A Study of College Students' Lifestyle Regularity Based on Wearable Devices and Deep Learning

With the popularity of wearable devices, smart wearable devices containing various sensors have been widely adopted in healthcare applications. However, there is little research on the use of these devices to study lifestyle regularity, especially to study lifestyle regularity of college students using physiological or exercise data collected by smart wearable devices. In this work, we use the wrist wearable devices worn by students every day to collect college students' daily routine data, and establish models to analyze the regularity of the collected data and propose the use of MOE (Mixture of Experts) and transfer learning to improve the classification performance of the model. The experimental results show that the classification accuracy can be improved by 8.3% using MOE compared with not using it, and the accuracy can be further increased by 2.9% with Transfer Learning.