Ubi-AD: Towards Ubiquitous, Passive Alzheimer Detection using the Smartwatch

Abstract

Alzheimer’s disease (AD) is a insidious and progressive neurodegenerative disease, the annual relevant social cost for AD patients can reach about $1 trillion in the world. Therefore, early diagnosis and treatment of AD play a vital role in slowing disease progression. However, existing detection methods for cognitive impairment can not consistently screen the stage of AD. To tackle this challenge, we propose an AD detection system, Ubi-AD, which combines the features of multiple biomarkers to realize passive and accurate AD detection. Unlike existing work, Ubi-AD can passively recognize the AD digital biomarkers during daily smartwatch usage without interfering with the user. At the user end, Ubi-AD first extracts the non-speech sounds (pause words, such as em, ah), which contain no privacy-sensitive content. Then, Ubi-AD recognizes the user’s walking activity, dining activity, and sleep activity from daily activities. Ubi-AD analyzes these data from smartwatch and predicts the AD stages using a multi-modal fusion neural network at the cloud end. We evaluate our model on a collected dataset from 45 volunteers. As a result, Ubi-AD can reach a detection accuracy of \(93.4\% \), which means that Ubi-AD can provide multiple effective biomarkers for ubiquitous and passive detection in daily life.