  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.
