A Recurrent Neural Network Provides Stable Across-Day Prosthetic Control for a Human Amputee with Implanted Intramuscular Electromyographic Recording Leads.

Abstract

Upper-limb prosthetic control is often challenging and non-intuitive, leading to up to 50% of prostheses users abandoning their prostheses. Convolutional neural networks (CNN) and recurrent long short-term memory (LSTM) networks have shown promise in extracting high-degree-of-freedom motor intent from myoelectric signals, thereby providing more intuitive and dexterous prosthetic control. An important next consideration for these algorithms is if performance remains stable over multiple days. Here we introduce a new LSTM network and compare its performance to previously established state-of-the-art algorithms-a CNN and a modified Kalman filter (MKF)-in offline analyses using 76 days of intramuscular recordings from one amputee participant collected over 425 calendar days. Specifically, we assessed the robustness of each algorithm over time by training on data from the first (one, five, ten, 30, or 60) days and then testing on myoelectric signals on the last 16 days. Results indicate that training on additional datasets from prior days generally decreases the Root Mean Squared Error (RMSE) of intended and unintended movements for all algorithms. Across all algorithms trained with 60 days of data, the lowest RMSE for unintended movements was achieved with the LSTM. The LSTM also showed less across-day variance in RMSE of unintended movements relative to the other algorithms. Altogether this work suggests that the LSTM algorithm introduced here can provide more intuitive and dexterous control for prosthetic users, and that training on multiple days of data improves overall performance on subsequent days, at least for offline analyses.