Sleep posture analysis using a dense pressure sensitive bedsheet

Jason J. Liu, Wenyao Xu*, Ming Chun Huang, Nabil Alshurafa, Majid Sarrafzadeh, Nitin Raut, Behrooz Yadegar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)34-50
Number of pages17
JournalPervasive and Mobile Computing
Issue numberPART A
StatePublished - Feb 2014


  • Bedsheet
  • Image analysis
  • Pressure image
  • Sleep posture analysis
  • Sparse classifier

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


Dive into the research topics of 'Sleep posture analysis using a dense pressure sensitive bedsheet'. Together they form a unique fingerprint.

Cite this