Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals

TL;DR

This paper introduces Random Channel Ablation (RChA), a training technique that enhances the robustness of multimodal biosignal classifiers to missing data channels, significantly improving gesture recognition accuracy.

Contribution

The paper presents RChA, a novel training method that improves robustness of biosignal classifiers against missing channels, demonstrated on ultrasound and FMG data for hand gestures.

Findings

12.2% and 24.5% accuracy improvement with missing channels

Robustness to increasing missing channels

Effective across multimodal biosignal data

Abstract

Biosignal-based hand gesture classification is an important component of effective human-machine interaction. For multimodal biosignal sensing, the modalities often face data loss due to missing channels in the data which can adversely affect the gesture classification performance. To make the classifiers robust to missing channels in the data, this paper proposes using Random Channel Ablation (RChA) during the training process. Ultrasound and force myography (FMG) data were acquired from the forearm for 12 hand gestures over 2 subjects. The resulting multimodal data had 16 total channels, 8 for each modality. The proposed method was applied to convolutional neural network architecture, and compared with baseline, imputation, and oracle methods. Using 5-fold cross-validation for the two subjects, on average, 12.2% and 24.5% improvement was observed for gesture classification with up to 4 and 8 missing channels respectively compared to the baseline. Notably, the proposed method is also robust to an increase in the number of missing channels compared to other methods. These results show the efficacy of using random channel ablation to improve classifier robustness for multimodal and multi-channel biosignal-based hand gesture classification.