AUTHOR=Morsch Richard , Böckenförde Tim , Wolf Milan , Landgraeber Stefan , Strauss Daniel J. 

TITLE=Enhanced rehabilitation after total joint replacement using a wearable high-density surface electromyography system

JOURNAL=Frontiers in Rehabilitation Sciences

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2025.1657543

DOI=10.3389/fresc.2025.1657543

ISSN=2673-6861

ABSTRACT=IntroductionNeuromuscular recovery after total joint arthroplasty remains insufficiently understood, and current tools for assessing muscle function lack the resolution to monitor detailed recovery dynamics. High-Density surface Electromyography (HD-sEMG) enables spatiotemporal analysis of muscle activation and may support individualized rehabilitation. However, its clinical application in orthopedic settings remains limited.MethodsThis exploratory study presents a methodological framework for applying wearable 64-channel HD-sEMG system to monitor neuromuscular recovery in patients undergoing total knee or hip arthroplasty. HD-sEMG data were recorded during standardized mobilization exercises at multiple pre- and postoperative time points. A custom signal processing pipeline was developed, encompassing artifact suppression, dimensionality reduction, feature extraction, and the derivation of five functional indices summarizing key aspects of muscle performance.ResultsInitial clinical application demonstrated the feasibility of the approach. The functional indices revealed distinct recovery dynamics across patients and showed promising alignment with patient-reported outcome measures. Individual case analyses suggested the potential of HD-sEMG to differentiate between restitution and dysfunctional compensation patterns.DiscussionThis study provides a structured, exploratory foundation for longitudinal HD-sEMG research in orthopedic rehabilitation. While not yet suited for clinical decision-making, the proposed framework offers methodological tools for future investigations of neuromuscular recovery trajectories and may contribute to the development of personalized, data-driven rehabilitation strategies.