A statistical modeling approach to content based video retrieval
Milind R. Naphade, Sankar Basu, et al.
ICPR 2008
Sleep monitoring is an important issue and has drawn considerable attention in medicine and healthcare. Given that traditional approaches, such as polysomnography, are usually costly, and often require subjects to stay overnight at clinics, there has been a need for a low-cost system suitable for long-term sleep monitoring. In this paper, we propose a system using low-cost multimodality sensors such as video, passive infrared, and heart-rate sensors for sleep monitoring. We apply machine learning methods to automatically infer a person's sleep state, especially differentiating sleep and wake states. This is useful information for inferring sleep latency, efficiency, and duration that are important for long-term monitoring of sleep quality in healthy individuals and in those with a sleep-related disorder diagnosis. Our experiments show that the proposed approach offers reasonable performance compared to an existing standard approach (i.e., actigraphy), and that multimodality data fusion can improve the robustness and accuracy of sleep state detection. © 2007 IEEE.
Milind R. Naphade, Sankar Basu, et al.
ICPR 2008
Ya-Ti Peng, Ching-Yung Lin, et al.
ICME 2008
Rogerio Feris, Lisa M. Brown, et al.
ICPR 2014
Arnon Amir, Sankar Basu, et al.
Computer Vision and Image Understanding