Predicting Sleep-Wake Times from Wearable Sensor Data using Change Point Detection

Oregon Convention Center 

In the era of ubiquitous wearable technology, the prediction of sleep and wake times based on activity and biometric data has emerged as an important area of research. Previous studies have predominantly relied on supervised learning algorithms, trained using polysomnography(PSG) data. However, the collection and labeling of PSG data are prohibitively expensive and logistically challenging. Moreover, the alternative use of self-recorded sleep reports as labels for PSG data is fraught with issues of subjectivity and inaccuracy. In response to these challenges, this study introduces an unsupervised algorithm for sleep/wake times prediction using biometric data obtained from wearable devices. This algorithm is grounded in the change point detection methodology, a technique well-suited for identifying pattern changes in time-series data. We estimate common parameters based on general patient data, which enhances the algorithm's adaptability across diverse patient profiles. The algorithm was tested on a cohort of 590 patients. Our results not only validate the effectiveness of the proposed method but also opens new avenues for leveraging wearable device data in sleep research.

Change point detection

Sleep-wake times prediction

Unsupervised learning

Multivariate time-series analysis

Wearable sensor data 

Section on Medical Devices and Diagnostics