Deep reinforcement learning for improving competitive cycling performance

作者：

Highlights：

• Sensory information from bike rides is used to predict speed and heart rate.

• The predictive models are used as a simulator of new bike rides.

• We use the simulator to train a recommendation system about a cyclist’s posture.

• The system’s sparse suggestions improve the speed with a minimal impact on heart rate

摘要

•Sensory information from bike rides is used to predict speed and heart rate.•The predictive models are used as a simulator of new bike rides.•We use the simulator to train a recommendation system about a cyclist’s posture.•The system’s sparse suggestions improve the speed with a minimal impact on heart rate

论文关键词：Reinforcement learning,Recommendation systems,Competitive cycling

论文评审过程：Received 30 December 2021, Revised 22 March 2022, Accepted 23 April 2022, Available online 5 May 2022, Version of Record 11 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117311