demonstration of overlap of two different submovements. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Plot of our mother (speed) "wavelet" with dilation parameter ( [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: High-level flow of our decomposition algorithm. Pre-processing entails: (1) low-pass filtering of the 2D time series, (2) taking a [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Example of a motion time series w/ 3 fit submovements, [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Demonstration of refinement of a poor fit based on (R)MSE and self-weighted loss. The (left) initial guess is based on a raw [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Evaluating submovement decomposition performance using real subject data. (Left) submovements tend to overlap more for [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Comparing three termination conditions for submovement decomposition. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: The task subjects completed in our experimental data. Before a trial (after a previous trial) the subject rotates the view to [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Probabilistic distributions of wavelet parameters: change in peak location ( [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 11
Figure 11. Figure 11: Evaluation of synthesized trials (blue) as compared with real data from 13 participants (grey/orange). (Left) a comparison of [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]
Figure 12
Figure 12. Figure 12: (Left) a comparison of different wavelet decomposition techniques over our synthetic data, note that wavelet-weighted loss [PITH_FULL_IMAGE:figures/full_fig_p010_12.png]
Figure 13
Figure 13. Figure 13: Qualitative comparison of the dual threshold, persistence 1D, and our wavelet detection algorithms. The dual threshold [PITH_FULL_IMAGE:figures/full_fig_p011_13.png]
Figure 14
Figure 14. Figure 14: Comparison of our method (in green) to results from a dual-threshold detector with 4 [PITH_FULL_IMAGE:figures/full_fig_p011_14.png]
Figure 15
Figure 15. Figure 15: (Left) An example of an early termination caused by a misfit submovement. In this case the submovement in gray is fit to the [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages

  1. [1]

    Ben Boudaoud, Josef Spjut, and Joohwan Kim. 2022. FirstPersonScience: An Open Source Tool for Studying FPS Esports Aiming. InACM SIGGRAPH 2022 Talks. Association for Computing Machinery, New York, NY, USA, 1–2

    work page 2022

  2. [2]

    Ben Boudaoud, Josef Spjut, and Joohwan Kim. 2022. Mouse Sensitivity in First-Person Targeting Tasks. In2022 IEEE Conference on Games (CoG) (Beijing, China). IEEE Press, New York, NY, USA, 183–190. doi:10.1109/CoG51982.2022.9893626

    work page doi:10.1109/cog51982.2022.9893626 2022

  3. [3]

    Robin T Bye and Peter D Neilson. 2008. The BUMP model of response planning: Variable horizon predictive control accounts for the speed–accuracy tradeoffs and velocity profiles of aimed movement.Human movement science27, 5 (2008), 771–798

    work page 2008

  4. [4]

    Lewis L Chen, Daeyeol Lee, Kikuro Fukushima, and Junko Fukushima. 2012. Submovement composition of head movement.PLoS One7, 11 (2012), e47565

    work page 2012

  5. [5]

    Yu Chen, Errol R Hoffmann, and Ravindra S Goonetilleke. 2015. Structure of hand/mouse movements.IEEE Transactions on Human-Machine Systems 45, 6 (2015), 790–798

    work page 2015

  6. [6]

    Edward Robert FW Crossman and PJ Goodeve. 1983. Feedback control of hand-movement and Fitts’ Law.The Quarterly Journal of Experimental Psychology Section A35, 2 (1983), 251–278

    work page 1983

  7. [7]

    Laura Dipietro, Hermano I Krebs, Susan E Fasoli, Bruce T Volpe, and Neville Hogan. 2009. Submovement changes characterize generalization of motor recovery after stroke.cortex45, 3 (2009), 318–324

    work page 2009

  8. [8]

    Natalia Dounskaia, Laetitia Fradet, Gyusung Lee, Berta C Leis, and Charles H Adler. 2009. Submovements during pointing movements in Parkinson’s disease.Experimental brain research193, 4 (2009), 529–544

    work page 2009

Show all 42 references
  1. [9]

    Natalia Dounskaia, Deric Wisleder, and Travis Johnson. 2005. Influence of biomechanical factors on substructure of pointing movements.Experimental Brain Research164, 4 (2005), 505–516

