Data-driven Gait-predictive Model for Anticipatory Prosthesis Control

Physiological movement is pre-planned based on current movement state, proprioception, and environmental cues. This preplanning is necessary to allow efficient use of the viscoelastic properties of musculoskeletal tissues in a 4D-environment. Similarly, efficient use of prosthetic devices needs to compensate for the time it takes to control the system. In this study, we propose a gated recurrent net-based gait predictive model to continuously predict the ankle angles and moments fifty milliseconds in advance based on the past trajectory of the input signals. It was observed that using a single input signal (the shank angle), high accuracy of prediction $(R^{2}\gt 0.91)$ was achieved for both ankle angle and moments on walking trials at a self-selected comfortable speed. The results of our study can be utilised for anticipatory lower-limb prosthesis control where embedded sensor information that reflects a prosthetic user's locomotive intent can be used to predict the required angles and moments in advance for actuating a prosthetic joint.