Wearable mHealth devices are being used in a variety of healthcare scenarios including tracking of sleep and physical activities. mHealth technology is a promising direction for revolutionizing the preventive and medical healthcare. To unlock the potential of mHealth, several data-related challenges including scalability, heterogeneity, noise, and privacy need to be addressed. Using polysomnography (PSG) and accelerometer recorded sleep data collected in Buxton’s lab, our goal is to develop reliable machine learning based models for sleep scoring and sleep parameter estimation.
Main research directions:
- How to develop reliable cost-effective models for sleep/wake classification and sleep parameter estimation using unlabeled accelerometer data?
- How to develop personalized models for sleep for sleep/wake classification and sleep parameter estimation?
- How to integrate data from multiple accelerometer sensors for tracking sleep quality?
Khademi A, El-Manzalawy Y, Buxton O, Honavar V (2018) Toward personalized sleep-wake prediction from actigraphy. In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). pp. 414-417.
El-Manzalawy Y, Buxton O, Honavar V (2017) Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017). pp. 718-723.