Ageing is associated with a decline in physical activity and a decrease in the ability to perform activities of daily living, affecting physical and mental health. Elderly people or patients could be supported by a human activity recognition (HAR) system that monitors their activity patterns and intervenes in case of change in behavior or a critical event has occurred. A HAR system could enable these people to have a more independent life. In our approach, we apply machine learning methods from the field of human activity recognition (HAR) to detect human activities. These algorithmic methods need a large database with structured datasets that contain human activities. Compared to existing data recording procedures for creating HAR datasets, we present a novel approach, since our target group comprises of elderly and diseased people, who do not possess the same physical condition as young and healthy persons. Since our targeted HAR system aims at supporting elderly and diseased people, we focus on daily activities, especially those to which clinical relevance in attributed, like hygiene activities, nutritional activities or lying positions. Therefore, we propose a methodology for capturing data with elderly and diseased people within a hospital under realistic conditions using wearable and ambient sensors. We describe how this approach is first tested with healthy people in a laboratory environment and then transferred to elderly people and patients in a hospital environment. We also describe the implementation of an activity recognition chain (ARC) that is commonly used to analyse human activity data by means of machine learning methods and aims to detect activity patterns. Finally, the results obtained so far are presented and discussed as well as remaining problems that should be addressed in future research.
REACH stands for "Responsive Engagement of the Elderly Promoting Activity and Customized Healthcare". Sustained physical activity matters greatly to the health and well-being of older people and significantly improves their chance of maintaining independent living. It can make a difference across the whole care continuum as well as in almost every setting. Therefore, REACH solutions focus on the systematic, targetoriented increase of physical activity of older people, and tackle the whole prevention spectrum (primary, secondary, and tertiary). It seeks to empower older people and their formal and informal caregivers, and works towards viable solutions for both the formal and in-formal care sector. Technology-based personalization of prevention, activation, and care services provided in various living and care settings is at the center of the developed solutions. Ideally toolkit approach would allow for the tailoring of solutions that create value for end-users, care providers and health care payers alike through the combination, integration and adaptation/re-design elements towards the different contexts of different countries, different payment and reimbursement structures. This Special Issue sheds light on such solutions, their conception, their development, and their testing.
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