Workout Detection by Wearable Device Data Using Machine Learning
There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4280 |
| _version_ | 1797608437331263488 |
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| author | Yutaka Yoshida Emi Yuda |
| author_facet | Yutaka Yoshida Emi Yuda |
| author_sort | Yutaka Yoshida |
| collection | DOAJ |
| description | There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy. |
| first_indexed | 2024-03-11T05:43:25Z |
| format | Article |
| id | doaj.art-fcb4099bd331445397bcf96b685a6d74 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:43:25Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fcb4099bd331445397bcf96b685a6d742023-11-17T16:18:06ZengMDPI AGApplied Sciences2076-34172023-03-01137428010.3390/app13074280Workout Detection by Wearable Device Data Using Machine LearningYutaka Yoshida0Emi Yuda1Center for Data-Driven Science and Artificial Intelligence, Tohoku University, Kawauchi 41, Aoba-ku, Sendai 980-8576, JapanCenter for Data-Driven Science and Artificial Intelligence, Tohoku University, Kawauchi 41, Aoba-ku, Sendai 980-8576, JapanThere are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy.https://www.mdpi.com/2076-3417/13/7/4280machine learningwearable sensorbiological informationworkout |
| spellingShingle | Yutaka Yoshida Emi Yuda Workout Detection by Wearable Device Data Using Machine Learning Applied Sciences machine learning wearable sensor biological information workout |
| title | Workout Detection by Wearable Device Data Using Machine Learning |
| title_full | Workout Detection by Wearable Device Data Using Machine Learning |
| title_fullStr | Workout Detection by Wearable Device Data Using Machine Learning |
| title_full_unstemmed | Workout Detection by Wearable Device Data Using Machine Learning |
| title_short | Workout Detection by Wearable Device Data Using Machine Learning |
| title_sort | workout detection by wearable device data using machine learning |
| topic | machine learning wearable sensor biological information workout |
| url | https://www.mdpi.com/2076-3417/13/7/4280 |
| work_keys_str_mv | AT yutakayoshida workoutdetectionbywearabledevicedatausingmachinelearning AT emiyuda workoutdetectionbywearabledevicedatausingmachinelearning |