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.
Highlights
Analyzing quality of motion is essential for evaluating the performance of athlete’s movements in sports [1]
Each motion sample from the test sets was separately fed into the detection and segmentation process as if it were an on-line data stream
We presented a method to detect and segment including sports motions using a wearable sensor
Summary
Analyzing quality of motion is essential for evaluating the performance of athlete’s movements in sports [1]. The analysis begins by observation; sports coaches or teachers need to know how movements are carried out before judging quality. In the sports science literature, systematic models have been introduced as aids to observation [2,3]. Terms and definitions may vary, it is common that a motion is perceived as a sequential pattern of movements and observation is thought of as a task to inspect spatio-temporal characteristics of the pattern in analysis of sports motions. Technologies have already been adopted for helping observation of sports motions [4]. Motion capture of the human body using multiple depth sensors.
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