Optimizing the feature set and electrode configuration of high-density electromyogram via interpretable deep forest

Hand gesture recognition via high-density surface electromyogram (HDsEMG) has received increasing atten-tions in human-machine interactions. However, with an extremely large number of electrodes in the electrode arrays, the computational burden substantially increased in real world mobile computing applications. Additionally, with diverse features extracted from all electrodes, large information redundancy reduced the learning efficiency of machine learning models. Furthermore, most machine learning models were employed as black-box modules, increasing concerns on both ethics and the user trust on machine learning techniques, especially in human-centered applications. In this work, we applied deep forest in HDsEMG-based hand gesture recognition. Deep forest is a new deep learning architecture where information could be processed layer-by-layer via cascaded random forest modules. Built on highly interpretable decision trees, deep forest models retain the interpretability of decision trees. With the inherent interpretability of deep forest, the most important features and electrodes that deep forest focused on in the decision making process can also be used to optimize the feature set and electrode configuration. We also compared the model interpretation results with the anatomy structure of forearm muscles, providing relations between the interpretations of deep forest and the physiological basis of neuromuscular systems. HDsEMG (256 electrodes) from 20 subjects were used in our analyses. To simulate realistic application scenarios, all analyses and evaluations were performed in inter-subject validations. Results showed that with optimized feature set and electrode configuration, the number of features/electrodes was substantially reduced, with the overall classification accuracy improved. Data and codes are available online.