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 a...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on neural systems and rehabilitation engineering Ročník 13; číslo 3; s. 280 - 291
Hlavní autori: Ajiboye, A.B., Weir, R.Fff
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.09.2005
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adult
Aged
Aged, 80 and over
Algorithms
Automatic control
Clustering
Clustering algorithms
Delay systems
electromyogram (EMG)
Electromyography
Electromyography - methods
Forearm - physiopathology
Fuzzy Logic
heuristics
Humans
Inference algorithms
Joint Prosthesis
Male
Movement
multifunctional control
Muscle Contraction
Muscle, Skeletal - physiopathology
myoelectric prostheses
Pattern recognition
Pattern Recognition, Automated - methods
Prostheses
Prosthesis Design
Prosthetics
Real time systems
Statistics
Studies
Therapy, Computer-Assisted - methods
ISSN:1534-4320, 1558-0210
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
content type line 23
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2005.847357