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REVIEW 3 major objections 5 minor 54 references

Personalized Control for Lower Limb Prosthesis Using Kolmogorov-Arnold Networks

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper reports that KAN and FKAN, with their learnable activation functions, did not significantly beat MLP and CNN for lower-limb prosthetic turn prediction, and that deep models performed as well trained on pooled multi-participant…

desk verdict A first, honest KAN benchmark for prosthesis turn intent that finds no advantage over static activations, but its per-participant significance claims rely on non-independent test divisions. read the letter →

arxiv 2505.09366 v1 pith:JOBQDYS4 submitted 2025-05-14 cs.LG

SeyedMojtaba Mohasel , Alireza Afzal Aghaei , Corey Pew This is my paper · ORCID
classification cs.LG
keywords Kolmogorov-Arnoldnetworksprosthesiscontrolturnintentpredictioninertialmeasurementunitlearnableactivationfunctionsdeeplearninguser-specifictrainingpooled
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 asks whether Kolmogorov-Arnold Networks (KANs), which replace fixed neuron activations with learnable functions, improve turn-intent prediction for a lower-limb prosthesis controller, and whether training on a single user's data beats training on pooled data from several users. Using simulated shank IMU data from five people with transtibial (below-knee) amputations, it compares KAN against a multilayer perceptron (MLP) and a fractional KAN (FKAN) against a convolutional neural network (CNN), scoring macro-averaged F1 on three classes: straight walking, pre-turn swing, and turning stance. The main result is a null result: learnable activations were not significantly better than static activations, and for the deep models no significant difference appeared between user-specific and pooled training. For the conventional models, user-specific training was significantly better. The practical stake is whether a prosthesis controller can be trained once on pooled data or must be personalized, and whether the extra complexity of learnable activations buys anything.

What carries the argument

The object carrying the comparison is the Kolmogorov-Arnold Network, a neural architecture that puts learnable univariate functions—B-spline bases in KAN, fractional Jacobi basis functions in FKAN—on the edges between nodes, instead of fixed nonlinearities at the nodes; the rival MLP and CNN use static activations such as Tanh, ReLU, and SiLU. The evaluation machinery is a three-class turn-intent prediction task built from simulated shank IMU signals, segmented into overlapping windows of 10, 20, or 30 samples (83, 167, or 250 ms) with 50% overlap, balanced by class weights in the loss, and optimized with Bayesian architecture search. Two paired comparisons—KAN versus MLP and FKAN versus CNN—isolate the effect of learnable activations; training on user-specific versus pooled data isolates the data-selection effect. Macro-averaged F1 is the outcome metric, and Wilcoxon signed-rank plus Bayesian paired t-tests give the significance claims.

What would settle it

Re-evaluate the specific-versus-pooled comparisons on a single held-out test set per participant, or with block bootstrap over non-overlapping windows, so each observation is independent; if the MLP/KAN advantage over pooled training ceases to be significant at p < 0.05, the paper's Hypothesis 2 support would not survive.

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Extended reading notes

Core claim

On the paper's own terms, learnable activation functions fail their promised test in this application. Per-participant Wilcoxon signed-rank comparisons between KAN and MLP gave p-values from 0.46 to 0.98, and between FKAN and CNN from 0.08 to 1.00; Bayesian paired t-tests across participants were also non-significant. For the training-data question, user-specific training significantly outperformed pooled training for MLP and KAN (p = 0.002 and p = 0.015), but not for CNN and FKAN (p = 0.127 and p = 0.833). The paper interprets these results as evidence that the three-label turn-prediction task is simple enough that static activations suffice, and that deep feature-extracting models can exploit pooled multi-participant data while conventional models still need individualization.

Load-bearing premise

The statistical conclusions assume the ten stratified divisions of each participant's test data are independent paired samples, even though they are overlapping subsamples of the same test windows, so the reported p-values rest on that independence assumption.

Editorial extensions

If this is right

  • A VSTA-style controller could be trained on pooled multi-participant data if the deployed model is a CNN or FKAN, avoiding the cost of collecting per-user training data before first use.
  • For conventional models such as MLP or KAN, the paper's results predict a meaningful macro-F1 gain from retraining on the intended user's own data rather than relying on a population-trained model.
  • Adding KAN-style learnable activations to an existing MLP/CNN pipeline is unlikely to improve turn prediction, so the extra parameters and slower inference are not justified on this three-class task.
  • No single architecture wins for every participant; the best model varies, so a practical controller might select among CNN, FKAN, MLP, KAN, and the SVM baseline per user.
  • The roughly 300 ms swing-phase control window leaves room for CNN/FKAN prediction at tested PC-scale compute, though on-device latency still needs a microcontroller benchmark.

