Abstract
In the current long-term care environment, there is a shortage of manpower and a high turnover rate of staff. Therefore, residential institutions are eager to build an effective Internet of Things integration mechanism to assist institutions with automatic sensor detection and early warning capabilities. Although Internet of Things facilities have enabled prompt notification and warning of emergency events, the following problems exist when implementing Internet of Things in the facilities: (1) low compatibility between sensors has led to excessive installation costs; (2) warning systems that are based on fixed threshold values and lack of flexibility can cause false or omitted reports that result in the incapability of reflecting real conditions and additional labor costs would be required. This study uses a medical-grade Internet of Things module that can calculate the environmental values with edge computing to generate different levels of alarms by combining the index-weighted moving average method to dynamically calculate the optimal threshold value for the environment. It takes 2 months to collect data from care institutions. The average F1-Score obtained in different environments is between 0.46 and 0.88. The results show that compared with using a fixed threshold, this method can effectively reduce sensor error notifications and missed notifications.
Highlights
The rapid growth of the aging population has prompted increased demand for medical resources
According to World Population Prospects published by the World Health Organization (WHO) in 2019, those aged ø 65 years account for 9% of the total global population, and this proportion is expected to increase to 16% by 2050
Aware of the effects of rapid population aging on society and families, governments have focused on establishing policies related to geriatric long-term care (LTC)
Summary
The rapid growth of the aging population has prompted increased demand for medical resources. International Journal of Distributed Sensor Networks administration systems These policies mainly support diverse care provision by families, communities, and residential institutions to gradually expand their range of care services to benefit older people; reduce dependence on services in places with concentrated resources, such as large medical centers; distribute medical resources to meet future demands for LTC; and alleviate families’ care burdens. Several front- and back-end technologies integrated with biomedical[10,11,12] and environmental systems[13,14] have been produced on the basis of IoT concepts These technologies are suitable for institutions that specialize in LTC and data tracking. Technologies in the biomedical and environmental fields remain separate For these institutions, introducing several incompatible devices for wireless data transmission would involve cost-intensive installation and communication incompatibilities among data integration systems. Provide assistance according to data on emergencies and the degree of urgency transmitted from the sensor, develop smart care services in residential institutions, and create adequate living environments for geriatric care
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