Affiliations: [a] School of Economics and Management, Yanshan University, Qinhuangdao, Hebei, China
| [b]
The First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| [c] Qinhuangdao Port Public Security Bureau Qinhuangdao, Hebei, China
| [d] LiRen College, Yanshan University, Qinhuangdao, Hebei, China
Correspondence:
[*]
Corresponding author. Yumei Hou, School of Economics and Management, Yanshan University, Qinhuangdao, Hebei, China. E-mail: cuiyan222333@163.com.
Abstract: At present, there is a certain lag in the construction of the service platform of the smart home pension system in my country, which does not reflect the use characteristics of the elderly. In order to improve the reliability of the smart service system for the elderly, this research builds a smart home care service platform based on machine learning and wireless sensor networks around the state of the elderly’s home life, disease stage, physical state, and intellectual state. Moreover, after comparing the advantages and disadvantages of several wireless sensor communication network technologies and in-depth understanding of communication principles and network topology, the overall design of the system is proposed. In addition, this study combines the design requirements of the system to optimize and improve the wearable physiological parameter collection system and focuses on the design and implementation of the hardware and software of the physiological parameter collection module in the construction of the new system platform. Finally, this study analyzes the performance of the model in this study through controlled trials. The results of the study show that the platform constructed in this paper is effective.
Keywords: Machine learning, wireless sensor, smart home, pension service
