Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features

Ajita Rattani; Parshuram N. Aarotale

REVIEW 3 major objections 5 minor 48 references

Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features

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

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Pith's one-line read Mutual-attention layers consistently improve EMG gesture models

desk verdict A thin but honest application of an existing fine-grained attention network to EMG time-frequency images; the central claim of consistent gains is not yet supported by the evidence. read the letter →

arxiv 2504.14708 v1 pith:P32ULYGR submitted 2025-04-20 cs.CV cs.AIcs.LG

Parshuram N. Aarotale , Ajita Rattani This is my paper · ORCID

classification cs.CVcs.AIcs.LG

keywords ElectromyographyEMGgesturerecognitionFine-grainedclassificationCross-layermutualattentionTime-frequencyanalysisShort-TimeFourierTransformWaveletscalogramCNNensembles

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 tries to establish that fine-grained classification, implemented as cross-layer mutual attention learning, improves EMG-based hand gesture recognition when applied to time-frequency images. It introduces XMANet, which treats each CNN layer as an expert that exchanges attention-guided regions with other layers, and tests it on STFT spectrograms and wavelet scalograms. Across two public datasets, Grabmyo and FORS-EMG, XMANet reports higher accuracy than four standard CNN baselines, with the largest gains on the harder FORS-EMG dataset. The sympathetic reading is that fine-grained, multi-scale features carry information that ordinary CNNs miss, and that the mutual-attention mechanism recovers this information.

What carries the argument

The central object is XMANet, which builds a hierarchy of N experts, each aggregating convolutional layers from the shallowest to a successively deeper point in a backbone CNN. Each expert produces a prediction from a global-max-pooled descriptor, and also generates a class-activation-map attention region that is cropped and used as an augmented training sample for the other experts. Training proceeds in a multi-step schedule: the deepest expert is trained first, then shallower experts, then a joint pass on the fused attention map, then a joint pass on the raw image. At inference, the network averages predictions from all experts on both the raw image and the overall attention map, yielding 2×(N+1) scores.

What would settle it

Re-run each baseline and each XMANet variant on the same train/validation/test splits with multiple random seeds (e.g., 10 seeds) and compute the mean and standard deviation of test accuracy; if the accuracy differences between XMANet and its corresponding baseline shrink to within one standard deviation across seeds, the central claim of consistent improvement collapses.

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Extended reading notes

Core claim

XMANet, a cross-layer mutual attention learning network, consistently outperforms standard CNN baselines on EMG gesture recognition when the input is a time-frequency image (STFT spectrogram or wavelet scalogram). The paper reports accuracy improvements over ResNet50, DenseNet121, MobileNetV3, and EfficientNetB0 on both the Grabmyo and FORS-EMG datasets, with the largest relative gains on FORS-EMG, where wavelet scalograms and STFT spectrograms leave more room for improvement. The core claim is that fine-grained features extracted from shallow-to-deep layers, combined through mutual attention and attention-based data augmentation, yield robust and accurate EMG classification.

Load-bearing premise

The paper assumes that a single run of each model, chosen by best validation loss, gives a reliable estimate of test accuracy, so that differences of a few tenths of a percentage point are meaningful rather than random noise.

Editorial extensions

If this is right

If XMANet's gains hold, the same cross-layer mutual-attention recipe can be applied to other backbones and other physiological signal modalities, not just EMG.
The larger improvements on the FORS-EMG dataset suggest the method is most valuable when the time-frequency representation is noisy or the classification task is harder.
The attention-region-based data augmentation provides a built-in way to focus models on discriminative parts of the spectrogram or scalogram, which could improve interpretability.
The paper's consistent improvements across four architectures imply that the mechanism is architecture-agnostic, so practitioners can retain their preferred CNN and wrap XMANet around it.
The two-pass inference strategy (raw plus attention map) is a simple test-time augmentation that could be combined with other ensembling methods.

