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. Therefore, we propose a methodology for capturing data with elderly and diseased people within a hospital under realistic conditions using wearable and ambient sensors. 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. 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. These algorithmic methods need a large database with structured datasets that contain human activities. In our approach, we apply machine learning methods from the field of human activity recognition (HAR) to detect human activities. A HAR system could enable these people to have a more independent life. ![]() 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. 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.
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