Application of GPS and Accelerometers in Predicting Physical Activity Patterns

To reduce the workload, to predict the physical activity mode with fewer variables, and to construct a path to predict PAM based on temporal and spatial data generated by physical activity and the amount of activity, this paper mainly uses the literature, logical analysis, and inductive method to sort out and summarize the basic methods and models in predicting physical activity mode using GPS and accelerometer at home and abroad and to construct a path from equipment. The process involves selecting and determining the predictors, collecting data, and using supervised learning algorithms and unsupervised learning algorithms. The joint use of GPS and accelerometers is fully capable of predicting physical activity patterns and can realize the method of predicting physical activity patterns based on the spatiotemporal data and the amount of activity generated by physical activity, although GPS and accelerometers have shortcomings in predicting PAM in terms of positioning error, missing data, and wearing position and mode.