Robust motion discrimination based on human forearm myoelectric potential by adaptive fuzzy inference considering muscle fatigue

Kiso, Atsushi; Seki, Hiroko

doi:10.1109/iembs.2009.5332527

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…This study uses the fuzzy inference for the motion discrimination based on the myoelectric signal measured from the selected optimal measurement position [16]. A flow of the motion discrimination system process appears in Fig.7.…”

Section: A Summarymentioning

confidence: 99%

Optimal measurement position estimation of EMG signal by multi-regression analysis for human forearm motion discrimination

Kiso

Seki

2010

2010 3rd IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics

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This paper describes an optimal measurement position estimation of the myoelectric signal by the multiple regression analysis for human forearm motion discrimination. The conventional studies use a lot of myoelectric sensors to obtain the high discrimination precision from the myoelectric signal. However, the use of a lot of myoelectric sensors becomes difficult by the cost and amputating situation of the human forearm. The purpose of this study is to decide the optimal measurement position for the motion discrimination, and to obtain high discrimination precision of the human forearm motion. This study proposes the selection method of the optimal measurement position to estimate the identification target motion from the myoelectric signal measured from multiple positions by the multiple regression analysis. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position selection method.

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Section: A Summarymentioning

confidence: 99%

Optimal measurement position estimation of EMG signal by multi-regression analysis for human forearm motion discrimination

Kiso

Seki

2010

2010 3rd IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics

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Motion discrimination technique by EMG signals using hyper-sphere model

Yamamoto

Tsujiuchi

Ito

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This study developed a method of discriminating real-time motion from electromyogram (EMG) signals. We previously proposed a real-time motion discrimination method using hyper-sphere models that discriminated five motions (open, grasp, pinching, wrist extension, and wrist flexion) above 90% and quickly learned EMG signals. Our method prevents elbow motions from interfering with hand motion discrimination. However, we presume in our method that feature quantities do not change with time. Discrimination accuracy might deteriorate over time. Additionally, our method only discriminated three motions (open, grasp, pinching) for finger motions. This paper proposes the effectiveness of our method for changing feature quantities caused by time variation and a real-time motion discrimination method using new hyper-sphere models for four finger motions (open, grasp, pinching, and 2-5th finger flexion). We carried out two experiments and verified the effectiveness of our method for changing feature quantities and four finger motions discrimination using the new hyper-sphere models.

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Optimal measurement position estimation by discriminant analysis based on Wilks' lambda for myoelectric hand control

Kiso

Taniguchi

Seki

2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper describes an optimal measurement position estimation by the discriminant analysis based on Wilks' lambda for the myoelectric hand control. In the past studies, the myoelectric signals were measured from the same positions for the motions discrimination. However, the optimal measurement positions of the myoelectric signals for the motion discrimination are different according to the remaining muscle situation of amputees. Therefore the purpose of this study is to estimate the optimal and fewer measurement positions for the precise motion discrimination of the human forearm. This study proposes the estimation method of the optimal measurement positions by the discriminant analysis based on Wilks' lambda among the myoelectric signal measured from multiple positions. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

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Robust motion discrimination based on human forearm myoelectric potential by adaptive fuzzy inference considering muscle fatigue

Cited by 4 publications

References 13 publications

Optimal measurement position estimation of EMG signal by multi-regression analysis for human forearm motion discrimination

Optimal measurement position estimation of EMG signal by multi-regression analysis for human forearm motion discrimination

Motion discrimination technique by EMG signals using hyper-sphere model

Optimal measurement position estimation by discriminant analysis based on Wilks' lambda for myoelectric hand control

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