Patient associated motion detection with optical flow using microsoft Kinect V2
Pages 274 - 275
Abstract
This work describes our recent work of detecting the patient associated motions in a hospital room. After we had installed the designed Kinect V2 sensor-based health system in the hospital, we began to face big data challenges. The acquired data is big in both size and content. In this paper, we will propose a method to filter the big data using optical flow methods. As a result, we can discard the unnecessary data and quickly target on the data including valuable motion information about the patient. The proposed methodology facilitates the follow-up activity detection and serves for evaluating the amount of the movement the patient generates to allow the caregiver to improve the treatment plan.
References
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https://www.cdc.gov/hai/surveillance/index.html
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L. Liu and S. Mehrotra, "Bed angle detection in hospital room using Microsoft Kinect V2," IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, 2016, pp. 277--280.
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L. Liu and S. Mehrotra, "Detecting Out-of-Bed Activities to Prevent Pneumonia for Hospitalized Patient Using Microsoft Kinect V2," IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, DC, 2016, pp. 364--365.
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https://www.mc10inc.com/our-products/biostamprc
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E. S. Sazonov, G. Fulk, J. Hill, Y. Schutz and R. Browning, "Monitoring of Posture Allocations and Activities by a Shoe-Based Wearable Sensor," IEEE Transactions on Biomedical Engineering, vol. 58, no. 4, pp. 983--990, April 2011.
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L. Liu, M. Popescu, M. Skubic, M. Rantz, "An automatic fall detection framework using data fusion of Doppler radar and motion sensor network," Proceedings of 36th EMBS, Chicago, 26-30 Aug. 2014, pp. 5940--5943.
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P.E. Cuddihy, T. Yardibi, Z.J. Legenzoff, L. Liu, C.E. Phillips, C. Abbott, C. Galambos, J. Keller, M. Popescu, J. Back, M. Skubic, and M. Rantz, "Radar walking speed measurements of seniors in their apartments: technology for fall prevention," Proceedings of 34th EMBS, San Diego CA, Aug. 28-Sep. 1, 2012, pp 260--263.
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E. Herbst, X. Ren and D. Fox, "RGB-D flow: Dense 3-D motion estimation using color and depth," IEEE International Conference on Robotics and Automation, Karlsruhe, 2013, pp. 2276--2282.
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Published: 17 July 2017
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CHASE '17
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CHASE '17: ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies
July 17 - 19, 2017
Pennsylvania, Philadelphia
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