Time Series Prediction of Sit-To-Stand Muscle Synergy Using Deep Learning

Sit-to-stand (STS) motion is a complex behavior and requires a high power, especially when included in activities of daily living. For people with difficulties in performing this motion, assistive devices are particularly suitable. Although assistive devices should timely activate for providing assistance, they often exhibit multiple inherent delays. We introduce a control scheme intended to ensure proper timing for triggering assistive devices by predicting user’s STS motion intention. We employ electromyography and muscle synergy and design a deep neural network to predict motion intention. The proposed network is evaluated in terms of the accuracy and anticipation time of the predicted motion according to the forecast time considered during network training. The accuracy decreases with increasing forecast time. In addition, a longer forecast time for training increases the inferred anticipation time. Our results suggest that the control of assistive devices with proper timing and output power is feasible by implementing the proposed control scheme.