Classification of motor commands using a modified self-organising feature map.
In this paper, a control system for an advanced prosthesis is proposed and has been investigated in two different biological systems: (1) the spinal withdrawal reflex system of a rat and (2) voluntary movements in two human males: one normal subject and one subject with a traumatic hand amputation. The small-animal system was used as a model system to test different processing methods for the prosthetic control system. The best methods were then validated in the human set-up. The recorded EMGs were classified using different ANN algorithms, and it was found that a modified self-organising feature map (SOFM) composed of a combination of a Kohonen network and the conscience mechanism algorithm (KNC) was superior in performance to the reference networks (e.g. multi-layer perceptrons) as regards training time, low memory consumption, and simplicity in finding optimal training parameters and architecture. The KNC network classified both experimental set-ups with high accuracy, including five movements for the animal set-up and seven for the human set-up.
Sebelius
F
F
Department of Electrical Measurements, Lund Institute of Technology, Lund, Sweden.
Eriksson
L
L
Holmberg
H
H
Levinsson
A
A
Lundborg
G
G
Danielsen
N
N
Schouenborg
J
J
Balkenius
C
C
Laurell
T
T
Montelius
L
L
D000328:Adult; D000465:Algorithms; D000671:Amputation, Surgical; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D004576:Electromyography; D006801:Humans; D008297:Male; D008377:Maps as Topic; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011474:Prosthesis Design / Q000379:methods*; D051381:Rats; D017208:Rats, Wistar; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors