Record number :
1730940
Title of article :
Real-time pinch force estimation by surface electromyography using an artificial neural network
Author/Authors :
Choi، نويسنده , , Changmok and Kwon، نويسنده , , Suncheol and Park، نويسنده , , Wonil and Lee، نويسنده , , Hae-dong and Kim، نويسنده , , Jung، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2010
Pages :
8
From page :
429
To page :
436
Abstract :
The palmar pinch force estimation is highly relevant not only in biomechanical studies, the analysis of sports activities, and ergonomic design analyses but also in clinical applications such as rehabilitation, in which information about muscle forces influences the physicianʹs decisions on diagnosis and treatment. Force transducers have been used for such purposes, but they are restricted to grasping points and inevitably interfere with the human haptic sense because fingers cannot directly touch the environmental surface. We propose an estimation method of the palmar pinch force using surface electromyography (SEMG). Three myoelectric sites on the skin were selected on the basis of anatomical considerations and a Fisher discriminant analysis (FDA), and SEMG at these sites yields suitable information for pinch force estimation. An artificial neural network (ANN) was implemented to map the SEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using SEMG signals recorded from the selected myoelectric sites of ten subjects in real time. The training time for each subject was short (approximately 96 s), and the estimation results were promising, with a normalized root mean squared error (NRMSE) of 0.081 ± 0.023 and a correlation (CORR) of 0.968 ± 0.017.
Keywords :
Artificial neural network (ANN) , Surface electromyography (SEMG) , Pinch force estimation
Journal title :
Medical Engineering and Physics
Journal title :
Medical Engineering and Physics
Serial Year :
2010
Link To Document :
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