|Jia Li||The Chinese University of Hong Kong|
|Yu Rong||Tencent AI Lab|
|Helen Meng||The Chinese University of Hong Kong|
|Zhihui Lu||The Chinese University of Hong Kong|
|Timothy Kwok||The Chinese University of Hong Kong|
|Hong Cheng||The Chinese University of Hong Kong|
The authors present a novel solution named time-aware TICC and CNN (TATC), for predicting AD (Alzheimer’s Disease) from actigraphy data.
With the increase of elderly population, Alzheimer’s Disease (AD), as the most common cause of dementia among the elderly, is affecting more and more senior people. It is crucial for a patient to receive accurate and timely diagnosis of AD. Current diagnosis relies on doctors’ experience and clinical test, which, unfortunately, may not be performed until noticeable AD symptoms are developed. In this work, we present our novel solution named time-aware TICC and CNN (TATC), for predicting AD from actigraphy data. TATC is a multivariate time series classification method using a neural attention-based deep learning approach. It not only performs accurate prediction of AD risk, but also generates meaningful interpretation of daily behavior pattern of subjects. TATC provides an automatic, low-cost solution for continuously monitoring the change of physical activity of subjects in daily living environment. We believe the future deployment of TATC can benefit both doctors and patients in early detection of potential AD risk.