EMG-to-Motion Classification for Prosthetic Applications-A Self-Organizing Approach with Level of Proficiency-

Abstract

The purpose of this research is to discriminate many motions using EMG signal for prosthetic application's control. The basic idea of this study is that, the current degree of proficiency, i.e., how well the application is operated by a user is to be analyzed from bio-signal closely related to human motor process, and control rule is generated depending on the proficiency level. Thus, our proposed system consists of two parts: estimation of user's proficiency level using EMG signal, and motion classification using self-organized clustering. In the experiment, users trained to discriminate motions using our proposed system, and then they all were able to discriminate seven forearm motions with approximately 90% accuracy. In addition, one of the users was able to discriminate nine motions with 21% higher accuracy than before training. The results indicated the effectiveness of proposed system.

Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals

“…On the other hand, (Nakaya et al, 2010) proposed a hand gesture identification method and a distinction method of any singularity in the identified hand gesture on the basis of the SEMG measurements. (Kita et al, 2010) proposed a self-organizing approach with level of proficiency to perform stable classification of operation. (Tada et al, 2006) proposed a distinction method of unusual manipulation of a driver when driving an automobile, using the degree of deviation on the basis of the acceleration measurements.…”