# Workout Detection by Wearable Device Data Using Machine Learning
## 4 Discussion  
Improving physical activity is an effective means for promoting health. In this study, we clarified that it is possible to detect workout time with a high accuracy, approximately 96.2%, under daily activities by RF and SVM using data collected from wristband wearable sensors without using existing applications. The effective early detection of diseases, care prevention, and health promotion programs are expected to be provided through the use of wearable devices and data. The effectiveness of wearable devices has already been verified in gait support for Parkinson's disease patients, rehabilitation for chronic stroke patients, and care for chronic diseases [21]. These directly contribute to the improvement of human quality of life (QOL). Although prospective comparative studies using machine learning for clinical evaluation of wearable sensor data are emerging [22], there are only a few studies that identify the workout. As the prevention of age-related frailty is a universal issue, the detection and prediction of exercise duration in long-term monitoring data will become important in the future. Conventional monitoring of training load has been mathematically complex, but simpler methods, such as the evaluation of derived variables, have been demonstrated [23]. In addition, continuously measured physical activity data using smartwatches has become widely applied to everything from the effects of gait training on functional performance to swimming motion solution [24]. However, there are still few studies to identify workout time in a simple and accurate manner, and this study has the potential to be applied to the objective evaluation of behavioral change and rhythm disorders. In previous research, [PERSON] et al. (2021) reported on wristband-type wearable sensors such as the Fitbit Charge 2 and Fitbit Alta (Fitbit Inc., San Francisco, CA, USA), and Apple Watch 2 (Apple Inc., Cupertino, CA, USA). Moderate-to-vigorous physical activity (MVPA) could not be detected [25]. In a consumer activity meter accuracy evaluation, 48 subjects wore a Yamax pedometer (Yamasa Tokei Keiki Co., Ltd., Tokyo, Japan), an ActiGraph GT3X+ (ActiGraph LLC, Pensacola, FL, USA), and a Polar H7 (Polar Electro Oy, Kempele, Finland) chest strap at the same time, and the number of steps taken, heart rate, and acceleration data under 24-hour daily activities were acquired and compared. As a result of evaluating the degree of agreement of the measurements, the number of steps taken and heart rate could be estimated reasonably, while the estimation of MVPA had a mean absolute error rate ranging from 45% to a maximum of 90%, suggesting that MVPA was significantly underestimated. In addition, there were variations in the accuracy of thresholds and algorithms for estimating active time and MVPA from biological information obtained from wearable sensors, and it was noted that it is necessary to continue to evaluate the effectiveness of devices. The machine learning algorithm in this study adopted k-fold cross-validation (k = 4) and used 2562 min out of 3416 min of long-term data for training data. In addition, it is specialized machine learning that detects one's own behaviors from one's own learning data, and it is thought that the detection accuracy of not only Workout but also Awake can improve because the characteristics and habits of one's own behaviors are reflected in learning. Wearable sensors are usually used by a single person to estimate the health, sleep, activity, and other conditions of that person, and we expect to compute predictions specific to the characteristics of a single person based on biological information. Additionally, this study used nine types of feature values for machine learning. The sleep state is an index that determines whether the subject is asleep or awake; it is evaluated in two stages and calculated using three-axis acceleration.