基于传感器的自行车组训练优化

A. Le, T. Jaitner, L. Litz
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引用次数: 10

摘要

确定最佳运动强度是提高职业自行车运动成绩的关键因素。新的传感器技术允许优化训练,不仅为个人自行车手,也为整个训练组。德国凯泽斯劳滕大学开发了一种基于传感器和控制算法的辅助自行车训练器(ABT)系统,以优化自行车群体训练。本文的重点是开发一种用于群训练优化的模型预测控制器(MPC)控制算法。控制器根据个人心率模型预测骑车者的心率,并通过建议骑车者改变小组位置、调整小组速度或将小组分开,以使每个骑车者都能尽可能准确地完成他的训练计划,来调节小组训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor-based Training Optimization of a Cyclist Group
Determining the optimal exercise intensity is a crucial factor to increase performance in professional cycling. Novel sensor technologies allow to optimize the training not only for an individual cyclist but also for an entire training group. A sensor-based assisted bicycle trainer (ABT) system with a control algorithm has been developed at the University of Kaiserslautern to optimize the group training in cycling. The focus of this paper is on the development of the control algorithm, a model predictive controller (MPC) for the optimization of the group training. The controller predicts the heart rate of the cyclists based on individualized heart rate models and regulates the group training by advising cyclists to change the position in the group, to adjust the group speed, or to split the group in such a way that each cyclist can meet his training plan as exactly as possible.
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