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학술지 On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor
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저자
김우석, 김명규
발행일
201803
출처
Sensors, v.18 no.3, pp.1-11
ISSN
1424-8220
출판사
MDPI AG
DOI
https://dx.doi.org/10.3390/s18030913
초록
In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.
키워드
Deep neural networks, Detection, Segmentation, Sports motion, Wearable sensor
KSP 제안 키워드
Deep neural network(DNN), Motion Data, Motion analysis system, Novel approach, On-line detection, Wearable sensors, explicit segmentation, motion model
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