RAPID: Wireless Positioning for Mitigating COVID19 Surface Transmissions

RAPID：用于缓解新冠病毒表面传播的无线定位

• 批准号: 2032704
• 负责人: Fadel Adib
• 金额: $ 10万
• 依托单位: Massachusetts Institute of Technology
• 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: 2020-06-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032704&HistoricalAwards=false
• 关键词: RAPID; Wireless; Positioning; Mitigating; COVID19

The project develops a new micro-location technology for tracking hand movements and alerting users when they are about to touch their face. The technology uses wireless signals and mobile sensors to track the distance between a user's hand and their face, and introduces new algorithms and frameworks that enable achieving high accuracy and robustness at ultra-low cost. Such technology can significantly reduce COVID19 surface transmissions, which account for 10% of all COVID transmissions according to the CDC and scientific studies. The success of this project can have an immediate impact on essential workers (in factories and grocery stores) and aide in a quicker economic recovery while minimizing the impact of a potential second wave as the economy re-opens. The potential long-term impact of this research extends beyond COVID19 and future pandemics to providing transformative capabilities in networked micro-location for smart environments, indoor navigation, and asset tracking.The goal of this proposal is to design and build a low-cost (sub-$5), ubiquitous wireless positioning technology that allows tracking and predicting hand-to-face distance. Developing such a technology requires addressing challenges in terms of accuracy (centimeter-scale), interference (from co-existing technologies and multi-path reflections), and compatibility (with existing ubiquitous technologies). To overcome these challenges, the project introduces a principled multi-modal sensor fusion framework for mobile devices. This framework operates by fusing various sensing modalities that already exist in mobile devices -- including BLE, accelerometers, magnetometers, and ultrasound. Taken individually, ultra-low-cost sensors for each of these modalities lack the necessary accuracy and robustness for centimeter-scale positioning; however, because they experience uncorrelated sources of noise and interference, combining these modalities in a principled probabilistic framework enables achieving high accuracy and robustness while maintaining low cost. If successful, the resulting system would be the most accurate, low-cost, and ubiquitious micro-location technology to date.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.

该项目开发了一种新的微定位技术，用于跟踪手部动作并在用户即将触摸自己的脸部时发出警报。该技术使用无线信号和移动传感器来跟踪用户的手和脸部之间的距离，并引入新的算法和框架，能够以超低成本实现高精度和鲁棒性。此类技术可以显着减少 COVID19 表面传播，根据 CDC 和科学研究，这种传播占所有新冠病毒传播的 10%。该项目的成功可以对基本工人（工厂和杂货店）产生立竿见影的影响，并有助于经济更快复苏，同时最大限度地减少经济重新开放时潜在的第二波浪潮的影响。这项研究的潜在长期影响不仅限于新冠肺炎和未来的流行病，还为智能环境、室内导航和资产跟踪的网络微定位提供变革性能力。该提案的目标是设计和构建一种低成本（低于 5 美元）、无处不在的无线定位技术，允许跟踪和预测手与脸的距离。开发这样的技术需要解决精度（厘米级）、干扰（来自共存技术和多路径反射）和兼容性（与现有普遍技术）方面的挑战。为了克服这些挑战，该项目引入了适用于移动设备的原则性多模态传感器融合框架。该框架通过融合移动设备中已有的各种传感模式（包括 BLE、加速计、磁力计和超声波）来运行。单独来看，每种模式的超低成本传感器缺乏厘米级定位所需的精度和鲁棒性；然而，由于它们遇到不相关的噪声和干扰源，因此将这些模式组合在原则性的概率框架中可以实现高精度和鲁棒性，同时保持低成本。如果成功，由此产生的系统将是迄今为止最准确、低成本和无处不在的微定位技术。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens

• DOI: 10.1145/3448121
• 发表时间: 2021-03
• 期刊: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
• 作者: Camilo Rojas;Niels Poulsen;Mileva Van Tuyl;Daniel Vargas;Zipporah Cohen;Joe A. Paradiso;P. Maes-