Intent Recognition in Gait Transition Using Muscle Volume Sensors with Deep Learning

Abstract

Intention recognition is essential for wearable robotics and assistive systems. However, conventional approaches often suffer from cumbersome sensor setups or sensitivity to external disturbances. To address these limitations, this study proposes an LSTM-based intention recognition method using lower-limb Muscle-Volume (MV) sensors. An insole-type pressure sensor, an IMU sensor, and a cuff-type MV sensor were used to record a series of motions, including sitting, standing, walking, and running. Deep learning techniques were then applied for classification and transition detection. Accuracies of the predicted movement states based on data from the IMU, insole-type pressure, and cuff-type MV sensors were 93.04%, 97.65%, and 93.08%, respectively. The average transition detection latencies for the IMU, insole, and MV sensor model were 0.135 s, 0.377 s, and 0.455 s, respectively. Results show that the proposed MV sensor achieves performance comparable to insole pressure sensors, demonstrating its potential as a practical and robust alternative for intention recognition in wearable systems.