Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging

TL;DR

This paper presents a robust mobile thermal imaging approach for accurately tracking respiratory rates in high-dynamic range scenes, overcoming challenges like motion artifacts and ambient temperature variations.

Contribution

It introduces three novel methods: Optimal Quantization, Thermal Gradient Flow, and Thermal Voxel, enhancing segmentation, nostril tracking, and signal reliability respectively.

Findings

Successfully tracks respiration in high-dynamic scenes

Outperforms traditional methods in robustness and accuracy

Effective in real-world mobile scenarios

Abstract

The ability to monitor respiratory rate is extremely important for medical treatment, healthcare and fitness sectors. In many situations, mobile methods, which allow users to undertake every day activities, are required. However, current monitoring systems can be obtrusive, requiring users to wear respiration belts or nasal probes. Recent advances in thermographic systems have shrunk their size, weight and cost, to the point where it is possible to create smart-phone based respiration rate monitoring devices that are not affected by lighting conditions. However, mobile thermal imaging is challenged in scenes with high thermal dynamic ranges. This challenge is further amplified by general problems such as motion artifacts and low spatial resolution, leading to unreliable breathing signals. In this paper, we propose a novel and robust approach for respiration tracking which compensates for the negative effects of variations in the ambient temperature and motion artifacts and can accurately extract breathing rates in highly dynamic thermal scenes. It has three main contributions. The first is a novel Optimal Quantization technique which adaptively constructs a color mapping of absolute temperature to improve segmentation, classification and tracking. The second is the Thermal Gradient Flow method that computes thermal gradient magnitude maps to enhance accuracy of the nostril region tracking. Finally, we introduce the Thermal Voxel method to increase the reliability of the captured respiration signals compared to the traditional averaging method. We demonstrate the extreme robustness of our system to track the nostril-region and measure the respiratory rate in high dynamic range scenes.