Automated spatial localization of ankle muscle sites and model-based estimation of joint torque post-stroke via a wearable sensorised leg garment.

Abstract

Assessing a patient's musculoskeletal function during over-ground walking is a primary objective in post-stroke rehabilitation, due to the importance of walking recovery for everyday life. However, the quantitative assessment of musculoskeletal function currently requires lab-constrained equipment, and labor-intensive analyses, which hampers assessment in standard clinical settings. The development of fully wearable systems for the online estimation of muscle-tendon forces and resulting joint torque would aid clinical assessment of motor recovery, it would enhance the detection of neuro-muscular anomalies and it would consequently enable highly personalized treatments. Here, we present a wearable technology that combines (1) a soft garment for the human leg sensorized with 64 flexible and dry electromyography (EMG) electrodes, (2) a generalized and automated algorithm for the localization of leg muscle sites, and (3) an EMG-driven musculoskeletal modeling framework for the estimation of ankle dorsi-plantar flexion torques. Our results showed that the automated clustering algorithm could detect muscle locations in both healthy and post-stroke individuals. The estimated muscle-specific EMG envelopes could be used to drive forward person-specific musculoskeletal models and estimate resulting joint torques accurately across all healthy and post-stroke individuals and across different walking speeds (R2 >0.82 and RMSD <0.16). The technology we proposed opens new avenues for automated muscle localization and quantitative musculoskeletal function assessment during gait in both healthy and neurologically impaired individuals.