Next Article in Journal
Enhanced Moisture-Reactive Hydrophilic-PTFE-Based Flexible Humidity Sensor for Real-Time Monitoring
Next Article in Special Issue
Inverse Piezoresistive Nanocomposite Sensors for Identifying Human Sitting Posture
Previous Article in Journal
Measuring Gait Quality in Parkinson’s Disease through Real-Time Gait Phase Recognition
Previous Article in Special Issue
Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration
Open AccessArticle

Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor

by Ali Al-Naji 1,2,* and Javaan Chahl 1,3
1
School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
2
Electrical Engineering Technical College, Middle Technical University, Al Doura 10022, Baghdad, Iraq
3
Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, VIC 3207, Australia
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(3), 920; https://doi.org/10.3390/s18030920
Received: 6 January 2018 / Revised: 2 March 2018 / Accepted: 19 March 2018 / Published: 20 March 2018
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI). This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation of cardiopulmonary-signal-and-detection-related abnormal cardiopulmonary events (e.g., tachycardia, bradycardia, tachypnea, bradypnea, and central apnoea) in many possible sleeping postures within varying environmental settings including in total darkness and whether the subject is covered by a blanket or not. The proposed system extracts the signal from the abdominal-thoracic region where cardiopulmonary activity is most pronounced, using a real-time image sequence captured by Kinect v2 sensor. The proposed system shows promising results in any sleep posture, regardless of illumination conditions and unclear ROI even in the presence of a blanket, whilst being reliable, safe, and cost-effective. View Full-Text
Keywords: cardiopulmonary signal; video magnification techniques; improved signal decomposition technique; blind source separation; canonical correlation analysis; frame subtraction method cardiopulmonary signal; video magnification techniques; improved signal decomposition technique; blind source separation; canonical correlation analysis; frame subtraction method
Show Figures

Graphical abstract

MDPI and ACS Style

Al-Naji, A.; Chahl, J. Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor. Sensors 2018, 18, 920.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop