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|Title:||Rehabilitation risk management: enabling data analytics with quantified self and smart home data.|
|metadata.dc.title.book:||Health informatics meets eHealth digital insight--information-driven health & care : proceedings of the 11th eHealth2017 conference.|
|Epworth Authors:||Wickramasinghe, Nilmini|
|Other Authors:||Hamper, Andreas|
Sensor Based Rehabilitation Risk Management
Health Informatics Clinical Institute, Epworth HealthCare, Victoria, Australia
|Citation:||Hamper, A., Eigner, I., Wickramasinghe, N., & Bodendorf, F. (2017, January). Rehabilitation Risk Management: Enabling Data Analytics with Quantified Self and Smart Home Data. In eHealth (pp. 152-160).|
|Series/Report no.:||Studies in Health technology and informatics;236|
|Abstract:||A variety of acute and chronic diseases require rehabilitation at home after treatment. Outpatient rehabilitation is crucial for the quality of the medical outcome but is mainly performed without medical supervision. Non-Compliance can lead to severe health risks and readmission to the hospital. While the patient is closely monitored in the hospital, methods and technologies to identify risks at home have to be developed. We analyze state-of-the-art monitoring systems and technologies and show possibilities to transfer these technologies into rehabilitation monitoring. For this purpose, we analyze sensor technology from the field of Quantified Self and Smart Homes. The available sensor data from this consumer grade technology is summarized to give an overview of the possibilities for medical data analytics. Subsequently, we show a conceptual roadmap to transfer data analytics methods to sensor based rehabilitation risk management.|
|Affiliated Organisations:||Deakin University, Burwood, , Victoria, Australia.|
University of Erlangen-Nuremberg FAU, Germany
|Appears in Collections:||Health Informatics|
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