Automated Remote Pulse Oximetry System (ARPOS)

Figures

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  Figure 1. Participants Equipment distribution. Plot shows participants who had required equipment to take part in the study and remaining participants were sent the equipment if they resided within the UK.

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  Figure 2. Final setup of the research study where the Wellue pulse oximeter and the ARPOS system acquire data simultaneously [59].

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  Figure 3. The optical extinction spectra of oxy-haemoglobin (red line) and deoxy-haemoglobin (blue line) within blood. Fig generated using data by Prahl [60].

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  Figure 4. The spectral extinction coefficient differences between deoxy-haemoglobin and oxy-haemoglobin (deoxy minus oxy, shown with a black line). Shaded areas correspond to the spectral regions of colour camera channels red, green, and blue; the grey shaded area is the near infrared (IR) spectral region above 800nm, which IR cameras, such the Microsoft Kinect games console camera, are able to detect. Fig generated using data by Prahl [60].

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  Figure 5. ARPOS live application demo image. The image shows peoples faces are identified on the left (monitor screen) where the heart rate is the blue line and blood oxygenation level is the red line plotted on the screen where the shaded regions represents the potential error.

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  Figure 6. Sample reading from the system. The image showing the regions of interest (forehead, cheeks, and lips) automatically extracted from the face.

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  Figure 7. Screenshot of data acquisition study. A screenshot of what participants see during the data acquisition study showing that their face has been identified correctly and showing a log to update the user about the next steps.

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  Figure 8. Illustration of data acquisition study design. Illustration showing the design of how the data were acquired in the study.

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  Figure 9. ARPOS Data Processing Flow Diagram. Showing the acquisition and the analysis for calculating heart rate and blood oxygenation level from obtained image data.

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  Figure 10. Blocking Queue Collection Concept. The image shows thread that puts the frame data in a blocking queue collection and another thread takes and processes it from the queue (extracts face data and writes it to the disk).

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  Figure 11. Flow chart showing post processing where ROI are extracted using Dlib in python from participants’ faces.

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  Figure 12. Colour ROI obtained from a participant’s face.