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Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

: Schrader, Lisa; Vargas Toro, Agustín; Konietzny, Sebastian; Rüping, Stefan; Schäpers, Barbara; Steinböck, Martina; Krewer, Carmen; Müller, Friedemann; Güttler, Jörg; Bock, Thomas

Volltext urn:nbn:de:0011-n-6022993 (3.6 MByte PDF)
MD5 Fingerprint: bd89d51acee7788d760f9e8c15485d74
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Erstellt am: 16.9.2020

Journal of Population Ageing 13 (2020), Nr.2, S.139-165
ISSN: 1874-7884
ISSN: 1874-7876
European Commission EC
H2020; 690425; REACH2020
Responsive Engagement of the Elderly promoting Activity and Customized Healthcare
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAIS ()
Active ageing; Human activity recognition; Machine learning; Supervised learning; Data acquisition

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.