Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors
© 2024 The Authors..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:41 |
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| Enthalten in: |
Preventive medicine reports - 41(2024) vom: 11. Apr., Seite 102710 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Chen [VerfasserIn] |
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| Links: |
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| Themen: |
Balance optimization system |
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| Anmerkungen: |
Date Revised 06.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.pmedr.2024.102710 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370659260 |
|---|
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| 100 | 1 | |a Wang, Chen |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors |
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| 500 | |a Date Revised 06.04.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 The Authors. | ||
| 520 | |a Objectives: To enhance the daily training quality of athletes without inducing significant physiological fatigue, aiming to achieve a balance between training efficiency and load | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Balance optimization system | |
| 650 | 4 | |a Inertial sensors | |
| 650 | 4 | |a Physiological fatigue | |
| 650 | 4 | |a Random forest algorithm | |
| 650 | 4 | |a Training efficiency and load | |
| 700 | 1 | |a Tang, Man |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Kun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Defa |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Bin |e verfasserin |4 aut | |
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