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REVIEW 4 major objections 5 minor 49 references

Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that bilateral vertical ground reaction forces in Parkinsonian gait can be estimated from as few as two wearable IMUs with accuracy comparable to a 13-sensor setup, using a CNN-BiLSTM deep-learning model.

desk verdict Useful feasibility study of CNN-BiLSTM for PD vGRF from IMUs, with an honest 13-IMU core but an optimized-sensor claim that is selected on the test set and needs external validation. read the letter →

arxiv 2608.02408 v1 pith:VYKPV6DM submitted 2026-08-03 cs.LG eess.SP

Run Lin , Yingtian Tang , Jiawen Xu , Dongfei Huo , Lefan Wang , Helen Dawes , Dominic J. Farris , Dong Wang
show 1 more author
Xijin Hua
This is my paper · ORCID
classification cs.LGeess.SP
keywords groundreactionforceestimationParkinson'sdiseasewearableIMUCNN-BiLSTMsensorplacementoptimizationverticalgaitanalysisdeeplearning
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 claims that vertical ground reaction forces during walking in Parkinson's disease can be estimated accurately from wearable inertial sensors using a deep-learning model, without force plates. A hybrid CNN-BiLSTM network, trained separately on 61 people with Parkinson's disease and 65 healthy controls, reaches $R^2 = 0.98$ when the same person's data appear in training, and $R^2 = 0.91$ (PD) and $R^2 = 0.93$ (HC) on people never seen during training. The paper also argues that sensor placement matters more in Parkinsonian gait: the full 13-sensor configuration is not needed, four carefully chosen IMUs give the top accuracy, and a two-IMU setup on the left foot dorsum and forehead keeps accuracy at $R^2 = 0.91$ on unseen PD patients. If true, this makes continuous, home-based gait monitoring for Parkinson's disease practical without laboratory equipment.

What carries the argument

The central object is the CNN-BiLSTM sequence-to-sequence regressor: one-dimensional convolutional layers extract local spatial features from the IMU channels, bidirectional LSTM layers model temporal dependencies across each 64-step sliding window, and fully connected layers output left and right vertical ground reaction forces normalized to body weight. The optimized-sensor analysis is carried by an exhaustive ablation over 1,023 IMU combinations (all subsets of 1 to 10 of the 13 placements after three low-accuracy single sensors are excluded), with every configuration retrained under the same inter-subject protocol. The two-IMU configuration at the left foot dorsum and forehead is the mechanism that turns the accuracy result into a practical wearable-monitoring claim.

What would settle it

Run the sensor-selection procedure again with a strict nested design: use only training and validation folds to pick the best two- and four-IMU configurations, freeze those configurations, and evaluate them on a test cohort of Parkinson's patients that was never touched during either model training or sensor selection. If the frozen two-IMU model's $R^2$ falls below roughly 0.85 or is materially worse than the 13-IMU model's $R^2$, the paper's central 'two IMUs are comparable' claim is contradicted. A direct field test of the LD + FH configuration on new patients with real wearable IMUs, without retraining, would also settle it.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that bilateral vertical ground reaction forces in Parkinsonian gait are learnable from body-worn inertial data under a realistic inter-subject protocol. With 13 IMUs the CNN-BiLSTM model reaches $R^2 = 0.98$ under within-subject training and, on people never seen in training, $R^2 = 0.91$ for PD patients and $R^2 = 0.93$ for healthy controls. The paper further claims that optimal sensor placement is disease-specific: for PD, a four-IMU configuration gives the best accuracy ($R^2 = 0.93$), and a two-IMU configuration at the left foot dorsum and forehead retains $R^2 = 0.91$, comparable to the full 13-sensor model. In a matched 12-IMU comparison, the model transfers to an independent PD dataset with $R^2 = 0.86$ versus $R^2 = 0.88$ on the primary data, a difference reported as not statistically significant.

Load-bearing premise

The practical conclusion that two IMUs are enough rests on accuracy numbers ($R^2 = 0.91$) that come from the same test set used to pick the best sensor combination out of 1,023 possibilities, with no separate unseen data to confirm that specific choice.

