Effects of Electromyographic Biofeedback on Functional Recovery of Patients Two Months after Total Knee Arthroplasty: A Randomized Controlled Trial

Advancing clinical understanding of surface electromyography biofeedback: bridging research, teaching, and commercial applications

INTRODUCTION

Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research.

AREAS COVERED

This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors.

EXPERT OPINION

The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection.

Effects of Electromyographic Biofeedback-Assisted Exercise on Functional Recovery and Quality of Life in Patients after Total Hip Arthroplasty: A Randomized Controlled Trial

The goal of the trial was to examine the effects of adding electromyographic biofeedback (EMG-BF) to the conventional program of physiotherapy after total hip arthroplasty (THA) on functional recovery and quality of life. The trial was designed as a prospective, interventional, single-blinded randomized controlled study. Ninety patients were randomized into an experimental group (EG) (n = 45; mean age 63.9 ± 8.8) and control group (CG) (n = 45; mean age 63.9 ± 9). All patients received 21 days of physiotherapy which consisted of therapeutic exercise (land-based and aquatic), electrotherapy, and education. Electromyographic biofeedback was added to a portion of the land-based exercise in EG. The Hip Disability and Osteoarthritis Outcome Score (HOOS), Numeric Rating Scale (NRS), Short Form Health Survey-36 (SF-36), use of a walking aid, 30 s chair stand test (CST) as well as the Timed Up and Go (TUG) test were used for outcome measurement. A higher proportion of the participants in both groups did not need a walking aid after the intervention (p < 0.05). All participants improved their 30 s CST and TUG results (p < 0.001), as well as their NRS and HOOS scores (p < 0.05). No significant differences between the groups were found. There were no additional benefits from adding EMG-BF to the conventional physiotherapy protocol.