Using pressure map sequences for recognition of on bed rehabilitation exercises

Ming Chun Huang, Jason J. Liu, Wenyao Xu, Nabil Alshurafa, Xiaoyi Zhang, Majid Sarrafzadeh

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Physical rehabilitation is an important process for patients recovering after surgery. In this paper, we propose and develop a framework to monitor on-bed range of motion exercises that allows physical therapists to evaluate patient adherence to set exercise programs. Using a dense pressure sensitive bedsheet, a sequence of pressure maps are produced and analyzed using manifold learning techniques. We compare two methods, Local Linear Embedding and Isomap, to reduce the dimensionality of the pressure map data. Once the image sequences are converted into a low dimensional manifold, the manifolds can be compared to expected prior data for the rehabilitation exercises. Furthermore, a measure to compare the similarity of manifolds is presented along with experimental results for five on-bed rehabilitation exercises. The evaluation of this framework shows that exercise compliance can be tracked accurately according to prescribed treatment programs.

Original languageEnglish (US)
Article number6698387
Pages (from-to)411-418
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
StatePublished - Mar 2014


  • Isomap
  • local linear embedding
  • manifold learning
  • pressure images
  • range of motion
  • rehabilitation exercise

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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