Simonetti D, Hendriks M, Herijgers J, Cuerdo Del Rio C, Koopman B, Keijsers N, Sartori M. Automated spatial localization of ankle muscle sites and model-based estimation of joint torque post-stroke via a wearable sensorised leg garment.
J Electromyogr Kinesiol 2023;
72:102808. [PMID:
37573851 DOI:
10.1016/j.jelekin.2023.102808]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/07/2023] [Accepted: 08/01/2023] [Indexed: 08/15/2023] Open
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.
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