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

作者：Al Naji Ali^*; Chahl Javaan

来源：Sensors (Switzerland), 2018, 18(3): 920.

DOI：10.3390/s18030920

• cardiopulmonary signal
• video magnification techniques
• improved signal decomposition technique
• blind source separation
• canonical correlation analysis
• frame subtraction method

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.

• 出版日期2018-3