Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models
Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes’ well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum c...
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/3251 |
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| author | Vaishali Balakarthikeyan Rohan Jais Sricharan Vijayarangan Preejith Sreelatha Premkumar Mohanasankar Sivaprakasam |
| author_facet | Vaishali Balakarthikeyan Rohan Jais Sricharan Vijayarangan Preejith Sreelatha Premkumar Mohanasankar Sivaprakasam |
| author_sort | Vaishali Balakarthikeyan |
| collection | DOAJ |
| description | Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes’ well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model’s accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors. |
| first_indexed | 2024-03-11T05:55:26Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:55:26Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-88d5c6ad719d404ab8d7b52ec49b26682023-11-17T13:48:05ZengMDPI AGSensors1424-82202023-03-01236325110.3390/s23063251Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression ModelsVaishali Balakarthikeyan0Rohan Jais1Sricharan Vijayarangan2Preejith Sreelatha Premkumar3Mohanasankar Sivaprakasam4Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, IndiaHealthcare Technology Innovation Centre (HTIC), Chennai 600113, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, IndiaWearable Heart Rate monitors are used in sports to provide physiological insights into athletes’ well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model’s accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.https://www.mdpi.com/1424-8220/23/6/3251wearable heart rate monitorsheart rateheart rate variabilitycardiorespiratory fitnessmachine learning |
| spellingShingle | Vaishali Balakarthikeyan Rohan Jais Sricharan Vijayarangan Preejith Sreelatha Premkumar Mohanasankar Sivaprakasam Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models Sensors wearable heart rate monitors heart rate heart rate variability cardiorespiratory fitness machine learning |
| title | Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models |
| title_full | Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models |
| title_fullStr | Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models |
| title_full_unstemmed | Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models |
| title_short | Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models |
| title_sort | heart rate variability based estimation of maximal oxygen uptake in athletes using supervised regression models |
| topic | wearable heart rate monitors heart rate heart rate variability cardiorespiratory fitness machine learning |
| url | https://www.mdpi.com/1424-8220/23/6/3251 |
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