Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features.

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ID: 44566
2019
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Abstract
In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players' behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) signals from a depth camera and from multiple inertial sensors are obtained and segmented, (ii) the time-variant skeleton vector projection from the depth camera and the statistical features extracted from the inertial sensors are used as features, and (iii) a deep learning-based scheme is proposed for training behavior classifiers. The experimental results demonstrate that the proposed deep learning behavior system achieves an accuracy of greater than 95% compared to the proposed dataset.
Reference Key
sun2019baseballsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sun, Shih-Wei;Mou, Ting-Chen;Fang, Chih-Chieh;Chang, Pao-Chi;Hua, Kai-Lung;Shih, Huang-Chia;
Journal sensors
Year 2019
DOI
E1425
URL
Keywords
Deep learning Machine learning multimodal inertial sensor behavior recognition lstm network depth camera

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