Park Myoung Soo Oh, Sang Rok 2024-01-12T15:06:05Z 2024-01-12T15:06:05Z 2021-09-29 2015-09 2153-0858 https://pubs.kist.re.kr/handle/201004/90007 In this paper, a novel sEMG decoder was proposed for estimating the detailed human intentions. For the use in practical applications, an sEMG decoder needs to have a high decoding performance, a robustness to the changes of electrode positions, and a capability to extract detailed and continuous intentions behind human motions. The new decoder satisfies all these needs by using a new supervised feature extractor, which was originally designed for discrete output values but modified to deal with the continuous values while maintaining the high performance and the robustness to the change of input variables resulting from the change of electrode positions. Experiments for decoding detailed human intentions, i.e. the angle between phalanges in a single finger, confirms that the high performance of the proposed decoder and that this performance is maintained when the electrode position changes. English IEEE Recognition Human-Computer Interaction Aritificial Intelligence Machine Learning sEMG-based Decoding of Detailed Human Intentions from Finger-level Hand Motions Conference 1 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.4271 - 4276 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 4271 4276 US Hamburg, GERMANY 2015-09-28 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) 000371885404068