Behavioral anomaly detection system for the wellbeign assessment and lifestyle support of older people at home

Abstract

The wellbeing assessment of older people is becoming crucial in today’s era of aging and home care in order to provide the best possible care. New technologies are being used to assist older people at home, which generates an extensive amount of health and wellbeing information. The application of artificial intelligence algorithms to this healthcare and wellbeing data can enhance patient care and provide support to professionals by reducing their cognitive load. These algorithms can detect anomalous physiological, physical, and cognitive conditions in older individuals, which can help to identify emergency situations, or the early detection of an emerging health condition. However, while there has been relevant research in the field of anomaly detection for various engineering applications, there is little knowledge about healthcare and wellbeing-related anomaly detection. To this end, in this article, we propose an innovative system for detecting behavioral anomalies for older people that are being monitored at home with the aim of improving their lifestyle and wellbeing as well as the early detection of any physical or cognitive condition