TY - JOUR
T1 - Reliable Breathing Tracking with Wearable Mask Device
AU - Tipparaju, Vishal Varun
AU - Xian, Xiaojun
AU - Bridgeman, Devon
AU - Wang, Di
AU - Tsow, Francis
AU - Forzani, Erica
AU - Tao, Nongjian
N1 - Funding Information:
Manuscript received November 8, 2019; revised December 16, 2019; accepted January 20, 2020. Date of publication January 27, 2020; date of current version April 16, 2020. This work was supported by the National Institutes of Health (NIH) under Project 5R44HL123164-04. The associate editor coordinating the review of this article and approving it for publication was Dr. Yong Zhu. (Corresponding authors: Xiaojun Xian; Nongjian Tao.) The authors are with the Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, AZ 85287 USA (e-mail: vlnu1@asu.edu; xiaojun.xian@asu.edu; dbridgem@asu.edu; dwang96@asu.edu; tsing.tsow@asu.edu; eforzani@asu.edu; Nongjian.Tao@asu.edu). Digital Object Identifier 10.1109/JSEN.2020.2969635
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2020/5/15
Y1 - 2020/5/15
N2 - Breathing tracking is critical for the assessment of lung functions, exercise physiologies, and energy expenditure. Conventional methods require using a face mask or mouthpiece that is connected to a stationary equipment through a tube, restricting the location, movement, or even the posture. To obtain accurate breathing physiology parameters that represent the true state of the patient during different scenarios, a wearable technology that has less intervention to patient's activities in free-living conditions is highly preferred. Here, we propose a miniaturized, reliable, and wide-dynamic ranged flow sensing technology that is immune to orientation, movement, and noise. As far as we know, this is the first work of introducing a fully integrated mask device focusing on breath tracking in free-living conditions. There are two key challenges for achieving this goal: miniaturized flow sensing and motion-induced artifacts elimination. To address these challenges, we come up with two technical innovations: 1) in hardware wise, we have designed an integrated flow sensing technique based on differential pressure Pneumotach approach and motion sensing; 2) in software wise, we have developed comprehensive algorithms based baseline tracking and orientation and motion compensation. The effectiveness of the proposed technology has been proven by the experiments. Experimental results from simulator and real breath conditions show high correlation (R2 = 0.9994 and 0.9964 respectively) and mean error within 2.5% for Minute Volume (VE), when compared to values computed from reference methods. These results show that the proposed method is accurate and reliable to track the key breath parameters in free-living conditions.
AB - Breathing tracking is critical for the assessment of lung functions, exercise physiologies, and energy expenditure. Conventional methods require using a face mask or mouthpiece that is connected to a stationary equipment through a tube, restricting the location, movement, or even the posture. To obtain accurate breathing physiology parameters that represent the true state of the patient during different scenarios, a wearable technology that has less intervention to patient's activities in free-living conditions is highly preferred. Here, we propose a miniaturized, reliable, and wide-dynamic ranged flow sensing technology that is immune to orientation, movement, and noise. As far as we know, this is the first work of introducing a fully integrated mask device focusing on breath tracking in free-living conditions. There are two key challenges for achieving this goal: miniaturized flow sensing and motion-induced artifacts elimination. To address these challenges, we come up with two technical innovations: 1) in hardware wise, we have designed an integrated flow sensing technique based on differential pressure Pneumotach approach and motion sensing; 2) in software wise, we have developed comprehensive algorithms based baseline tracking and orientation and motion compensation. The effectiveness of the proposed technology has been proven by the experiments. Experimental results from simulator and real breath conditions show high correlation (R2 = 0.9994 and 0.9964 respectively) and mean error within 2.5% for Minute Volume (VE), when compared to values computed from reference methods. These results show that the proposed method is accurate and reliable to track the key breath parameters in free-living conditions.
KW - Breathing tracking
KW - differential pressure pneumotach
KW - face mask
KW - wearable device
UR - http://www.scopus.com/inward/record.url?scp=85083838128&partnerID=8YFLogxK
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U2 - 10.1109/JSEN.2020.2969635
DO - 10.1109/JSEN.2020.2969635
M3 - Article
AN - SCOPUS:85083838128
SN - 1530-437X
VL - 20
SP - 5510
EP - 5518
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 10
M1 - 8970244
ER -