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

Abstract

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.

Non-Intrusive Load Monitoring (NILM), Interests and Applications

“…Later, the same authors Alcalá et al [68], then proposed a paper in which they examined the home care monitoring system through the data of smart meters installed in these elderly people. Later, Dai et al [69] developed in the context of telehealth and for elderly people with reduced mobility and dementia, a model for recognizing patterns of activities of daily living (ADL) based on the NILM. For the specific case of NILM applied to ADL, the demand response is able to deliver real-time information to the involved parties.…”