Transfer learning using low-dimensional subspaces for EMG-based classification of hand posture.

This study proposes a novel approach for evaluating the task invariance of muscle synergies, vital for potential implementation in improving prosthetic hand control. We do this by using a transfer learning paradigm to test for invariance across a relatively small set of hand/forearm muscle synergies, derived from electromyographic (EMG) activation patterns during voluntary behaviors such as finger spelling and grasp mimicking postures and unconstrained exploration. EMG for each task were decomposed using non-negative matrix factorization into synergy and weight matrices, and cross-task weights for each task were then reconstructed by employing the base matrices from different tasks. Support Vector Machine and Extreme Learning Machine classifiers were used to classify the resulting weights in order to compare their performance, as well as their behaviors as a function of synergy rank. Both algorithms showed robust and significantly higher performance, compared to two distinct randomized controls, with lower rank EMG representations, both within and between tasks/postures, supporting hypotheses of functional invariance of multi-muscle synergies. Our results suggest that this invariance could be leveraged to efficiently calibrate postures for prosthetic hand implementation by transferring learned EMG patterns from unconstrained movements to other tasks.