A Reinforcement Learning-Based Basketball Player Activity Recognition Method Using Multisensors

Author:

Bo Yang1ORCID

Affiliation:

1. University of Shanghai for Science and Technology, Shanghai 200093, China

Abstract

It is an effective means to use a computer auxiliary system to assist athletes in training. In this paper, we design a technical activity recognition system for basketball players. The system uses the sensing module bound to the basketball player to collect the activity data and uses the proposed Multilayer Parallel Long Short Term Memory (MP-LSTM) algorithm to recognize the activity. Moreover, in order to extend the working time of the system and reduce the energy consumption of the sensing module, we also utilize the classical reinforcement learning algorithm DQN to adaptively control the sampling frequency of the sensing module for making a trade-off between recognition accuracy and energy consumption. Experiment results show that the recognition accuracy of the proposed MP-LSTM algorithm reaches 94%, while the recognition accuracy of the system remains at about 90% after applying the DQN algorithm, and the energy consumption is reduced by 76%.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

Reference28 articles.

1. Basketball in America: a history;J. Furey,2017

2. The Use of Computer-Aided Teaching Software in the Teaching of Basketball Tactics

3. MieraisanM.Basketball game analyzing based on computer vision2013Tampere, FinlandTampere UniversityDoctorial dissertation

4. MCDA based approach to sports players’ evaluation under incomplete knowledge

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