Title :
A dense pressure sensitive bedsheet design for unobtrusive sleep posture monitoring
Author :
Liu, Jason J. ; Wenyao Xu ; Ming-Chun Huang ; Alshurafa, Nabil ; Sarrafzadeh, Majid ; Raut, N. ; Yadegar, B.
Author_Institution :
Dept. of Comput. Sci., Univ. of California, Los Angeles, Los Angeles, CA, USA
Abstract :
Sleep plays a pivotal role in the quality of life, and sleep posture is related to many medical conditions such as sleep apnea. In this paper, we design a dense pressure-sensitive bedsheet for sleep posture monitoring. In contrast to existing techniques, our bedsheet system offers a completely unobtrusive method using comfortable textile sensors. Based on high-resolution pressure distributions from the bedsheet, 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. We run a pilot study and evaluate the performance of our methods with 14 subjects to analyze 6 common postures. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than state-of-the-art methods, achieving up to 83.0% precision and 83.2% recall on average.
Keywords :
image recognition; medical computing; medical image processing; monitoring; patient diagnosis; sleep; comfortable textile sensors; dense pressure sensitive bedsheet design; medical conditions; posture recognition; pressure distributions; pressure image analysis; quality of life; unobtrusive sleep posture monitoring; Feature extraction; Hip; Monitoring; Sensors; Shoulder; Sleep apnea; Training; Bedsheet; Pressure Image Analysis; Sleep Posture; Sparse Classifier;
Conference_Titel :
Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-4573-6
Electronic_ISBN :
978-1-4673-4574-3
DOI :
10.1109/PerCom.2013.6526734