In-bed patient motion and pose analysis using depth videos for pressure ulcer prevention

Ming Ching Chang, Ting Yi, Kun Duan, Jiajia Luo, Peter Tu, Michael Priebe, Elena Wood, Max Stachura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We present a real-time depth based computer vision system for pressure ulcer prevention, in-bed patient care and monitoring. Our system can effectively determine whether or not a mobility-compromised patient has been correctly repo-sitioned at the required frequency. A depth sensor is used to detect and recognize patient movements, motion patterns, and pose positions. If the patient has stayed in an unchanged pose for too long and needs pressure releasing movements, our system can notify caregivers for repositioning or assistance. Privacy concerns are mitigated by removing the RGB components of the video stream from the camera capturing, and only processing depth measurements. We collaborated with clinical practitioners at the Charlie Norwood VA Medical Center for in-field data collection and experimental evaluation. A web portal front-end is developed such that all historical patient movements, pose positions, and repositioning data can be organized to support telehealth applications.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages4118-4122
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - Feb 20 2018
Externally publishedYes
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: Sep 17 2017Sep 20 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period9/17/179/20/17

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Keywords

  • Depth video
  • Motion analysis
  • Patient care
  • Pose analysis
  • Pressure ulcer prevention

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Chang, M. C., Yi, T., Duan, K., Luo, J., Tu, P., Priebe, M., Wood, E., & Stachura, M. (2018). In-bed patient motion and pose analysis using depth videos for pressure ulcer prevention. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 4118-4122). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8297057