Editorial extensions

If this is right

  • A two-IMU wearable (left foot dorsum plus forehead) can estimate bilateral vertical ground reaction forces on new Parkinson's patients at $R^2 = 0.91$, which is enough for gait monitoring without force plates.
  • Because the optimal sensor set differs between Parkinson's disease and healthy controls, wearable gait systems should be validated with disease-specific sensor placements rather than a universal configuration.
  • The cross-dataset transfer result suggests that a model pretrained on one Parkinson's cohort can be adapted to another cohort through transfer learning, retaining most of its accuracy.
  • Adding sensors beyond four yields only marginal accuracy gains, so a small, strategically placed set is sufficient for practical deployment in this population.
  • The intra-subject $R^2 = 0.98$ result implies that a short personalized calibration recording could support high-fidelity individual gait monitoring in a clinic.

Reading between the lines

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

  • Because the same inter-subject test set was used both to select the best configurations and to report their $R^2$, the headline two-IMU and four-IMU accuracies are probably somewhat optimistic; a nested or fully separated selection hold-out would give the unbiased numbers.
  • The left-dorsum sensor's dominance in Parkinson's disease may reflect the cohort's left-dominant symptom laterality, which suggests that a mirrored LD + FH setup could be more appropriate for patients with right-dominant symptoms.
  • The same exhaustive ablation protocol could be transferred to other asymmetric or pathological gaits, such as stroke or osteoarthritis, to test whether disease-specific optimal sensor sets are a general rule rather than a Parkinson's peculiarity.
  • Real-world deployment would need to confirm that the two-IMU model tolerates sensor repositioning, day-to-day variability, and magnetic or mechanical disturbances, since the external validation used motion-capture-derived virtual IMUs rather than physical wearable recordings.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript reports a deep-learning pipeline for estimating bilateral vertical ground reaction forces (vGRFs) in Parkinsonian gait from wearable IMU signals. Using a public multi-site dataset (61 PD patients and 65 age-matched healthy controls), the authors train a CNN-BiLSTM model on 13 IMUs and report intra-subject R2 = 0.98 for both cohorts and inter-subject R2 = 0.93 for HC and 0.91 for PD. They compare the proposed model against MLP, TCN, Transformer, CNN-only, and BiLSTM-only baselines under the full 13-IMU inter-subject setting, and they perform a cross-dataset external evaluation using OpenSim-derived virtual IMU signals from a separate PD dataset. The central novel contribution is a sensor-configuration ablation in the PD cohort: after screening the 13 single-IMU placements, the authors retrain the model for all 1,023 combinations of the remaining 10 IMUs and identify the best one- through ten-IMU configurations by test-set R2. They conclude that a two-IMU configuration (left dorsum and forehead) attains R2 = 0.91, comparable to the full set, and that a four-IMU configuration (reported with different sensor lists in different sections) attains R2 = 0.93.

Significance. If the inter-subject results survive scrutiny, the paper would be a valuable contribution: it is among the first to show that IMU-to-vGRF regression can generalize across unseen PD patients with cohort-level R2 above 0.9, and the systematic sweep of sensor configurations is a useful design resource. The study has clear strengths: subject-wise and trial-wise splitting before windowing, z-score standardization computed on training data only, a public data source, baseline comparisons under matched settings, and an explicit acknowledgement of the virtual-IMU limitation in the external validation. However, the headline minimal-sensor claim is currently an optimization on the test set rather than a predictive evaluation, and the label signal is pressure-derived rather than force-plate measured. These issues bear directly on the magnitude of the claimed two-IMU accuracy and on its comparison with prior force-plate-based studies.

