Abstract
Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space.
Highlights
Respiration is a vital function of our human body, bringing in oxygen as we inhale and sending out carbon dioxide as we breathe out
We argue in this article that breathing as a modality for activity recognition can complement well that of user body posture tracking, which is often central in vision-based action recognition from the user’s environment
We present in this paper a novel method and its crucial parameters for estimating a user’s breathing from a distance using depth camera data, to enable respiration as supplementary feature in depth-based activity recognition scenarios, without any additional hardware requirements while being non-intrusive and computationally efficient
Summary
Respiration is a vital function of our human body, bringing in oxygen as we inhale and sending out carbon dioxide as we breathe out. The rate of respiration is one of the most important vital signs [1] It usually lies between 12 to 16 breaths per minute, and tends to change with physical exercise, fever, illness, and with a range of conditions [2]. For instance in sports and fitness applications, it is required to learn and maintain a specific breathing technique, while the respiratory rate is an important indicator of an athlete’s performance. Being less obstructive, remain underrepresented in this domain due to their susceptibility to physical movements. They can play an important role once such limitations are removed, and can help in distinguishing breathing-specific activities and provide user posture using the same modality
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