Myoelectric control of robotic lower limb prostheses: A review of electromyography interfaces, control paradigms, challenges and future directions

Aaron Fleming, Nicole Stafford, Stephanie Huang, Xiaogang Hu, Daniel P. Ferris, He Helen Huang

Research output: Contribution to journalReview articlepeer-review

83 Scopus citations

Abstract

Objective. Advanced robotic lower limb prostheses are mainly controlled autonomously. Although the existing control can assist cyclic movements during locomotion of amputee users, the function of these modern devices is still limited due to the lack of neuromuscular control (i.e. control based on human efferent neural signals from the central nervous system to peripheral muscles for movement production). Neuromuscular control signals can be recorded from muscles, called electromyographic (EMG) or myoelectric signals. In fact, using EMG signals for robotic lower limb prostheses control has been an emerging research topic in the field for the past decade to address novel prosthesis functionality and adaptability to different environments and task contexts. The objective of this paper is to review robotic lower limb Prosthesis control via EMG signals recorded from residual muscles in individuals with lower limb amputations. Approach. We performed a literature review on surgical techniques for enhanced EMG interfaces, EMG sensors, decoding algorithms, and control paradigms for robotic lower limb prostheses. Main results. This review highlights the promise of EMG control for enabling new functionalities in robotic lower limb prostheses, as well as the existing challenges, knowledge gaps, and opportunities on this research topic from human motor control and clinical practice perspectives. Significance. This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.

Original languageEnglish (US)
Article number041004
JournalJournal of neural engineering
Volume18
Issue number4
DOIs
StatePublished - Aug 2021

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

Fingerprint

Dive into the research topics of 'Myoelectric control of robotic lower limb prostheses: A review of electromyography interfaces, control paradigms, challenges and future directions'. Together they form a unique fingerprint.

Cite this