CNS Core: Small: Fundamentals of Gait Disorder Assessment with Ubiquitous Wireless Signals

神经/大脑相关的步态障碍影响许多人，并可能对个人的机动性、独立性、认知和自尊造成毁灭性的影响。因此，早期诊断和适当的监测是给予个人适当护理的关键，可以优化他们的福祉。然而，许多人只是在疾病进展了一段时间后才寻求医学意见。此外，一旦寻求医疗帮助，许多患者就会错过后续预约，以监测治疗/药物治疗下的疾病进展。在贫困/发展中国家(或美国的农村地区)，情况可能会更糟，因为普通家庭的医疗保健成本很高，而且/或缺乏医疗保健。另一方面，无线信号如今无处不在，这为使用无线信号来感知和了解环境打开了可能性。这是拟议工作的主要动机，引入了一种新的数学基础和设计方法，使日常射频信号，如WiFi，能够检测、分类和监测步态障碍。拟议的工作可以在为步态障碍评估开发负担得起的家庭健康系统方面取得相当大的进步。这样的系统可以进一步与医疗专业人员合作，诊断和监测步态疾病。该项目也有一个教育部分，目标是K-12和代表性不足的群体。这项研究为利用无处不在的射频信号评估步态障碍提供了新的基础。这是一个相当具有挑战性的问题，分为四大任务。第一个主要目标提出了一种新的方法，可以将大量已有的在线非射频步态障碍数据集转换为射频数据，从而能够创建与不同步态障碍相关的大型合成步态障碍射频数据集。这种数据集对于有条不紊的分析/设计是必要的，但目前缺乏，手工收集费力得令人望而却步。第二个主要任务然后提出了一个新的处理基础，它可以第一次在数学上表征接收信号的一般全频内容及其高能历元，从而能够适当地分析更复杂的运动。第三个目标是在前两个目标的基础上，从接收到的信号中提取丰富、有效和有意义的特征，并设计一个健壮的机器学习流水线来检测、分类和监控步态障碍，重点了解其可行性和局限性。最后，最后一个目标通过广泛的实验来验证提出的基金会。这一奖项反映了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。

Neurological/brain-related gait disorders affect many people and can be devastating to the mobility, independence, cognition, and self-esteem of an individual. Thus, early diagnosis and proper monitoring is key to giving an individual the proper care that can optimize their well-being. Yet, many individuals seek a medical opinion only after the disease has progressed for a while. Furthermore, once medical help is sought, many patients miss follow-up appointments to monitor the progress of their disease under therapy/medication. The situation can be much worse in impoverished/developing nations (or rural areas in US), due to the high cost of healthcare and/or lack of it for an average family. Wireless signals, on the other hand, are ubiquitous these days, which open up the possibility of using them for sensing and learning about the environment. This is the main motivation for the proposed work, to introduce a new mathematical foundation and design methodology that enables everyday RF signals, such as WiFi, to detect, classify, and monitor gait disorders. The proposed work can result in a considerable advancement towards developing an affordable home health system for gait disorder assessment. Such a system can further work in partnership with medical professionals, for the diagnosis and monitoring of gait disorders. The project also has an educational component, targeting K-12 as well as under-represented groups.This research proposes a new foundation for gait disorder assessment with ubiquitous RF signals. This is a considerably challenging problem, which is divided into four major tasks. The first major goal proposes a new methodology that can translate the vast already-available online non-RF gait disorder datasets to RF data, enabling the creation of a large synthetic gait disorder RF datasets pertaining to different gait disorders. Such datasets are necessary for a methodical analysis/design, but currently lacking and prohibitively laborious to manually collect. The second major task then proposes a new processing foundation that can mathematically characterize, for the first time, the general full frequency content of the received signal, and its high-energy epochs, enabling proper analysis of more complex movements. The third objective then builds on the previous two to extract rich, efficient, and meaningful features from the received signal and design a robust machine leaning pipeline for the detection, classification, and monitoring of gait disorders, with an emphasis on understanding the feasibility and limitations. Finally, the last objective validates the proposed foundation with extensive experiments.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.