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Related Experiment Video

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Assessment and Communication for People with Disorders of Consciousness

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Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals.

Keshav Bimbraw, Jing Liu, Ye Wang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 5, 2025

Summary

Random Channel Ablation (RChA) improves hand gesture classification using biosignals. This method enhances robustness against missing data channels in multimodal sensing, outperforming baseline and imputation techniques.

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Area of Science:

Biomedical Engineering
Human-Computer Interaction
Machine Learning

Background:

Biosignal-based hand gesture classification is crucial for human-machine interaction.
Multimodal biosignal sensing often suffers from data loss due to missing channels, impacting classification performance.
Developing robust classifiers for incomplete biosignal data is a significant challenge.

Purpose of the Study:

To propose and evaluate a novel method, Random Channel Ablation (RChA), to enhance the robustness of hand gesture classifiers against missing data channels.
To assess the effectiveness of RChA in multimodal biosignal classification using ultrasound and force myography (FMG).

Main Methods:

Acquired multimodal biosignal data (ultrasound and FMG) from the forearm for 12 hand gestures from 2 subjects.
Implemented Random Channel Ablation (RChA) during the training of a convolutional neural network architecture.
Compared RChA against baseline, imputation, and oracle methods using 5-fold cross-validation.

Main Results:

RChA demonstrated significant improvements in gesture classification accuracy compared to the baseline method.
An average improvement of 12.2% and 24.5% was observed with up to 4 and 8 missing channels, respectively.
The proposed RChA method showed superior robustness to increasing numbers of missing channels compared to other evaluated methods.

Conclusions:

Random Channel Ablation (RChA) is an effective strategy for improving classifier robustness in multimodal, multi-channel biosignal-based hand gesture classification.
RChA offers a promising approach to mitigate the adverse effects of data loss in real-world biosignal acquisition systems.
The findings highlight the potential of RChA for enhancing the reliability of human-machine interfaces reliant on biosignal interpretation.