# Workout Detection by Wearable Device Data Using Machine Learning
###### Abstract  
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.  
machine learning; wearable sensor; biological information; workout +
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drug efficacy, and so on. Additionally, practical applications such as electrocardiograms, pulse waves, and body acceleration obtained from wearable sensors are used. There is a need for rapid and simple analysis and evaluation methods for living organisms. In general, biometric data recorded by wearable devices are managed and analyzed by smartphone applications, and individual psychosomatic state characteristics are evaluated. Wearable sensors on the market mainly manage data and stream real-time data by directly integrating sensors and applications, and users need to use specific applications.  
These applications estimate calorie consumption based on personal information such as a person's height, weight, sex, and age. The automatic detection of the presence or absence of workouts is often based on the time, duration, and intensity of activities. Applications are personalized and provide guidance for incorporating workouts into one's daily lifestyle based on personal exercise patterns and trends. In contrast, the workout detection algorithm is usually a black box and has the drawback that common and frequent workouts cannot be detected. For example, in the case of a wristband-type sensor, we have confirmed from preliminary experiments that it is difficult to detect aerobic exercises without wrist movement, such as cycling. Therefore, there is still little research on how well a workout can actually be detected by analyzing biological information obtained from sensors.  
Therefore, we investigated whether it is possible to detect workouts by applying machine learning to pulse, activity level, and skin temperature data obtained from wearable sensors in daily life.