SBIR Phase I: A Workplace Wearable Device For Social Distancing and Contact Tracing in Essential Businesses During COVID-19

SBIR 第一阶段：一种工作场所可穿戴设备，用于在 COVID-19 期间在基本业务中进行社交距离和接触者追踪

• 批准号: 2030327
• 负责人: Haytham Elhawary
• 金额: $ 25.6万
• 依托单位: One Million Metrics Corp
• 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: 2020-09-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030327&HistoricalAwards=false
• 关键词: SBIR; Phase; Workplace; Wearable; Device

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to protect the public during the COVID-19 pandemic through contact tracing. This project will develop wearable technology to provide proximity alerts when workers are in close contact, creating awareness around social distancing. It will also record every contact between workers to provide contact tracing should a worker test positive for COVID-19. These tools protect front-line workers from the spread of the virus, along with their families and communities. The technology will extend existing wearable technology to reduce overexertion injuries in the workplace. This Small Business Innovation Research (SBIR) Phase I project will estimate distance between workers through a machine learning algorithm combining signal strength from Bluetooth technology with motion sensor data from accelerometers and gyroscopes to measure worker activity prior and at the end of each contact event. The project will develop distance detection algorithms and a set of performance requirements to optimize design of a rapid, accurate solution.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.

这个小企业创新研究第一阶段项目的更广泛的影响/商业潜力是通过追踪接触者在新冠肺炎大流行期间保护公众。该项目将开发可穿戴技术，当员工近距离接触时提供接近警报，从而提高人们对社交距离的认识。它还将记录工人之间的每一次接触，以便在工人的新冠肺炎检测呈阳性时提供接触者追踪。这些工具保护一线工作人员及其家人和社区免受病毒传播的影响。该技术将扩展现有的可穿戴技术，以减少工作场所的过度劳累伤害。该小型企业创新研究(SBIR)第一阶段项目将通过一种机器学习算法来估计工人之间的距离，该算法将蓝牙技术的信号强度与加速计和陀螺仪的运动传感器数据相结合，以测量工人在每次接触事件之前和结束时的活动。该项目将开发距离检测算法和一套性能要求，以优化快速、准确的解决方案的设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。