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Trajectory planning in college football training using deep learning and the internet of things

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Abstract

The research is conducted to improve the efficiency of college football training. The long short-term memory (LSTM) + genetic algorithm (GA) is proposed based on deep learning and internet of things (IoT) intelligent wearable devices. Specifically, the LSTM makes up for the weak local searchability of GA. The research idea is to use LSTM–GA to train and simulate the network model. The experimental result reads: the LSTM–GA model converges at the 101st iteration. The model fitness is about 11% higher than GA and about 2% higher than the LSTM. Thus, the proposed LSTM–GA can plan football players' trajectory to score. Therefore, players can wear IoT-ready intelligent wearable devices to plan the optimal shooting trajectory. The research contribution is to optimize the LSTM–GA model of IoT-ready intelligent wearable devices. The proposal can improve the efficiency of college football training.

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Authors and Affiliations

  1. Physical Education College, Shangrao Normal University, Shangrao, 334001, China

    Yingrong Guan, Yaoyu Qiu & Cheng Tian

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  1. Yingrong Guan
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Correspondence to Yingrong Guan.

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Guan, Y., Qiu, Y. & Tian, C. Trajectory planning in college football training using deep learning and the internet of things. J Supercomput 78, 18616–18635 (2022). https://doi.org/10.1007/s11227-022-04619-9

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