A Review on Upper-Limb Myoelectric Prosthetic Control

ABSTRACT

One of the most interesting topics in the field of rehabilitation is that of upper-limb myoelectric prosthetic control. It is a technique by which prostheses are controlled by means of surface electromyogram (sEMG) signals collected from remnant muscle tissues at the residual limb of an amputee. Intuitive control of multifunctional upper-limb prosthesis can be accomplished using pattern recognition (PR) of sEMG signals. In spite of the tremendous progress made in the research of the so-called mind-controlled artificial arm, none of the academic achievements has yet reached the end users. This review paper portrays the current state-of-the-art approach in sEMG pattern classification-based control, identifies the factors that hinder the clinical usability of the system and focuses on the recent research directions toward translating the academic findings into a commercially acceptable robust myoelectric prosthesis. Control strategies proposed for simultaneous and proportional control (SPC) of multiple degrees of freedom (DoFs), which is identified as the most significant barrier for the transition from laboratory to clinical practice, are discussed. Directions for future research are also briefly outlined.

KEYWORDS:

• Classification
• Degrees of freedom (DoFs)
• Feature extraction
• Myoelectric
• Electromyogram (EMG)
• Prosthetic control
• Pattern recognition
• Simultaneous and proportional control
• Upper-limb amputation

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Nisheena V. Iqbal

Nisheena V. Iqbal is currently pursuing her PhD degree in electronics and communication engineering at Karpagam University, Coimbatore, India, and working as associate professor in MES College of Engineering, Kuttippuram, India. Her research interests include biomedical signal processing.

E-mail: [email protected]

Kamalraj Subramaniam

Kamalraj Subramaniam received his PhD degree in mechatronic engineering from University Malaysia Perlis, Perlis, Malaysia in 2014. Currently, he is an associate professor and deputy head (human machine interface cluster) Karpagam University, Coimbatore, India. His¸ research interests include biomedical signal processing, artificial neural networks, VLSI design.

E-mail: [email protected]

Shaniba Asmi P.

Shaniba Asmi P. is currently pursuing her PhD degree in electronics and communication engineering at Karpagam University, Coimbatore, India, and working as an associate professor in MES College of Engineering, Kuttippuram, India. Her research interests include biomedical signal processing.

E-mail: [email protected]