Study on behavioral risk for aging based on smart mattress monitoring data

Abstract

With an aging population and a gap in demand for professional caregivers, China's elderly care system is facing severe challenges. Using data mining to expand the smart senior care scenario can effectively improve elderly care services. Based on smart mattress datasets, this paper uses machine learning classification and unsupervised anomaly detection models to analyze the possible risks in the daily behavior of older people from the perspectives of both sleep apnea problems and abnormal physiological information. The model results show that the Stacking algorithm based on data fusion can effectively identify the risk of sleep apnea. In contrast, the Prophet and DBSCAN models can carry out anomaly mining of physiological information of single and combined variables, respectively. Ultimately, based on the research, this paper provides targeted recommendations regarding data collection and integration applications.