Reading between the lines

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

The reported gains, especially the small ones (0.03 to 0.5 percentage points on Grabmyo segmented images), may lie within run-to-run variance; repeated-seed experiments with confidence intervals would clarify the true effect size.
The method's benefit appears larger when the baseline accuracy is lower, suggesting that mutual attention mainly helps by recovering information that standard CNNs discard on harder inputs, not by re-ranking already-easy examples.
A natural extension is to apply XMANet directly to raw or lightly preprocessed EMG waveforms instead of spectrogram images, which would remove the time-frequency conversion step and potentially preserve temporal resolution.
Because the attention regions are generated per predicted class, the method could be adapted for weakly supervised localization of the informative muscle-activation patterns, connecting gesture recognition with interpretability for prosthetics.

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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

Quick take: this is an applied benchmark paper that takes the XMANet architecture from Liu et al. 2023 and runs it on STFT spectrograms and CWT scalograms of EMG data. The combination is new, the datasets are public, and the comparisons are standard. But the paper oversells its results. The headline improvements are mostly small, the abstract numbers don't match the tables, and there are no repeated runs or significance tests. One variant actually underperforms its baseline, which contradicts the word 'consistently.'

What's genuinely useful: the authors test four backbones (ResNet50, DenseNet121, MobileNetV3, EfficientNetB0) on two distinct EMG datasets and report accuracy/precision/recall/F1. If reproducible, this gives the community reference numbers for a known architecture on these tasks. The preprocessing pipeline (0.6s windows, 50% overlap, stacked spectrograms/scalograms) is described in enough detail to follow.

The soft spots are real. First, the numerical inconsistencies: the abstract claims a 1.72% gain for XMANet(ResNet50) on Grabmyo STFT, but Table 2 shows 93.03 vs 91.46 = 1.57. Similar mismatches appear elsewhere. Second, there is no estimate of run-to-run variance. The best model is selected by validation loss and evaluated once; differences of 0.2–2 points are exactly the kind that vanish across seeds. Third, Table 5 has a negative result: XMANet(EfficientNetB0) at 48.08% against a 48.18% baseline. That is not fatal by itself, but it kills the 'always improves' phrasing. Fourth, the method is adopted, not invented. The authors cite Liu et al. [24], but the abstract and contribution list present XMANet as their own without explicitly saying it is a direct application. That needs to be fixed.

On balance, the empirical direction is plausible and the larger gains on FORS-EMG (e.g., DenseNet121 on wavelet: 60.49 vs 55.31) deserve a closer look. But the paper as written does not establish reliable gains.

Recommendation: send to peer review with a major-revision bar. The work is sufficiently substantive for referee time, and the issues are reparable with multi-seed results, corrected numbers, and honest attribution. I would not cite it in its current form.

Referee Report

3 major / 5 minor

Summary. The paper proposes XMANet, a fine-grained classification model that applies cross-layer mutual attention among CNN experts, for EMG-based hand gesture recognition. Raw EMG is converted into stacked STFT spectrograms and wavelet scalograms, which are then classified by XMANet built on top of ResNet50, DenseNet121, MobileNetV3, and EfficientNetB0 backbones. Experiments on the Grabmyo and FORS-EMG datasets report accuracy, precision, recall, and F1, and the abstract and conclusion claim that XMANet consistently improves performance over all baselines on both time-frequency representations.

Significance. If the central claim were supported, the paper would provide a simple, architecture-agnostic way to improve EMG gesture recognition from time-frequency images, and the systematic comparison of STFT versus wavelet representations across four backbones on two public datasets would be a useful empirical contribution. The paper is transparent in reporting per-model accuracies and uses standard preprocessing and evaluation protocols. However, the methodological novelty is limited because XMANet is essentially the cross-layer mutual attention learning method of Liu et al. [24] applied to EMG spectrograms and scalograms; the contribution is empirical rather than architectural. The reliability of the empirical claim is the main issue: the evidence consists of single training runs without error bars or significance tests, and the paper contains a negative result in Table 5 that is not acknowledged. If the experimental protocol is strengthened, the study could be a valid application-oriented contribution.

major comments (3)

