The objective of the study is to develop an EMG measurement system aimed to control an active assisted device that is used by a subject whose upper limb is paralyzed as Erb’s palsy. The EMG measurement circuit with eight channel differential amplifiers, rectifier and filter and the software that discriminate the motion were developed. For discriminant method, nearest neighbor algorithm using Euclidean (EUC) and Mahalanobis (MH) distances, and support vector machine (SVM) were used. The EMG by six motions with radial and ulnar flexion, extension-flexion of wrist were and open-close movement of fingers were measured and evaluated by 4, 6 and 8 channels. The discriminant ratio was lowest by four channels; however the result of six channels was almost same as that of eight when the appropriate channels were selected. 100% discriminant ratio can be obtained by choosing the largest number of distinguished motion using SVM and EUC methods.
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