Abstract
In the future patients will have a more active role in strengthening and maintaining their own health. Telehealth can empower and motivate patients by giving them the chance to stay in their own homes instead of going to the hospital. A telehealth system is deployed in a patient’s home hence it will influence his or her everyday live. Therefore we believe that a telehealth system shall adapt its behavior so that it will not be a burden for the patient/resident to use. To this aim we have extended an existing telehealth platform to reason about activities of daily living in a smart home scenario. The extensions have been tested on up to three of the CASAS datasets. The extensions are two algorithms: one for understanding the resident’s everyday habits and one for predicting the resident’s next activity. The prediction algorithm correctly predicts 69.76%, 73.06%, and 65.14% in the CASAS datasets.
Original language | English |
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Title of host publication | Proceedings of the 2nd IEEE International Conference on Healthcare Informatics |
Publisher | IEEE |
Publication date | 2016 |
Pages | 168-176 |
ISBN (Print) | 978-1-5090-6118-1 |
ISBN (Electronic) | 978-1-5090-6117-4 |
DOIs | |
Publication status | Published - 2016 |
Event | 2nd IEEE International Conference on Healthcare Informatics - Chicago, United States Duration: 4. Oct 2016 → 7. Oct 2016 Conference number: 2 http://www.ieee-ichi.org/ |
Conference
Conference | 2nd IEEE International Conference on Healthcare Informatics |
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Number | 2 |
Country/Territory | United States |
City | Chicago |
Period | 04/10/2016 → 07/10/2016 |
Internet address |
Keywords
- activities of daily living
- activity prediction
- ambient intelligence
- case-based reasoning
- sequential prediction algorithm