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

Abstract

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.

Hand Gestures for Elderly Care Using a Microsoft Kinect

“…This is because the v2 release utilized a ToF technology [26] instead of light coding technology [27] utilized in Kinect sensor v1. Differentiation between the two releases (Kinect v1, v2) technology was explained in previous studies [25][26][27][28]. Fig.…”

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

“…Furthermore, they only considered respiratory activity and apnoea, and did not consider other vital signs. To mitigate this limitation, an improved contactless system was presented to measure the HR and RR and to sense irregular cardiopulmonary functions such as bradycardia, tachycardia, bradypnea, tachypnoea, and central apnoea by using the signal from the thoracic-abdominal region based on image sequences taken by the Microsoft Kinect v2 sensor with processing that considered unclear ROI, various illumination conditions and different sleeping postures [64]. The basic block diagram of the proposed system is presented in Figure 5.…”

Elderly Care Based on Hand Gestures Using Kinect Sensor

“…The most important features of the Kinect sensor V2 are listed in Table 2. More detail can be found in [22][23][24][25][26][27][28].…”

“…Moreover, it has enhanced specifications compared older version. The most important features of the Kinect sensor V2 are listed in More detail can be found in [22][23][24][25][26][27][28]. An Arduino-Nano type microcontroller was the heart of the proposed system it received a command from computer via serial port and controlled the GSM m had suitable specifications such as small size with a clock frequency 16 MHz [ Nano connected with a GSM-module via a transmitter and receiver through tw pins and with the computer via a mini-B USB cable.…”