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Open AccessArticle

On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor

Creative Content Research Division, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
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This paper is an extended version of Kim, W. and Kim, M. Sports motion analysis system using wearable sensors and video cameras. In Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea, 18–20 October 2017.
Sensors 2018, 18(3), 913; https://doi.org/10.3390/s18030913
Received: 14 February 2018 / Revised: 16 March 2018 / Accepted: 17 March 2018 / Published: 19 March 2018
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. View Full-Text
Keywords: sports motion; detection; segmentation; wearable sensor; deep neural networks sports motion; detection; segmentation; wearable sensor; deep neural networks
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Kim, W.; Kim, M. On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor. Sensors 2018, 18, 913.

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