Development of an Evaluation Method for Controlling Skill of a Myoelectric Control Hand

Hara, Yoshiaki; Matsubara, Hideaki; Shibata, Yaeko; Mizobe, Futoshi; Fukazawa, Yoshihiro; Okamoto, Makiko; Chin, Takaaki; Minato, Kotaro; Yoshida, Masaki

doi:10.1541/ieejeiss.128.1257

Cited by 2 publications

References 7 publications

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Evaluation method for the proficiency level of an operating myoelectric hand using EMG signals

Kita

Kato

Yokoi

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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To evaluate the proficiency level of an operating myoelectric hand, we proposed an evaluation index consisting of the accuracy and the reproducibility of electromyography (EMG) signal patterns. Our proposed method is not an absolute evaluation because we use bio-signals, so it is necessary to verify the correlation between the proposed index and performance evaluation to confirm the usefulness of the index. Therefore, we conducted classification tests on eight forearm motions and verified the correlation between the proposed method and the classification rate. There was a strong correlation between the accuracy and the classification rate. In addition, if the accuracy was high, high reproducibility led to an increase in the classification rate. We conclude that the proposed method can evaluate the proficiency level of a myoelectric hand.

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Evaluation method for the proficiency level of an operating myoelectric hand using EMG signals

Kita

Kato

Yokoi

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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

Kita¹,

Kato²,

Yokoi³

2010

JRSJ

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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.

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Development of an Evaluation Method for Controlling Skill of a Myoelectric Control Hand

Cited by 2 publications

References 7 publications

Evaluation method for the proficiency level of an operating myoelectric hand using EMG signals

Evaluation method for the proficiency level of an operating myoelectric hand using EMG signals

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

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