Sit-to-stand and stand-to-sit motions are important in daily activities. However, elderly individuals often find these motions difficult to perform with declining lower limb strength, which causes a considerable reduction to their quality of life. In this study, a sensing method for controlling robotic assistive devices was proposed. This method utilizes electromyographic measurements and a deep neural network to predict motion initiation, and it estimates the timing of triggering assistive devices. Experimental results indicate that four muscle synergy patterns are required to represent the sit-to-stand and stand-to-sit motions together, with two of them being shared between both movements. Subsequently, a long short-term memory network was designed to forecast these two motions, and the result indicates that the prediction accuracy reached 92.95% ± 0.83% with forecasting time of 300 ms.

This paper introduces a novel form of communication via a combination of muscle sensing by electromyography and stimulation via a skin-stretcher device as a motion monitoring system. After sensing muscle activity through electromyography, the skin-stretcher device provides a skin sensation that confidentially informs or induces movements of the user who wears the device. This paper also introduces methods for translating muscle activities to the skin-stretch sensations, and additional filtering to improve the performance. In this study, we conducted preliminary experiments that demonstrate the potential of our system design.