    2005

  2. [10]

    Alon Fishbach, Stephane A Roy, Christina Bastianen, Lee E Miller, and James C Houk. 2007. Deciding when and how to correct a movement: discrete submovements as a decision making process.Experimental brain research177, 1 (2007), 45–63

    2007

  3. [11]

    Paul M Fitts. 1954. The information capacity of the human motor system in controlling the amplitude of movement.Journal of experimental psychology47, 6 (1954), 381

    1954

  4. [12]

    Laetitia Fradet, Gyusung Lee, and Natalia Dounskaia. 2008. Origins of submovements during pointing movements.Acta psychologica129, 1 (2008), 91–100

    2008

  5. [13]

    Hiroaki Gomi. 2008. Implicit online corrections of reaching movements.Current opinion in neurobiology18, 6 (2008), 558–564

    2008

  6. [14]

    Suraj Gowda, Simon A Overduin, Mo Chen, Young-Hwan Chang, Claire J Tomlin, and Jose M Carmena. 2015. Accelerating submovement decomposition with search-space reduction heuristics.IEEE Transactions on Biomedical Engineering62, 10 (2015), 2508–2515

    2015

  7. [15]

    Joachim Groß, Lars Timmermann, Jan Kujala, Martin Dirks, Frank Schmitz, R Salmelin, and Alfons Schnitzler. 2002. The neural basis of intermittent motor control in humans.Proceedings of the National Academy of Sciences99, 4 (2002), 2299–2302

    2002

  8. [16]

    Errol R Hoffmann. 2016. Fitts’ law with an average of two or less submoves?Journal of motor behavior48, 4 (2016), 318–331

    2016

  9. [17]

    Neville Hogan and Dagmar Sternad. 2012. Dynamic primitives of motor behavior.Biological cybernetics106 (2012), 727–739

    2012

  10. [18]

    Nguyen, Keith B

    Juan Pablo Hourcade, Christopher M. Nguyen, Keith B. Perry, and Natalie L. Denburg. 2010. Pointassist for Older Adults: Analyzing Sub-Movement Characteristics to Aid in Pointing Tasks. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Atlanta, Georgi...

    2010 doi

  11. [19]

    Maaike A Huysmans, Marco JM Hoozemans, Allard J van der Beek, Michiel P de Looze, and Jaap H van Dieën. 2012. Submovement organization, pen pressure, and muscle activity are modulated to precision demands in 2D tracking.Journal of Motor Behavior44, 5 (2012), 379–388

    2012

  12. [20]

    Faustina Hwang, Simeon Keates, Patrick Langdon, and John Clarkson. 2003. Mouse Movements of Motion-Impaired Users: A Submovement Analysis. InProceedings of the 6th International ACM SIGACCESS Conference on Computers and Accessibility(Atlanta, GA, USA)(Assets ’04). Association ...

    2003 doi

  13. [21]

    Rakesh Krishnan, Niclas Björsell, and Christian Smith. 2017. Segmenting humeral submovements using invariant geometric signatures. In2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE Press, New York, NY, USA, 6951–6958. doi:10.1109/IROS.2017.8206619

    2017 doi

  14. [22]

    Byungjoo Lee, Mathieu Nancel, Sunjun Kim, and Antti Oulasvirta. 2020. AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’20). Association for Comput...

    2020 doi

  15. [23]

    Liao and Robert F

    James Y. Liao and Robert F. Kirsch. 2012. Predicting the initiation of minimum-jerk submovements in three-dimensional target-oriented human arm trajectories. In2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Press, New York, N...

    2012 doi

  16. [24]

    Kyle Lockwood, Yunus Bicer, Sadjad Asghari-Esfeden, Tianjie Zhu, Mariusz Furmanek, Madhur Mangalam, Garrit Strenge, Tales Imbiriba, Mathew Yarossi, Taskin Padir, Deniz Erdogmus, and Eugene Tunik. 2022. Leveraging Submovements for Prediction and Trajectory Planning for Human-Ro...

