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http://dx.doi.org/10.21288/resko.2017.11.4.371

Hand Gesture Recognition Regardless of Sensor Misplacement for Circular EMG Sensor Array System  

Joo, SeongSoo (한양대학교 생체의공학과)
Park, HoonKi (한양대학교병원 가정의학과)
Kim, InYoung (한양대학교 의공학교실)
Lee, JongShill (한양대학교 의공학연구소)
Publication Information
Journal of rehabilitation welfare engineering & assistive technology / v.11, no.4, 2017 , pp. 371-376 More about this Journal
Abstract
In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.
Keywords
Bio-Signal Processing; EMG; Pattern Classification; Machine Learning; PCA;
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Times Cited By KSCI : 1  (Citation Analysis)
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