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

Donatella Simonetti*, Maartje Hendriks, Joost Herijgers , Carmen Cuerdo del Rio, H.F.J.M. Koopman, Noel Keijsers, M. Sartori

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.
Original languageEnglish
Article number102808
Number of pages10
JournalJournal of electromyography and kinesiology
Volume72
Early online date7 Aug 2023
DOIs
Publication statusPublished - Oct 2023

Keywords

  • UT-Hybrid-D

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