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

ABSTRACT

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.

KEYWORDS:

• Electromyography
• EMG biofeedback
• physiotherapy
• rehabilitation
• rehabilitation engineering

Article highlights

• Electromyographic biofeedback (EMG-BF) has a significant role in providing real-time feedback for rehabilitation and enabling detailed analysis of muscle activation patterns, which is crucial for diagnosing and treating neuromuscular disorders.

• EMG-BF devices come in three types: PC-based, handheld, and mobile/tablet-based. They vary in the number of channels and reliability based on scientific research. EMG-BF has multiple clinical applications, including mental and physical rehabilitation, facial expression detection, and stress analysis.

• The SENIAM and ISEK promote standardized practices in sEMG through educational efforts and consensus papers, essential for ensuring reliable research and clinical applications. These efforts also aim to dispel misunderstandings about the scientific underpinnings of sEMG in physiotherapy.

• The review paper also highlighted the importance of good training on the clinical application of EMG and how to perform EMG biofeedback exercises. Universities and global initiatives are enhancing sEMG education and its integration into clinical practice. This addresses training gaps and promotes effective utilization of sEMG technology to enhance patient care.

• The AI applications on EMG biofeedback have been addressed and shown how machine and deep learning change the concept of EMG Biofeedback.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewers disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17434440.2024.2376699

Additional information

Funding

This paper was not funded.