Real-Time Breath Pattern Detection from Helmet
Authors: Yang Cai, Tomas Vancura, Haocheng Zheng, Lenny Weiss
Abstract: In this paper, we developed a remote breath pattern from a helmet-mounted thermal sensor while providing real-time feedback from the head-up display on the helmet. We use Lucas-Kanade Tracking and the Fast Fourier Transform to estimate and display a subject’s breaths per minute and breathing waveform in an embedded systems environment. In addition to respiration rate (RR), our visualization shows the waveform of the subject’s breathing pattern, which provides real-time diagnostic information. Our system was able to predict respiration rate with high accuracy and stability in all trials of subjects wearing face masks, due to the heat-trapping effect of facial coverings. In the unmasked cases, the error rates are higher than the masked cases, due to the higher signal-to-noise ratio and other causes. In future work, we would like to focus on unmasked RR detection to improve accuracy and robustness with better color mapping from the raw data to pixel colors, improve the tracking accuracy, improve the thermal resolution, apply signal filters, and reduce the sampling region of interest that is more sensitively tracked and centered around the nostrils.
Keywords: Wearable, breath rate, biometric, AR, augmented Reality, AI, Artificial Intelligence
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