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Detail
Journal Article
On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor
- Authors
- Woosuk Kim, Myunggyu Kim
- Issue Date
- 2018-03
- Citation
- Sensors, v.18, no.3, pp.1-11
- ISSN
- 1424-8220
- Publisher
- MDPI AG
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Deep neural network(DNN), Motion Data, Motion analysis system, Novel approach, On-line detection, Wearable sensors, explicit segmentation, motion model
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
