Abstract
The demand for smart home technologies that enable ageing in place is rising. Through activity recognition, users’ activities can be monitored. However, for dementia patients, activity recognition alone cannot address the challenges associated with changes in the user’s habits along the disease’s stage transitions. Extending activity recognition to habit recognition enables the capturing of patients’ habits and changes in habits in order to detect anomalies. This paper aims to introduce relevant features for habit recognition solutions, extracted from data, in order to enrich the representation of the user’s habits. This solution is personalisable to meet the specific needs of the patients and generalizable for use in different scenarios. In this way caregivers are better informed on the expected changes of the patient’s habits, which can help to mitigate further deterioration through early treatment and intervention.
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Acknowledgments
This work has been supported by both the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754285, and the distributed research environment E-care@home funded by the Swedish Knowledge Foundation (KKS), 2015–2019.
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Chimamiwa, G., Alirezaie, M., Banaee, H., Köckemann, U., Loutfi, A. (2019). Towards Habit Recognition in Smart Homes for People with Dementia. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_29
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DOI: https://doi.org/10.1007/978-3-030-34255-5_29
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