A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control.
This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.
Ajiboye
Abidemi Bolu
AB
Department of Biomedical Engineering, Rehabilitation Engineering Research Center and Prosthetic Research Laboratory, Northwestern University, Chicago, IL 60611, USA. a-ajiboye@northwestern.edu
Weir
Richard F ff
RF
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005542:Forearm / Q000503:physiopathology*; D017143:Fuzzy Logic; D006801:Humans; D007595:Joint Prosthesis; D008297:Male; D009068:Movement; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011474:Prosthesis Design; D013813:Therapy, Computer-Assisted / Q000379:methods*