    2022 doi

  17. [25]

    David E Meyer, Richard A Abrams, Sylvan Kornblum, Charles E Wright, and JE Keith Smith. 1988. Optimality in human motor performance: ideal control of rapid aimed movements.Psychological review95, 3 (1988), 340. 16 Auejin Ham and Ben Boudaoud

    1988

  18. [26]

    Seyedeh Somayeh Naghibi, Ali Maleki, Ali Fallah, and Farnaz Ghassemi. 2017. A modified method of submovement decomposition based on velocity profile and endpoint position. In2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME). IEEE, IE...

    2017

  19. [27]

    Peter D Neilson and Megan D Neilson. 2005. An overview of adaptive model theory: solving the problems of redundancy, resources, and nonlinear interactions in human movement control.Journal of neural engineering2, 3 (2005), S279

    2005

  20. [28]

    KE Novak, Lee E Miller, and James Charles Houk. 2000. Kinematic properties of rapid hand movements in a knob turning task.Experimental Brain Research132 (2000), 419–433

    2000

  21. [29]

    KE Novak, Lee E Miller, and James Charles Houk. 2002. The use of overlapping submovements in the control of rapid hand movements.Experimental brain research144, 3 (2002), 351–364

    2002

  22. [30]

    Réjean Plamondon, Adel M Alimi, Pierre Yergeau, and Franck Leclerc. 1993. Modelling velocity profiles of rapid movements: a comparative study. Biological cybernetics69, 2 (1993), 119–128

    1993

  23. [31]

    Brandon Rohrer, Susan Fasoli, Hermano Igo Krebs, Bruce Volpe, Walter R Frontera, Joel Stein, and Neville Hogan. 2004. Submovements grow larger, fewer, and more blended during stroke recovery.Motor control8, 4 (2004), 472–483

    2004

  24. [32]

    Brandon Rohrer and Neville Hogan. 2003. Avoiding spurious submovement decompositions: a globally optimal algorithm.Biological cybernetics89, 3 (2003), 190–199

    2003

  25. [33]

    Brandon Rohrer and Neville Hogan. 2006. Avoiding spurious submovement decompositions II: a scattershot algorithm.Biological cybernetics94, 5 (2006), 409–414

    2006

  26. [34]

    2003.Submovement overlap as a measure of movement smoothness.Citeseer

    Brandon Robinson Rohrer and Neville Hogan. 2003.Submovement overlap as a measure of movement smoothness.Citeseer. https://citeseerx.ist.psu. edu/document?repid=rep1&type=pdf&doi=966121b80590297bf5262093a2f163753d73d3c9

    2003

  27. [35]

    Adam J Toth, Fazilat Hojaji, and Mark J Campbell. 2023. Exploring the mechanisms of target acquisition performance in esports: The role of component kinematic phases on a first person shooter motor skill.Computers in Human Behavior139 (2023), 107554

    2023

  28. [36]

    Gerard P van Galen and Willem P de Jong. 1995. Fitts’ law as the outcome of a dynamic noise filtering model of motor control.Human movement science14, 4-5 (1995), 539–571

    1995

  29. [37]

    Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J

    Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, E...

  30. [38]

    doi:10.1038/s41592-019-0686-2

    SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.Nature Methods17 (2020), 261–272. doi:10.1038/s41592-019-0686-2

    2020 doi

  31. [39]

    Neff Walker, David Philbin, Aileen Worden, and John B Smelcer. 1997. A program for parsing mouse movements into component submovements. Behavior Research Methods, Instruments, & Computers29 (1997), 456–460

    1997

  32. [40]

    Neff Walker, David A Philbin, and Arthur D Fisk. 1997. Age-related differences in movement control: Adjusting submovement structure to optimize performance.The Journals of Gerontology Series B: Psychological Sciences and Social Sciences52, 1 (1997), P40–P53

    1997

  33. [41]

    Tino Weinkauf. [n. d.]. https://www.csc.kth.se/~weinkauf/notes/persistence1d.html

  34. [42]

    Deric Wisleder and Natalia Dounskaia. 2007. The role of different submovement types during pointing to a target.Experimental Brain Research176 (2007), 132–149

    2007

Pith tools

Reviewed May 9, 2026 · model on record in the stance chip above.