Reading between the lines

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

  • The paper's own explanation predicts that learnable activations would show an advantage on tasks with more action classes and larger data volumes; this is a direct, testable extension using a multi-activity IMU dataset.
  • If the pooled-training result for deep models repeats at larger participant counts, the personalization bottleneck in powered prosthetics shifts from data collection to model selection and on-device deployment.
  • The inference-time gap between KAN and MLP reported in the appendix suggests that realizing any future KAN advantage on a real controller will require approximating or pruning the B-spline or Jacobi basis evaluations, not just improving accuracy.
  • Recomputing the significance tests with non-overlapping folds or participant-level clustering would clarify how much of the MLP/KAN user-specific advantage is real, since the reported ten test divisions are overlapping subsamples of the same windows.
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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

3 major / 5 minor

Summary. The manuscript evaluates Kolmogorov-Arnold Networks (KAN) and fractional Kolmogorov-Arnold Networks (FKAN) for turn-intent prediction in a variable-stiffness torsion adapter for lower-limb prostheses, using IMU-derived data from five individuals with amputation. The authors compare KAN against MLP and FKAN against CNN to test whether learnable activation functions improve macro-averaged F1 over static activation functions, and they compare user-specific versus pooled training. Their reported results are that learnable activations do not significantly improve performance, that user-specific training outperforms pooled training for MLP and KAN (aggregate p < 0.05), and that no significant difference is observed for CNN and FKAN, which they interpret as pooled training being comparable for deep learning models.

Significance. If the claims hold, the paper provides one of the first assessments of KAN and FKAN for lower-limb prosthetic control, with a carefully constructed experimental setup that includes Bayesian hyperparameter optimization, class weighting, macro-F1 evaluation, and comparisons against a prior SVM baseline and Auto-sklearn. The per-participant analyses and the observation that KAN inference is substantially slower than MLP are practically useful for real-time control constraints. However, the statistical analysis of per-participant comparisons is flawed, and the equivalence claim for deep learning models is not supported by the data, so the reported significance of the user-specific versus pooled findings is overstated as presented.

major comments (3)
  1. [Section II-D, Figs. 4-5] The per-participant Wilcoxon signed-rank tests compare ten stratified random divisions of each participant's test set. Because these divisions are overlapping subsamples of the same test data, and because the underlying sliding windows themselves overlap by 50% (Section II-A5), the observations are not independent. Treating the ten F1 scores as independent paired samples violates the independence assumption of the Wilcoxon test and inflates the significance of the per-participant p-values reported in Fig. 5 (for example, 'p < 0.01' for MLP and 'p = 0.02' for KAN for each participant). This invalidates the per-participant evidence for Hypothesis 2 and similarly affects the per-participant Hypothesis 1 comparisons in Fig. 4. The authors should either use a single test-set evaluation per participant and test across participants, or use a dependence-aware resampling scheme such as a block bootstrap stratified by trial. The aggregate Bayesian paired t-test in Table IV uses one average per participant and is less affected by this issue, but the per-participant p-values in Figs. 4-5 should be corrected or removed.
  2. [Abstract, Section IV, Section V] The manuscript concludes that pooled training 'showed comparable performance' for deep learning models and that 'deep learning achieves similar performance using pooled data,' based on the absence of a statistically significant difference (Table IV: CNN p = 0.127, FKAN p = 0.833). With only five participants, a non-significant result does not establish equivalence. The authors should either conduct an equivalence test with a pre-specified margin (for example, two one-sided tests) or limit the conclusion to 'no evidence of a difference' and remove the strong claim of comparable performance.
  3. [Section II-A4 and II-D] The relationship between the test set construction described in Section II-A4 (one trial from each turn type at each stiffness setting, yielding nine turning events and three straight-walking trials) and the 'ten divisions of test data' used for the statistical tests is unclear. It is not evident whether the ten divisions are independently drawn subsets, overlapping bootstrap samples, or something else. Please define the divisions explicitly, and state whether they are overlapping or disjoint, since this is directly relevant to the validity of the Wilcoxon tests.
minor comments (5)
  1. [Section III] There is a typo in 'specifc vs pooled training' that should read 'specific vs pooled training.'
  2. [Section IV and Table VIII] The text states that KAN inference is 'approximately 1000 times greater' than MLP, but Table VIII shows total inference times of 0.2 s versus 0.02 s for participant A01, which is a factor of 10. Please reconcile the numbers or clarify the basis for the 1000-fold claim.
  3. [Section II-D and Table IV] The 'Bayesian paired t-test' results are reported as p-values (for example, 0.965, 0.002). A Bayesian analysis typically yields a posterior probability or a Bayes factor rather than a frequentist p-value; please specify the prior, the test statistic, and the decision rule used.
  4. [Introduction, Section I] The phrase 'parallel processing, which may reduce inference speed for prediction' is ambiguous; parallel processing generally reduces latency. Please rephrase to state the intended direction of the effect.
  5. [Figs. 4-5] The violin plots aggregate the ten overlapping test divisions; given the dependence concern, it might be clearer to display the individual per-division scores as points or to show a single test-set evaluation per participant.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: all central claims are empirical outcomes benchmarked against external baselines.

