Wang C, Tang M, Xiao K, Wang D, Li B. Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors.
Prev Med Rep 2024;
41:102710. [PMID:
38576513 PMCID:
PMC10990899 DOI:
10.1016/j.pmedr.2024.102710]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/06/2024] Open
Abstract
Objectives
To enhance the daily training quality of athletes without inducing significant physiological fatigue, aiming to achieve a balance between training efficiency and load.
Design methods
Firstly, we developed an activity classification training model using the random forest algorithm and introduced the "effective training rate" (the ratio of effective activity time to total time) as a metric for assessing athlete training efficiency. Secondly, a method for rating athlete training load was established, involving qualitative and quantitative analyses of physiological fatigue through subjective fatigue scores and heart rate data. Lastly, an optimization system for training efficiency and load balance, utilizing multiple inertial sensors, was created. Athlete states were categorized into nine types based on the training load and efficiency ratings, with corresponding management recommendations provided.
Results
Overall, this study, combining a sports activity recognition model with a physiological fatigue assessment model, has developed a training efficiency and load balance optimization system with excellent performance. The results indicate that the prediction accuracy of the sports activity recognition model is as high as 94.70%. Additionally, the physiological fatigue assessment model, utilizing average relative heart rate and average RPE score as evaluation metrics, demonstrates a good overall fit, validating the feasibility of this model.
Conclusions
This study, based on relative heart rate and wearable devices to monitor athlete physiological fatigue, has developed a balanced optimization system for training efficiency and load. It provides a reference for athletes' physical health and fatigue levels, offering corresponding management recommendations for coaches and relevant professionals.
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