CSR: Medium: Collaborative Research: Guardian Angel-Enabling Mobile Safety Systems

迄今为止，安全服务通常被构建为专注于高可靠性和特定风险领域（例如汽车安全系统）的专用烟囱系统。使用of& # 8232;此类服务仍然有限，因为它们需要对每个系统进行专门的投资。该项目权衡了专用系统的超高可靠性，以获得更多的&amp；将安全服务直接集成到移动设备和可穿戴设备中，从而迅速普及。通过证明这种方法的可行性，该项目可以为挽救生命做出贡献，例如美国每年有超过30,000人死于交通事故。它还可以为安全服务的监管政策提供信息&amp；#8232；在CPSC， NHTSA或FCC。此外，pi不仅将培养研究生进行研究，还将通过研究实习计划积极招收本科生和高中生。研究结果将通过学术出版物进行传播，并通过WINLAB的行业活动和联系积极推广到无线和移动行业。这个项目旨在证明我们携带和佩戴的移动设备可以提供有效的安全服务。这一点尤其重要，因为我们的设备会分散司机和行人的注意力，从而造成危险。因此，该项目追求一个系统的愿景，通过不断感知我们的活动和环境，识别潜在的危险情况，并通过适当的干预来减轻这种不安全的使用。在技术层面上，主要的挑战不仅在于设计精确的传感技术，而且在于理解和管理这些技术提供的信心水平。一个关键的观察是，通常有多种可能的干预措施，其侵入程度和对假阳性的容忍度各不相同。因此，将干预措施与传感器提供的置信度相匹配是很重要的。为了应对这一挑战，该项目开发了系统支持和工具包，以帮助开发人员跟踪和管理移动设备。感知不确定性。它探讨了众包失败和相关数据&amp；#8232；从大量用户群中自动估计提供的置信度&amp；#8232；由内部传感和活动识别组成。工具包可以进一步使用获得的度量来帮助调整感知或应用程序行为。该系统可以通过将一个上下文传感器从分集模式切换到回退模式来节省能量；或者，系统可以&amp；#8232；如果信心水平发生变化，则切换到不同的干预措施。系统验证包括两个应用程序用例的原型设计，这些用例可以感知和减轻移动设备对驾驶员和行人的干扰。这些技术共同构成了该系统，该系统支持在移动设备上开发许多其他有效的安全服务。

To date, safety services are typically constructed as dedicated stovepipe systems focusing on high reliability and a specific area of risk (e.g., automotive safety systems). Usage of
 such services remains limited since they require a dedicated investment for each system. This project trades off the ultra-high reliability of dedicated systems for the much more 
rapid adoption of safety services that comes with integrating them directly into mobiles and wearables. By demonstrating the feasibility of this approach, this project can contribute to saving lives, such as some of the more than 30,000 traffic fatalities in the United States each year. It can also inform regulatory policy for safety services 
at the CPSC, NHTSA, or FCC. Moreover, the PIs will not only train graduate students to conduct the research but also actively include undergraduates and high school students through research internship programs. Results will be disseminated through scholarly publications, active outreach to the wireless and mobile industry through WINLAB's industry events and connections.This project seeks to demonstrate that the mobile devices we carry and wear can provide effective safety services. This is particularly relevant where our devices contribute to dangers by causing distractions for drivers and pedestrians. This project therefore pursues the vision of a system that offsets such unsafe use by continually sensing our activities and surroundings, identifying potentially dangerous situations, and mitigating them through appropriate interventions. At a technical level, the primary challenge lies not only in designing precise sensing techniques but in understanding and managing the level of confidence provided by these techniques. A key observation is that there are usually multiple possible interventions of varying levels of intrusiveness and tolerance to false positives. It is therefore important to match interventions to the confidence level provided by the sensors. To address this challenge, the project develops system support and a toolkit to help developers track and manage mobile
 sensing uncertainty. It explores crowdsourcing failure and relevance data
 from a large user population and automatically estimating the confidence provided
 by internal sensing and activity recognition components. The toolkit can further use the obtained metrics to help adapt sensing or application behavior. The system might conserve energy by switching one context sensor to a fallback mode from a diversity mode; or, the system could
 switch to a different intervention if the level of confidence has changed. System validation includes prototyping two application use cases, which sense and mitigate mobile device distractions for drivers and pedestrians. Together, these techniques form the system, which supports development of many other effective safety services on mobile devices.