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

迄今为止，安全服务通常被构造为专注于高可靠性和特定风险区域（例如，汽车安全系统）。#8232;这些服务的使用仍然有限，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#8232;快速采用安全服务，并将其直接集成到手机和可穿戴设备中。通过展示这种方法的可行性，该项目可以帮助拯救生命，例如美国每年30 000多起交通事故中的一些人。它还可以为CPSC、NHTSA或FCC的安全服务#8232;提供监管政策。此外，PI不仅将培训研究生进行研究，还将通过研究实习计划积极包括本科生和高中生。研究结果将通过学术出版物传播，并通过WINLAB的行业活动和联系积极推广到无线和移动的行业。该项目旨在证明我们携带和佩戴的移动的设备可以提供有效的安全服务。当我们的设备因分散驾驶员和行人的注意力而导致危险时，这一点尤为重要。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周围环境，识别潜在的危险情况，并通过适当的干预措施来减轻这种不安全的使用。在技术层面上，主要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置信度。一个关键的观察结果是，通常存在多种可能的干预措施，这些干预措施具有不同程度的侵入性和对误报的容忍度。因此，必须使干预措施与传感器提供的置信水平相匹配。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的#8232;感知不确定性。它从大量用户群体中探索众包失败和相关性数据，并自动估计内部传感和活动识别组件提供的置信度。工具包可以进一步使用所获得的度量来帮助适配感测或应用行为。系统可以通过将一个上下文传感器从多样性模式切换到回退模式来节省能量;或者，如果置信水平已经改变，则系统可以#8232;切换到不同的干预。系统验证包括对两个应用用例进行原型设计，这些用例感知并减轻驾驶员和行人对移动终端的干扰。这些技术一起形成了系统，该系统支持在移动的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.