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A telehealth framework for dementia care: an ADLs patterns recognition model for patients based on NILM

Dai, Shuang and Wang, Qian and Meng, Fanlin (2021) A telehealth framework for dementia care: an ADLs patterns recognition model for patients based on NILM. In: International Joint Conference on Neural Networks (IJCNN 2021), 2021-07-18 - 2021-07-22, Virtual. (In Press)

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The ageing of the population and the increasing number of patients with dementia in modern society undoubtedly put tremendous pressure on the medical system. Providing telehealth care for potential patients and patients with dementia can reduce the burden on both the health system and caregivers. This paper describes a telehealth framework for dementia early detection and dementia care. Specifically, we propose an improved deep neural network model for Non-Intrusive Load Monitoring (NILM), which disaggregates the household’s overall energy usage into those of individual appliances based on the sequence-to-point model and transfer learning. The daily behaviour regularities of patients are then inferred by combining principal component analysis and K-means clustering based on the disaggregated appliance-level consumptions. Experiments show that the proposed model can significantly improve training efficiency and maintain load disaggregation accuracy, and the inferred behaviour regularities have great potential to be used as useful inputs and prior knowledge to the dementia condition detection platform for early detection and real-time monitoring of patient’s conditions.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Uncontrolled Keywords: non-intrusive load monitoring, dementia, deep neural network, sequence-to-point, transfer learning
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 May 2021 08:52
Last Modified: 06 Jan 2022 14:24

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