Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG.

We anticipate wide adoption of wrist and forearm electomyographic (EMG) interface devices worn daily by the same user. This presents unique challenges that are not yet well addressed in the EMG literature, such as adapting for session-specific differences while learning a longer-term model of the specific user. In this manuscript we present two contributions toward this goal. First, we present the MiSDIREKt (Multi-Session Dynamic Interaction Recordings of EMG and Kinematics) dataset acquired using a novel hardware design. A single participant performed four kinds of hand interaction tasks in virtual reality for 43 distinct sessions over 12 days, totaling 814 min. Second, we analyze this data using a non-linear encoder-decoder for dimensionality reduction in gesture classification. We find that an architecture which recalibrates with a small amount of single session data performs at an accuracy of 79.5% on that session, as opposed to architectures which learn solely from the single session (49.6%) or learn only from the training data (55.2%).