[Abstract, Section 6.1, Tables 2 and 3] The headline improvement percentages in the abstract and conclusion (e.g., 1.72%, 4.38%, 5.10%, 2.53% for STFT on Grabmyo) are relative percentage increases, not absolute accuracy gains. For example, XMANet(ResNet50) at 93.03% versus ResNet50 at 91.46% is a gain of 1.57 percentage points, which is a 1.72% relative improvement. Elsewhere in Section 6.1 (Table 1 discussion) the text uses absolute percentage points, e.g., 'improved accuracy by 0.424% relative.' The paper never defines which quantity is being reported, so the quantitative claims in the abstract and conclusion are ambiguous and the two usages are inconsistent.
[Section 5.2 and Section 6] All results come from a single training run per configuration: the model with the best validation loss is saved and evaluated once on the test set, with no seeds, repeated runs, confidence intervals, or statistical significance tests reported. Many of the claimed improvements are very small (e.g., 0.03 percentage points in Table 1 and 0.21 percentage points in Table 5), and without an estimate of run-to-run variability these differences cannot be distinguished from training noise. Since the paper's central claim is that XMANet consistently improves accuracy, this lack of statistical support is load-bearing.
[Table 5, Section 6.2] The claim that XMANet 'consistently improves performance' is contradicted by the paper's own results in Table 5: on FORS-EMG STFT, XMANet(EfficientNetB0) obtains 48.08% accuracy versus 48.18% for the EfficientNetB0 baseline, a decrease. The surrounding text does not acknowledge this negative result, and the summary in Section 6.2 reports only the positive gains. The claim of consistency must either be removed or qualified, and the negative case should be explained or shown to be within the run-to-run variation.

minor comments (5)

[Section 5.2, Section 6.1] The sentence listing the four pre-trained architectures says 'ResNet50, MobileNetV3, and DenseNet121' but omits EfficientNetB0, which is nevertheless used in all experiments. Also, the text says 'DenseNet121 model achieves an accuracy of 98.44%' while Table 1 reports 99.44%.
[Equation (11)] The min-max normalization formula is written as '(α,β) = ˜Ωn(α,β) - min( ˜Ωn)/max( ˜Ωn) - min( ˜Ωn)', which is missing parentheses and is not mathematically well-formed; it should be (˜Ωn - min(˜Ωn)) / (max(˜Ωn) - min(˜Ωn)).
[Section 4, Section 5.1] The description of Grabmyo does not state how many EMG channels are used, while FORS-EMG is described as eight channels; channel count is important for reproducing the stacked spectrogram and scalogram images.
[Section 6.2] The list of improvements in the text mixes comparisons against different baselines without a consistent convention; for instance, 'the proposed XMANet(ResNet50) model outperforms the baseline ResNet50, EfficientNetB0, MobileNetV3, and DenseNet121 models with improvements of approximately 5.04%, 15.44%, 34.73%, and 2.53%' is confusing because each number is relative to a different baseline. A single table of baseline-vs-proposed pairwise differences would improve readability.
[Section 2.2] The reference to 'Zhao et al. [17]' appears to mismatch the cited paper by W. Zhang and J. Zhang; the authors should verify all reference-author name alignments.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: XMANet is an empirical application of an externally published mutual-attention method to EMG time–frequency inputs, benchmarked against standard baselines.

full rationale

The paper makes no first-principles derivation whose conclusion is secretly an input. XMANet is explicitly adopted from an external prior work ([24], Liu et al., Pattern Recognition 2023), and the present contribution is an empirical application to STFT spectrograms and wavelet scalograms from EMG with standard train/validation/test splits (Section 5.2). No parameter is fitted to the test set and then reported as a prediction; the validation-loss-based model selection is standard practice, and the reported differences, though small and unrepeated, are direct comparisons rather than quantities constructed from the compared baselines. The single self-citation ([47], Manzoor and Rattani, FineFace) appears only in a related-work sentence on fine-grained classification and is not load-bearing: no claim in the paper is justified by that citation. The paper's numerical inconsistencies (e.g., Table 5 shows XMANet(EfficientNetB0) at 48.08% vs. baseline 48.18%, contradicting the abstract's 'consistently improves') are correctness/reproducibility concerns, not circularity, because they do not arise from a definitional equivalence or a fitted-input-renamed-as-prediction. Accordingly, no circular step is present.

