Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2

Liang Liu; Sanjay Mehrotra

doi:10.1109/CHASE.2017.99

Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2

Liang Liu, Sanjay Mehrotra

Industrial Engineering and Management Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings - 2017 IEEE 2nd International Conference on Connected Health
Subtitle of host publication	Applications, Systems and Engineering Technologies, CHASE 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	274-275
Number of pages	2
ISBN (Electronic)	9781509047215
DOIs	https://doi.org/10.1109/CHASE.2017.99
State	Published - Aug 14 2017
Event	2nd IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017 - Philadelphia, United States Duration: Jul 17 2017 → Jul 19 2017

Other

Other	2nd IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017
Country/Territory	United States
City	Philadelphia
Period	7/17/17 → 7/19/17

Keywords

motion detection
optical flow
Patient monitoring

ASJC Scopus subject areas

Computer Networks and Communications
Health(social science)
Communication
Computer Science Applications
Software
Biomedical Engineering
Health Informatics

Access to Document

10.1109/CHASE.2017.99

Cite this

Liu, L., & Mehrotra, S. (2017). Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2. In Proceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017 (pp. 274-275). [8010654] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CHASE.2017.99

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title = "Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2",

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.",

keywords = "motion detection, optical flow, Patient monitoring",

author = "Liang Liu and Sanjay Mehrotra",

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doi = "10.1109/CHASE.2017.99",

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booktitle = "Proceedings - 2017 IEEE 2nd International Conference on Connected Health",

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Liu, L & Mehrotra, S 2017, Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2. in Proceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017., 8010654, Institute of Electrical and Electronics Engineers Inc., pp. 274-275, 2nd IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017, Philadelphia, United States, 7/17/17. https://doi.org/10.1109/CHASE.2017.99

Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2. / Liu, Liang; Mehrotra, Sanjay.
Proceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 274-275 8010654.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2

AU - Liu, Liang

AU - Mehrotra, Sanjay

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N2 - 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.

AB - 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.

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ER -

Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2

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Keywords

ASJC Scopus subject areas

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