major comments (4)
  1. [III.E; II.F] The optimized sensor configurations are selected by taking, for each sensor count, the combination with the highest R2 among 1,023 retrained models, all evaluated on the same inter-subject PD test set used to report the final accuracy. The reported two-IMU (LD/FH) R2 = 0.91 ± 0.09 and four-IMU R2 = 0.93 are therefore maxima over a large set of correlated R2 statistics, not unbiased predictions for new subjects, and the 13-IMU baseline is the only result with an honest train/test split. No outer hold-out or nested validation is described to freeze the sensor configuration before final evaluation. Because the abstract's central claim is that a two-IMU setup provides estimation comparable to the full set, this selection bias must be addressed, for example by reserving an outer test set for the chosen configuration or by reporting selection-corrected estimates and reframing the sweep as exploratory.
  2. [III.E vs. IV.B] The best four-IMU configuration is reported inconsistently: Section III.E states it is LD, RA, RS, and FH, while Section IV.B states it is LD, RS, RT, FH, both with R2 = 0.93. The Fig. 4/Fig. 5 summary should be checked against these lists. This discrepancy means the reader cannot tell which sensor set produced the headline four-IMU accuracy and suggests the optimal-configuration results are not being reported from a single protocol. Please reconcile the text, figure, and table, and ensure the accuracy value corresponds to the listed configuration.
  3. [II.F; III.C] The initial screening step excludes XP, LW, and RW because their single-IMU R2 values fall below 0.8 in the PD inter-subject test set, and the 0.80 threshold is applied after inspecting the test-set results in Fig. 2. This is a further test-set-based selection, and it restricts the subsequent 1,023-combination search to the 10 sensors that already performed best on the test data. The screening decision should be made on training/validation data only, or the entire sweep should be treated as exploratory and validated on an untouched cohort.
  4. [II.B] The vGRF labels in the primary dataset are not force-plate measurements but estimates derived from pressure-walkway contact-area and pressure data via a static-standing calibration. The reported coefficient of variation of less than 0.03 is a repeatability measure and does not establish agreement with a gold-standard force measurement. Since every R2 and MAE in the paper is computed against these derived labels, the claims should be qualified as estimation of pressure-derived vGRFs, or a subset should be validated against simultaneous force-plate data, before comparing absolute error levels with force-plate-based studies.
minor comments (5)
  1. [II.F] The text contains the typo "one way ANOV A" and should read "one-way ANOVA"; the same typo appears in the following sentence.
  2. [Fig. 2 caption] The caption uses "Left Mid-Lateral Thigh (LM)" and "Right Mid-Lateral Thigh (RM)", which are inconsistent with Table II's "Left lateral thigh (LT)" and "Right lateral thigh (RT)"; please use one abbreviation set throughout.
  3. [Fig. 5 caption; Conclusion] The caption phrase "alone with the worst-performing" should read "along with the worst-performing", and the conclusion's "This study firstly proposes" should be rephrased to standard English.
  4. [Abstract; I] The claim that "no study has investigated" deep-learning-based GRF estimation in PD should be softened to "to our knowledge", and the closest prior works cited later (e.g., Eltoukhy et al. and Liu et al.) should be explicitly distinguished in the introduction.
  5. [III.B; Table VI] The statement that a non-significant difference in R2 between the primary and external datasets (p = 0.51) supports cross-dataset generalizability should be backed by an equivalence test or confidence intervals, since a non-significant test is not evidence of equivalence, particularly because the external test uses virtual IMU signals (a caveat acknowledged in Section IV.C).

Circularity Check

2 steps flagged · score 6.0 of 10

Optimized two-/four-IMU R² values are selected maxima over 1,023 test-set evaluations, not independent predictions.

  1. fitted input called prediction [Section III.E (Impact of IMU Quantity on Optimal Placement and Estimation Accuracy in PD Patients)]
    "To determine the optimal IMU placement for varying sensor configurations under realistic generalization conditions, an ablation study was conducted within the PD inter-subject evaluation setting, evaluating a total of 1,023 unique IMU combinations. The optimal configuration for each sensor count was defined as the combination that achieved the highest R2 among all possible configurations with the same number of IMUs."

    The same PD inter-subject test set is used both to choose the optimal sensor set (maximum R2 over 1,023 retrained models) and to report that set's accuracy (two-IMU LD+FH R2=0.91; four-IMU R2=0.93). No outer hold-out is reserved to freeze the selected configuration before final evaluation, so the reported optimized-set accuracy is the maximum of 1,023 correlated test-set statistics. The claim that a two-IMU or four-IMU setup maintains comparable accuracy is thus a selection result presented as an unbiased prediction: the fitted selection statistic and the reported performance statistic are the same R2 on the same test data.

  2. fitted input called prediction [Section II.F (Statistical Tests and Ablation Study)]
    "IMU placements failing to meet this criterion were excluded. As a result, 10 IMUs, with a total of 1,023 IMU combinations, were selected for the subsequent ablation study."

    The search space itself is built by thresholding single-IMU R2 values computed on the same inter-subject test set that later selects the optimal configurations. This is an earlier test-set-based filter on the same target statistic; it narrows the configuration space using test data and further contributes to the optimistic bias of the subsequent 1,023-way maximum. The optimized-set result therefore depends on test-set information at two stages, not solely on the final maxima.

