Forearm functional movement recognition using spare channel surface electromyography

Myoelectric signal analysis provides insight into neural control during muscle contraction and it has been widely used to identify the intention of performing different movements for patients with disabilities. Previous studies have demonstrated that detailed neural control information could be extracted from high-density surface electromyography (EMG) signals. However, this imposes practical constraints for routine applications. In this paper, we present an analysis framework using low-density EMG with example experiments demonstrating the control of forearm functional movement Eight channel surface EMG signals are used with subjects performing 6 different forearm and hand movements. Data analysis consisting of feature selection and pattern classification based on KNN, linear discriminant analysis and support vector machine is then performed. High classification accuracy has been achieved for all the subjects, illustrating the practical value of the method proposed.

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