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Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features
2019
Sensors
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
doi:10.3390/s19061425
fatcat:dwb7acqdfzh77nbwqf7oosnzeq