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

神经/脑相关步态障碍影响许多人，并且可能对个体的移动性、独立性、认知和自尊造成破坏。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，许多患者错过了后续预约，以监测他们在治疗/药物治疗下的疾病进展。在贫困/发展中国家（或美国农村地区），由于医疗保健费用高和/或普通家庭缺乏医疗保健，情况可能更糟。 另一方面，无线信号如今无处不在，这为使用它们来感知和了解环境提供了可能性。这是拟议工作的主要动机，引入一种新的数学基础和设计方法，使日常RF信号（如WiFi）能够检测、分类和监测步态障碍。 拟议的工作可以导致一个相当大的进步，朝着发展负担得起的家庭健康系统的步态障碍评估。 这样的系统可以进一步与医疗专业人员合作，用于步态障碍的诊断和监测。该项目也有一个教育部分，针对K-12以及代表性不足的群体。这项研究提出了一个新的基础步态障碍评估与无处不在的RF信号。这是一个相当具有挑战性的问题，分为四个主要任务。 第一个主要目标提出了一种新的方法，可以将大量现有的在线非RF步态障碍数据集转换为RF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RF数据集。这样的数据集是有条理的分析/设计所必需的，但目前缺乏和人工收集的劳动力。第二个主要任务，然后提出了一个新的处理基础，可以数学表征，第一次，一般的全频率内容的接收信号，其高能量的时期，使更复杂的运动进行适当的分析。 然后，第三个目标建立在前两个基础上，从接收到的信号中提取丰富、有效和有意义的特征，并设计一个强大的机器学习管道，用于步态障碍的检测、分类和监测，重点是了解可行性和局限性。最后，最后一个目标是通过广泛的实验验证拟议的基金会。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.