SBIR Phase I: A smart wearable platform for remote respiratory monitoring: building better technologies for telemedicine in the age of COVID-19

SBIR 第一阶段：用于远程呼吸监测的智能可穿戴平台：为 COVID-19 时代的远程医疗构建更好的技术

• 批准号: 2014713
• 负责人: Jason Kroh
• 金额: $ 22.5万
• 依托单位: Strados Labs, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014713&HistoricalAwards=false
• 关键词: SBIR; Phase; smart; wearable; platform

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to create a smart wearable stethoscope platform as a new tool to remotely monitor patients affected by COVID-19. Many infected patients may not present with symptoms until it is too late. Remotely monitoring these patients for the development of cough and shortness of breath prior to presentation in respiratory distress is critical. Patients with existing cardiopulmonary disease are at increased risk of contracting viral or secondary bacterial pneumonia due to COVID-19, but it is challenging to continuously assess these patients’ lung sounds due to risks of healthcare worker exposure. There is a clear need for more effective ways to monitor patients’ respiratory health due to COVID-19 both in quarantined patients and those in acute care. This project allows for remote monitoring to help triage COVID-19 patients and reduce healthcare worker exposure.This Small Business Innovation Research (SBIR) Phase I project addresses the further development and optimization of an artificial intelligence-based wearable device that monitors and analyzes lung sounds in high ambient noise environments. Ambient noise affects the use of standard electronic stethoscopes. Many commercially available electronic stethoscopes address ambient noise by reducing dynamic range or by warning the user not to use the device in a high noise environment. These mitigation methods restrict the utility of these devices by limiting the information that can be obtained from the acoustic measurements. Additionally, susceptibility to ambient noise eliminates its potential use in the home environment. Ambient noise has been shown to degrade the effectiveness of machine learning algorithms trained in low-noise environments to accurately detect lung sounds. This project addresses issues with high ambient noise using novel and established techniques of passive noise cancellation, active noise cancellation, signal processing techniques, and machine learning algorithms. The optimal combination and integration of these solutions in a wearable respiratory monitoring platform will establish a useful tool for use in a variety of real-world environments. The success of this project will be measured by the improvement of the machine learning sensitivity metrics after system optimization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是创建一个智能可穿戴听诊器平台，作为远程监测受COVID-19影响的患者的新工具。许多受感染的患者可能不会出现症状，直到为时已晚。在出现呼吸窘迫之前，远程监测这些患者咳嗽和呼吸短促的发展至关重要。现有心肺疾病的患者因COVID-19感染病毒性或继发性细菌性肺炎的风险增加，但由于医护人员暴露的风险，持续评估这些患者的肺音具有挑战性。显然需要更有效的方法来监测被隔离患者和急诊患者因COVID-19而导致的呼吸系统健康状况。该项目允许远程监控，以帮助分诊COVID-19患者并减少医护人员的暴露。该小型企业创新研究（SBIR）第一阶段项目旨在进一步开发和优化基于人工智能的可穿戴设备，以监测和分析高环境噪声环境中的肺音。环境噪声会影响标准电子听诊器的使用。许多市售的电子听诊器通过减小动态范围或通过警告用户不要在高噪声环境中使用设备来解决环境噪声。这些缓解方法通过限制可以从声学测量获得的信息来限制这些设备的效用。此外，对环境噪声的敏感性消除了其在家庭环境中的潜在用途。环境噪声已被证明会降低在低噪声环境中训练的机器学习算法的有效性，以准确检测肺音。该项目使用被动噪声消除，主动噪声消除，信号处理技术和机器学习算法的新技术解决高环境噪声问题。这些解决方案在可穿戴呼吸监测平台中的最佳组合和集成将为各种现实环境中的使用提供有用的工具。该项目的成功将通过系统优化后机器学习灵敏度指标的改善来衡量。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。