Classification of motor commands using a modified self-organising feature map.
Författare
Summary, in English
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.
Avdelning/ar
Publiceringsår
2005
Språk
Engelska
Sidor
403-413
Publikation/Tidskrift/Serie
Medical Engineering & Physics
Volym
27
Issue
5
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Environmental Health and Occupational Health
- Surgery
Status
Published
Forskningsgrupp
- Radiology Diagnostics, Malmö
- Hand Surgery, Malmö
- Neural Interfaces
- Neuronano Research Center (NRC)
- Neurophysiology
ISBN/ISSN/Övrigt
- ISSN: 1873-4030