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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响 /商业潜力是通过接触跟踪在Covid-19大流行期间保护公众。 该项目将开发可穿戴技术，以便在工人紧密接触时提供接近警报，从而对社会疏远产生认识。如果工人测试呈阳性-19，它还将记录工人之间的每一个联系，以提供联系跟踪。这些工具可以保护一线工人免受病毒的传播以及其家人和社区的传播。该技术将扩展现有的可穿戴技术，以减少工作场所中的过度锻炼伤害。这项小型企业创新研究（SBIR）I阶段项目将通过机器学习算法估算工人之间的距离，该算法结合了从蓝牙技术的信号强度以及从加速度计和陀螺仪的运动传感器数据以及每个接触事件结束时测量工人活动的运动传感器数据。该项目将开发距离检测算法和一系列绩效要求，以优化快速，准确的解决方案的设计。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来提供支持。