基于超声平面阵列的非接触式呼吸波形估计

Geng-Shi Jeng;Sheng Chen;Le-Tung Hsieh;Men-Tzung Lo
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摘要

准确和非接触式呼吸监测对于临床诊断和家庭医疗保健都是必不可少的,它提供了持续、非侵入性观察的潜力。基于超声波的系统,特别是集成到家庭智能设备中时,提供了一种经济高效的解决方案。然而,现有的方法受到指向性差、衣服穿透力不足、依赖平均呼吸率而没有波形细节以及由于连续波多普勒技术而无法测量范围的限制。为了解决这些挑战,本研究开发了一种新的18khz, 16通道二维(2-D)超声阵列系统,采用自适应波束形成来提高呼吸波形检测的灵敏度和准确性。该系统集成了脉冲和调频连续波(FMCW)激励,将信噪比(SNR)提高了20 dB,而二维波束形成技术直接估计呼吸运动的延迟,将信噪比提高了8.5 dB,并且无需进行耗时的体积扫描。实验结果表明,在电机控制板测试中,亚毫米位移精度超过可穿戴惯性测量设备,人体试验表明,在不同服装类型和距离下,平均呼吸频率误差为每分钟0.13次。提出的系统不仅推进了远程呼吸监测,而且为增强临床和家庭环境中的健康诊断铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contactless Respiratory Waveform Estimation Using Ultrasound Planar Array
Accurate and contactless respiratory monitoring is essential for both clinical diagnostics and home healthcare, offering the potential for continuous, non-invasive observation. Ultrasound-based systems, particularly when integrated into home smart devices, provide a cost-effective solution. However, existing approaches are limited by poor directivity, inadequate clothing penetration, reliance on averaged respiratory rates without waveform details, and the inability to measure range due to continuous-wave Doppler techniques. To address these challenges, this study develops a novel 18-kHz, 16-channel two-dimensional (2-D) ultrasound array system employing adaptive beamforming to enhance sensitivity and accuracy in respiratory waveform detection. The system integrates pulsed and frequency-modulated continuous-wave (FMCW) excitation to improve the signal-to-noise ratio (SNR) by 20 dB, while the 2-D beamforming technique directly estimates delays from respiratory movements, boosting SNR by an additional 8.5 dB and eliminating the need for time-intensive volumetric scanning. Experimental results demonstrate sub-millimeter displacement accuracy in motor-controlled plate tests, surpassing wearable inertial measurement devices, and human trials reveal an average respiratory rate error of 0.13 breaths per minute across various clothing types and distances. The proposed system not only advances remote respiratory monitoring but also paves the way for enhanced health diagnostics in both clinical and home settings.
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