Wireless Wearable Respirator for Accurate Measurement of Breathing Parameters

Xiang, Bingjin; Xu, Yu; Zhou, Meng; Zheng, Yanlang; Zhan, Luxi; Fan, Jun; Zhou, Dan; Li, Jun

doi:10.1109/iceict.2019.8846401

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These sensors, which are attached underneath the nose, are insensitive to body movement, but cannot detect respiration while airflow is exhaled through the mouth. This paper is an extended version of [47], where we introduced our preliminary approach to detect respiration. In this paper, we propose a miniaturized, ultra-low-power, and reusable sensor attaching to an off-the-shelf KN95 mask to implement wireless, non-invasive, realtime, ambulatory, and accurate monitoring of respiration in daily life.…”

Section: Introductionmentioning

confidence: 99%

Towards Accurate, Cost-Effective, Ultra-Low-Power and Non-Invasive Respiration Monitoring: A Reusable Wireless Wearable Sensor for an Off-the-Shelf KN95 Mask

Tang

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Respiratory rate is a critical vital sign that indicates health condition, sleep quality, and exercise intensity. This paper presents a non-invasive, ultra-low-power, and cost-effective wireless wearable sensor, which is installed on an off-the-shelf KN95 mask to facilitate respiration monitoring. The sensing principle is based on the periodic airflow temperature variations caused by exhaled hot air and inhaled cool air in respiratory cycles. By measuring the periodic temperature variations at the exhalation valve of mask, the respiratory parameters can be accurately and reliably detected, regardless of body movements and breathing pathways through nose or mouth. Specifically, we propose a voltage divider with controllable resistors and corresponding selection criteria to improve the sensitivity of temperature measurement, a peak detection algorithm with spline interpolation to increase sampling period without reducing the detection accuracy, and effective low-power optimization measures to prolong the battery life. The experimental results have demonstrated the effectiveness of the proposed sensor, showing a small mean absolute error (MAE) of 0.449 bpm and a very low power consumption of 131.4 μW. As a high accuracy, low cost, low power, and reusable miniature wearing device for convenient respiration monitoring in daily life, the proposed sensor holds promise in real-world feasibility.

show abstract

Section: Introductionmentioning

confidence: 99%