Elements of information theory
Elements of information theory
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Identifying Movement Onset Times for a Bed-Based Pressure Sensor Array
MEMEA '06 Proceedings of the IEEE International Workshop on Medical Measurement and Applications, 2006. MeMea 2006.
Body posture identification using hidden Markov model with a wearable sensor network
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Unobtrusive sleep posture detection for elder-care in smart home
ICOST'10 Proceedings of the Aging friendly technology for health and independence, and 8th international conference on Smart homes and health telematics
Monitoring body positions and movements during sleep using WISPs
WH '10 Wireless Health 2010
eCushion: An eTextile Device for Sitting Posture Monitoring
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
BSN '12 Proceedings of the 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks
IEEE Transactions on Information Theory
Unobtrusive Assessment of Motor Patterns During Sleep Based on Mattress Indentation Measurements
IEEE Transactions on Information Technology in Biomedicine
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Sleep posture affects the quality of our sleep and is especially important for such medical conditions as sleep apnea and pressure ulcers. In this paper, we propose a design for a dense pressure-sensitive bedsheet along with an algorithmic framework to recognize and monitor sleeping posture. The bedsheet system uses comfortable textile sensors that produces high-resolution pressure maps. We develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. In demonstrating this system, we run 2 pilot studies: one evaluates the performance of our methods with 14 subjects to analyze 6 common postures; the other is a series of overnight studies to verify continuous performance. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than traditional methods, achieving up to 83.0% precision and 83.2% recall on average.