Perspective Chapter: Recognition of Activities of Daily Living for Elderly People in the Era of Digital Health

Abstract

People around the world are living longer. The question arises of how to help elderly people to live longer independently and feel safe in their homes. Activity of Daily Living (ADL) recognition systems automatically recognize the daily activities of residents in smart homes. Automated monitoring of the daily routine of older individuals, detecting behavior patterns, and identifying deviations can help to identify the need for assistance. Such systems must ensure the confidentiality, privacy, and autonomy of residents. In this chapter, we review research and development in the field of ADL recognition. Breakthrough advancements have been evident in recent years with advances in sensor technology, the Internet of Things (IoT), machine learning, and artificial intelligence. We examine the main steps in the development of an ADL recognition system, introduce metrics for system evaluation, and present the latest trends in knowledge transfer and detection of behavior changes. The literature overview shows that deep learning approaches currently provide promising results. Such systems will soon mature for more diverse practical uses as transfer learning enables their fast deployment in new environments.