کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10435202 | 910892 | 2005 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Classification of motor commands using a modified self-organising feature map
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی پزشکی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Medical Engineering & Physics - Volume 27, Issue 5, June 2005, Pages 403-413
Journal: Medical Engineering & Physics - Volume 27, Issue 5, June 2005, Pages 403-413
نویسندگان
F. Sebelius, L. Eriksson, H. Holmberg, A. Levinsson, G. Lundborg, N. Danielsen, J. Schouenborg, C. Balkenius, T. Laurell, L. Montelius,