Pixel Intensity Tracking for Remote Respiratory Monitoring: A Study on Indonesian Subject

TL;DR

This study proposes a non-contact method for respiratory rate monitoring using pixel intensity changes in RGB images, tested on Indonesian subjects with various configurations, achieving promising accuracy in static and dynamic conditions.

Contribution

Introduces a novel non-contact respiratory monitoring technique based on pixel intensity changes with comprehensive testing on diverse configurations.

Findings

Best static condition MAE: 0.85, RMSE: 1.49

Best dynamic condition MAE: 0.81, RMSE: 1.35

Effective configurations vary with static and dynamic states

Abstract

Respiratory rate is a vital sign indicating various health conditions. Traditional contact-based measurement methods are often uncomfortable, and alternatives like respiratory belts and smartwatches have limitations in cost and operability. Therefore, a non-contact method based on Pixel Intensity Changes (PIC) with RGB camera images is proposed. Experiments involved 3 sizes of bounding boxes, 3 filter options (Laplacian, Sobel, and no filter), and 2 corner detection algorithms (ShiTomasi and Harris), with tracking using the Lukas-Kanade algorithm. Eighteen configurations were tested on 67 subjects in static and dynamic conditions. The best results in static conditions were achieved with the Medium Bounding box, Sobel Filter, and Harris Method (MAE: 0.85, RMSE: 1.49). In dynamic conditions, the Large Bounding box with no filter and ShiTomasi, and Medium Bounding box with no filter and Harris, produced the lowest MAE (0.81) and RMSE (1.35)