Predicting the types of physical activities using data from apple watch and fitbit devices based on machine learning (ML) methods

Apart from their communication and multimedia features, wearable health devices are highly practical for monitoring health status. The demand for wearable technologies and medical or health monitoring devices is on the rise. Because these devices are designed to collect data related to health and activity, many people want to monitor their health and vital signs. Therefore, in this study, using data from Apple Watch and Fitbit devices, two of the most important health gadgets in the field of electronic devices, a model was developed to classify and predict the type of physical activity into 6 classes. For this purpose, 8 ML frameworks, including LGBM, Extra Trees, XGB, RF, Bagging, K-Nearest Neighbors, NuSVC, and AdaBoost, were used in 2 approaches: balanced and unbalanced data. For balancing the data, the SMOTE method was used as one of the oversampling methods. The results showed that, across both Apple Watch and Fitbit devices, the LGBM classifier trained on balanced data achieved the highest evaluation indices and thus the highest accuracy.