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. 2017 Feb 3;17(2):286.
doi: 10.3390/s17020286.

Real Time Apnoea Monitoring of Children Using the Microsoft Kinect Sensor: A Pilot Study

Ali Al-Naji  1   2 , Kim Gibson  3 , Sang-Heon Lee  4 , Javaan Chahl  5   6
Affiliations

Real Time Apnoea Monitoring of Children Using the Microsoft Kinect Sensor: A Pilot Study

Ali Al-Naji et al. Sensors (Basel). .

Abstract

The objective of this study was to design a non-invasive system for the observation of respiratory rates and detection of apnoea using analysis of real time image sequences captured in any given sleep position and under any light conditions (even in dark environments). A Microsoft Kinect sensor was used to visualize the variations in the thorax and abdomen from the respiratory rhythm. These variations were magnified, analyzed and detected at a distance of 2.5 m from the subject. A modified motion magnification system and frame subtraction technique were used to identify breathing movements by detecting rapid motion areas in the magnified frame sequences. The experimental results on a set of video data from five subjects (3 h for each subject) showed that our monitoring system can accurately measure respiratory rate and therefore detect apnoea in infants and young children. The proposed system is feasible, accurate, safe and low computational complexity, making it an efficient alternative for non-contact home sleep monitoring systems and advancing health care applications.

Keywords: Microsoft Kinect sensor; apnoea; apparent life-threatening event; motion detection; motion magnification; real-time image sequence analysis.

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Conflict of interest statement

The authors of this manuscript have no conflict of interest relevant to this work.

Figures

Figure 1
Figure 1
Microsoft Kinect v2 sensor.
Figure 2
Figure 2
System overview. The proposed system includes a Kinect sensor connected to a laptop via Microsoft Kinect adapter; a real-time system based on motion magnification and motion detection; and software built-in Kinect library, including body index and skeleton tracking.
Figure 3
Figure 3
Skeletal joints provided in the Kinect code library. The Region of interest is the yellow pentagon defined by the 5 points.
Figure 4
Figure 4
Red, green & blue image, depth map, thermal, skeleton tracking, and body index from the Kinect v2 sensor.
Figure 5
Figure 5
A five minutes simulation for healthy subject where the subject was asked to hold his breath twice during the measurement for the first scenario (without a blanket).
Figure 6
Figure 6
A five minutes simulation for healthy subject where the subject was asked to hold his breath twice during the measurement for the first scenario (with a blanket).
Figure 7
Figure 7
A five minutes simulation for healthy subject where the subject was asked to hold his breath twice during the measurement for the second scenario (without a blanket).
Figure 8
Figure 8
A five minutes simulation for healthy subject where the subject was asked to hold his breath twice during the measurement for the second scenario (with a blanket).
Figure 9
Figure 9
(a) Correlation plot; (b) Bland-Altman plot of the difference between measured data and reference data (first scenario without a blanket).
Figure 10
Figure 10
(a) Correlation plot; (b) Bland-Altman plot of the difference between measured data and reference data (first scenario with a blanket).
Figure 11
Figure 11
(a) Correlation plot; (b) Bland-Altman plot of the difference between measured data and reference data (second scenario without a blanket).
Figure 12
Figure 12
(a) Correlation plot; (b) Bland-Altman plot of the difference between measured data and reference data (second scenario with a blanket).
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