full rationale

The full-13-IMU feasibility claim, the intra-/inter-subject comparisons, baseline model comparisons, and matched 12-IMU external validation are self-contained: they use proper train/test splits and do not depend on sensor selection, so no circularity attaches to those results. The circularity burden is concentrated in the optimized-minimal-configuration claims. In Sections II.F and III.C-III.E, single-IMU R2 values on the inter-subject PD test set are used to discard sensors, and then 1,023 configurations are retrained and the configuration with the highest R2 on that same test set is declared optimal and its R2 is reported as expected performance. Because the reported two-IMU (LD+FH, R2=0.91) and four-IMU (R2=0.93) numbers are maxima over test-set evaluations with no nested outer hold-out, they are fitted statistics rather than predictions for new subjects. This matches the 'fitted input called prediction' pattern. Additionally, the optimal four-IMU configuration is inconsistent across the paper (Section III.E: LD, RA, RS, FH; Section IV.B: LD, RS, RT, FH; Conclusion: LD, RA, RS, FH), which makes the selected-configuration result not reproducibly reportable. These issues affect only the optimized-set claims; hence a moderate-to-substantial partial circularity score of 6 is appropriate rather than a higher score that would apply if the central 13-IMU derivation itself reduced to its inputs.

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

The paper introduces no new physical entities. Its central empirical claims rest on three assumptions: pressure-derived vGRF labels are treated as ground truth, virtual IMU signals stand in for real IMU data in external validation, and sensor selection on the inter-subject test set does not bias reported R2 values. The third is violated by the protocol.

free parameters (3)
  • CNN-BiLSTM hyperparameters = CNN channels 256/128, LSTM hidden 256, 3 LSTM layers, FC sizes 60/30, dropout 0.1, learning rate 1e-4, window 64…
    Selected via Optuna on the training/validation set; these shape the estimator but are not physical constants.
  • IMU sensor configuration subset = LD+FH for two-IMU setup; LD, RA, RS, FH for four-IMU setup in Results, but LD, RS, RT, FH in Discussion
    Chosen as the highest R2 among 1,023 combinations evaluated on the inter-subject test set, so the reported accuracy is in-sample with respect to sensor selection.
  • Single-IMU screening threshold = R2 > 0.80
    Ad hoc criterion from prior work used to exclude XP, LW, and RW before the exhaustive ablation; this changes the configuration space searched.
assumptions (3)
  • domain assumption Pressure-walkway vGRF estimates are treated as ground truth labels.
    The primary labels are derived from pressure and contact area via static calibration, not from force plates; a CV below 0.03 is reported but the model target remains an indirect estimate.
  • domain assumption Simulated virtual IMU signals from motion capture represent real wearable IMU signals.
    External validation in Methods B uses OpenSim-derived virtual IMUs because the external dataset lacked wearable sensors; the paper acknowledges this in Limitations.
  • ad hoc to paper Selecting the best test-set R2 among 1,023 sensor combinations does not bias the reported performance.
    The exhaustive ablation in Section III.E defines optimal configurations as the highest R2 on the inter-subject test set and then reports those R2 values; no nested or independent validation is performed, so this assumption is violated.

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Cite this review

Pith. "Pith review of Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data." pith.science (2026). https://pith.science/paper/VYKPV6DM

@misc{pith2026260802408,
  author       = {Pith},
  title        = {Pith review of: Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VYKPV6DM}},
  note         = {Machine review of arXiv:2608.02408}
}
abstract

Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction forces (GRFs). Estimating GRFs using inertial measurement units (IMUs) provides a feasible alternative. However, this approach remains challenging in pathological gait like PD due to its high variability and complexity. Moreover, existing monitoring approaches often require multiple body-mounted sensors, which limit practicality and reduce patient compliance. To date, no study has investigated the application of deep learning approaches to address this challenge. This study proposes, for the first time, a deep learning framework to estimate bilateral vertical GRFs (vGRFs) in PD using an optimized set of wearable IMUs. A hybrid CNN-BiLSTM model was trained separately on data from 61 PD patients and 65 healthy controls (HC) using 13 IMUs. The model achieved high intra-subject accuracy ($R^2$ = 0.98) and strong inter-subject generalization ($R^2$ = 0.93 for HC, $R^2$ = 0.91 for PD). Sensor configuration was found to significantly influence estimation accuracy, with optimal sensor placement varying between PD patients and HC. For PD patients, estimation accuracy dropped markedly when reducing to a single IMU. The optimal configuration for PD used four IMUs. We identified a minimal setup with only two IMUs still enabled robust estimation. This compact setup offers a practical and scalable solution. Overall, the proposed approach supports the development of wearable vGRF-based gait analysis systems for Parkinsonian gait and potentially other pathological conditions, enabling accessible clinical assessments, remote monitoring, and personalized rehabilitation.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.