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

The paper relies on standard signal processing transforms and on the transferability of ImageNet features to time-frequency images. The number of experts, wavelet scale range, and STFT window parameters are design choices without ablation or sensitivity analysis.

free parameters (3)

Number of experts N = 3 (from Figure 2)
The paper uses three experts in XMANet but does not justify or ablate this choice.
Wavelet scale range = 1 to 128
The CWT scales are fixed to 1 to 128 to capture EMG frequency components; no sensitivity analysis is provided.
STFT window parameters = Unspecified
The window function, window length, and hop size for the STFT are not specified, making exact reproduction impossible.

assumptions (3)

standard math STFT and CWT provide valid time-frequency representations of the EMG signal.
Equations 1 to 7 define standard transforms; the paper assumes these transforms capture discriminative signal information.
domain assumption ImageNet-pretrained CNN weights transfer to time-frequency images of EMG.
Section 5.2 says the CNNs are fine-tuned from pretrained weights; the paper provides no evidence that ImageNet features are suitable for spectrogram images.
domain assumption A 0.6 second window with 50 percent overlap produces segments that are independent enough for training and testing.
Section 5.2 describes this segmentation; the paper does not discuss possible temporal leakage between training and test segments.

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Cite this review

Pith. "Pith review of Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features." pith.science (2026). https://pith.science/paper/P32ULYGR

@misc{pith2026250414708,
  author       = {Pith},
  title        = {Pith review of: Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P32ULYGR}},
  note         = {Machine review of arXiv:2504.14708}
}

read the original abstract

Electromyography (EMG) based hand gesture recognition converts forearm muscle activity into control commands for prosthetics, rehabilitation, and human computer interaction. This paper proposes a novel approach to EMG-based hand gesture recognition that uses fine-grained classification and presents XMANet, which unifies low-level local and high level semantic cues through cross layer mutual attention among shallow to deep CNN experts. Using stacked spectrograms and scalograms derived from the Short Time Fourier Transform (STFT) and Wavelet Transform (WT), we benchmark XMANet against ResNet50, DenseNet-121, MobileNetV3, and EfficientNetB0. Experimental results on the Grabmyo dataset indicate that, using STFT, the proposed XMANet model outperforms the baseline ResNet50, EfficientNetB0, MobileNetV3, and DenseNet121 models with improvement of approximately 1.72%, 4.38%, 5.10%, and 2.53%, respectively. When employing the WT approach, improvements of around 1.57%, 1.88%, 1.46%, and 2.05% are observed over the same baselines. Similarly, on the FORS EMG dataset, the XMANet(ResNet50) model using STFT shows an improvement of about 5.04% over the baseline ResNet50. In comparison, the XMANet(DenseNet121) and XMANet(MobileNetV3) models yield enhancements of approximately 4.11% and 2.81%, respectively. Moreover, when using WT, the proposed XMANet achieves gains of around 4.26%, 9.36%, 5.72%, and 6.09% over the baseline ResNet50, DenseNet121, MobileNetV3, and EfficientNetB0 models, respectively. These results confirm that XMANet consistently improves performance across various architectures and signal processing techniques, demonstrating the strong potential of fine grained features for accurate and robust EMG classification.

Figures

Figures reproduced from arXiv: 2504.14708 by the authors.

**Figure 2.** This figure illustrates XMANet method by introducing three experts [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** A) Electrode Positions [30] and B) Gesture list for Grabmyo dataset [30] and C) FORS-EMG dataset [31]. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

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Pith tools

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

[1] [24]

D. Liu, L. Zhao, Y . Wang, J. Kato, Learn from each other to classify better: Cross-layer mutual attention learning for fine-grained visual classification, Pattern Recognition 140 (2023) 109550. 18

work page 2023

[2] [1]

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M. Atzori, M. Cognolato, H. M ¨uller, Deep learning with convolutional neural networks applied to electromyo- graphy data: A resource for the classification of movements for prosthetic hands, Frontiers in neurorobotics 10 (2016) 9

work page 2016

[3] [2]

D. K. Kumar, B. Jelfs, X. Sui, S. P. Arjunan, Prosthetic hand control: A multidisciplinary review to identify strengths, shortcomings, and the future, Biomedical Signal Processing and Control 53 (2019) 101588. 17

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