Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals
This addresses robustness issues in human-machine interaction systems using biosignals, though it is incremental as it builds on existing neural network methods.
The paper tackled the problem of missing channels in multimodal biosignal data for hand gesture classification by proposing Random Channel Ablation during training, resulting in average improvements of 12.2% and 24.5% for up to 4 and 8 missing channels, respectively, compared to a baseline.
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.