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

Sensors (Basel). 2019 Mar 22;19(6):1425. doi: 10.3390/s19061425.


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.

Keywords: LSTM network; behavior recognition; deep learning; depth camera; inertial sensor; machine learning; multimodal.

MeSH terms

  • Accelerometry / instrumentation
  • Accelerometry / methods*
  • Baseball
  • Behavior / physiology*
  • Deep Learning*
  • Humans
  • Joints / physiology
  • Memory, Long-Term
  • Memory, Short-Term
  • Photography
  • Wearable Electronic Devices