Tipparaju Vishal Varan, Xian Xiaojun, Bridgeman Devon, Wang Di, Tsow Francis, Forzani Erica, Tao Nongjian
Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA.
IEEE Sens J. 2020 May 15;20(10):5510-5518. doi: 10.1109/jsen.2020.2969635. Epub 2020 Feb 27.
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 (R = 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.
呼吸追踪对于评估肺功能、运动生理学和能量消耗至关重要。传统方法需要使用通过管子连接到固定设备的面罩或口器，这限制了位置、运动甚至姿势。为了在不同场景下获得代表患者真实状态的准确呼吸生理参数，非常需要一种在自由生活条件下对患者活动干预较少的可穿戴技术。在此，我们提出一种小型化、可靠且宽动态范围的流量传感技术，该技术不受方向、运动和噪声的影响。据我们所知，这是第一项引入专注于自由生活条件下呼吸追踪的完全集成面罩设备的工作。实现这一目标存在两个关键挑战：小型化流量传感和消除运动引起的伪影。为应对这些挑战，我们提出了两项技术创新：1）在硬件方面，我们设计了一种基于差压呼吸流速计方法和运动传感的集成流量传感技术；2）在软件方面，我们开发了基于基线追踪以及方向和运动补偿的综合算法。所提出技术的有效性已通过实验得到证明。与参考方法计算的值相比，模拟器和真实呼吸条件下的实验结果显示，分钟通气量（VE）的相关性很高（分别为R = 0.9994和0.9964），平均误差在2.5%以内。这些结果表明，所提出的方法在自由生活条件下追踪关键呼吸参数方面准确可靠。