full rationale

This paper is an empirical benchmark study, not a derivation chain. The central claims — that KAN/FKAN learnable activations do not significantly beat MLP/CNN, that user-specific training beats pooled training for ML models, and that pooled training is comparable for DL models — are all direct experimental outcomes, reported as measured macro-F1 scores and p-values. No fitted parameter is renamed as a prediction, and no uniqueness theorem or ansatz is imported to force a choice. The self-citations are [9] (prior SVM baseline and dataset by co-author Pew) and [33] (FKAN architecture by co-author Aghaei); both are used as external experimental/comparison references rather than as proof of the present results. The FKAN-vs-CNN comparison actually reports no significant advantage for the authors' own FKAN architecture, so the citation does not load-bearingly favor the paper's hypothesis. The statistical caveat about Wilcoxon tests over overlapping ten test divisions is a validity limitation, not a circularity; it does not reduce any claim to its inputs by construction. Accordingly, no circular step is exhibited.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper is an empirical comparison, so there are no derivation-level free parameters. The main burdens are domain assumptions about simulated IMU data and stiffness pooling, plus the statistical assumption about independent test divisions.

assumptions (4)
  • domain assumption Simulated IMU data from the Vicon motion capture system adequately represents data from a physical IMU mounted on the shank.
    All model inputs are simulated from optical motion capture and post-processed in Visual 3D and MATLAB (Section II-A2). If real IMU noise and placement effects differ, the conclusions may not transfer.
  • domain assumption Combining data across three VSTA stiffness settings is valid because stiffness had no measurable effect on turning kinematics.
    The paper cites prior work [5] for this claim (Section II-A2). If stiffness subtly changes the signal, pooling may obscure subject-specific effects.
  • domain assumption The ten test divisions are independent paired samples for the Wilcoxon test.
    This is statistically questionable because divisions overlap; the paper does not account for this in Section II-D.
  • standard math Class weighting formula (1) correctly balances the three classes.
    Inverse-frequency weighting is a standard method; no controversy.

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Cite this review

Pith. "Pith review of Personalized Control for Lower Limb Prosthesis Using Kolmogorov-Arnold Networks." pith.science (2026). https://pith.science/paper/JOBQDYS4

@misc{pith2026250509366,
  author       = {Pith},
  title        = {Pith review of: Personalized Control for Lower Limb Prosthesis Using Kolmogorov-Arnold Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JOBQDYS4}},
  note         = {Machine review of arXiv:2505.09366}
}
abstract

Objective: This paper investigates the potential of learnable activation functions in Kolmogorov-Arnold Networks (KANs) for personalized control in a lower-limb prosthesis. In addition, user-specific vs. pooled training data is evaluated to improve machine learning (ML) and Deep Learning (DL) performance for turn intent prediction. Method: Inertial measurement unit (IMU) data from the shank were collected from five individuals with lower-limb amputation performing turning tasks in a laboratory setting. Ability to classify an upcoming turn was evaluated for Multilayer Perceptron (MLP), Kolmogorov-Arnold Network (KAN), convolutional neural network (CNN), and fractional Kolmogorov-Arnold Networks (FKAN). The comparison of MLP and KAN (for ML models) and FKAN and CNN (for DL models) assessed the effectiveness of learnable activation functions. Models were trained separately on user-specific and pooled data to evaluate the impact of training data on their performance. Results: Learnable activation functions in KAN and FKAN did not yield significant improvement compared to MLP and CNN, respectively. Training on user-specific data yielded superior results compared to pooled data for ML models ($p < 0.05$). In contrast, no significant difference was observed between user-specific and pooled training for DL models. Significance: These findings suggest that learnable activation functions may demonstrate distinct advantages in datasets involving more complex tasks and larger volumes. In addition, pooled training showed comparable performance to user-specific training in DL models, indicating that model training for prosthesis control can utilize data from multiple participants.

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Reference graph

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Pith tools

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