Collaborative Research: ECCS: Small: Personalized RF Sensing: Learning Optimal Representations of Human Activities and Ethogram on the Fly

射频（RF）传感可以改变游戏规则，降低医疗成本和差距，提高护理质量，并促进就地老化，因为它们是非接触式低功耗设备，在黑暗中有效，不限制或改变行动自由，也不会获取私人视听记录。然而，射频技术用于人类步态识别和健康评估的进步的一个重大障碍是人类运动的连续性和顺序性，其特征可以通过取决于人的移动性的活动周期和活动之间的过渡来确定。步态是一个人特有的特征，体现了许多疾病以及衰老相关影响的重要健康信息。因此，第二个重要的挑战是开发个性化机器学习（ML）模型，该模型可以不断地从个人特定的射频数据中学习，以改进健康驱动的步态分析。该项目的目标是设计一个个性化的射频传感框架，用于监测日常生活活动（ADL），检测和表征病理，在自然环境中24小时24小时的步态。该提案的两个主要目标是：1)通过建立一个新的和通用的框架来解释、分割和分类广泛的人类活动，基于个人流动的结构和动态建模，创建一个人类族谱；2)设计一种新的深度神经网络个性化方法，利用连续和连续的观察值来提高被监测人的ADL分类精度。直方图是一种定量的、结构化的方法，以“身体状态”来描述人类的日常行为，然后将活动建模为状态之间的转换。通过将个性化概念引入射频传感，该项目将扩大个性化设备的领域，超越目前可穿戴和可植入设备的范围，现在包括射频传感器。该提案将传感器和运动学知识集成到深度神经网络设计中，从而产生具有更高精度的新架构，这将更广泛地推进射频信号分类的最新技术。此外，本文的中心思想不受设备规格的影响，可以推广到其他传感模式，为非接触、无处不在、细粒度的个性化步态分类和分析铺平了道路。该项目的成果将为在家庭和临床环境中进行及时干预和更有效的治疗铺平道路，从而降低成本并改善获得保健的机会。该项目由工程局ECCS部门的CCSS计划和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

Radio Frequency (RF) sensing can be a game changer to reduce healthcare costs and disparities, improve quality of care, and facilitate aging-in-place because they are non-contact, low-power devices that are effective in the dark, do not limit or alter freedom of movement, and do not acquire private visual or audio recordings. However, a significant impediment to the advancement of RF technologies for recognition and health assessment of human gait is the continuous and sequential nature of human movement, which can be characterized by periods of activity and transitions between activities that depend upon a person’s mobility. Gait is a person-specific trait that embodies important health information for many disorders as well as aging related impacts. Thus, a second important challenge is the development of personalized machine learning (ML) models that continually learn from person-specific RF data to improve health-driven gait analysis.The goal of this project is the design of a personalized RF-sensing framework for the monitoring of activities of daily living (ADL), detection and characterization of pathological, gaits round-the-clock 24/7 in a natural setting. The proposal’s two main objectives are: 1) Create a human ethogram via the formulation of a new and general framework for interpretation, segmentation, and categorization of a broad swath of human activities based on modeling the structure and dynamics of individual mobility; 2) Design a new approach for personalization of deep neural networks, where consecutive and contiguous observations are used to increase the classification accuracy of ADL for the monitored person. The ethogram is a quantitative, structured approach to describing daily human behavior in terms of “body states” while activities are then modeled as transitions between states. By introducing the concept of personalization to RF sensing, this project will broaden the realm of personalized devices beyond its current scope of wearable and implantable devices to now include RF sensors. The proposal integrates sensor and kinematic knowledge into DNN design, resulting in novel architectures with greater accuracy that will advance the state-of-the-art in RF signal classification more generally. Moreover, the central ideas in this proposal are independent of device specifications and can be generalized to other sensing modalities, paving the way for non-contact, ubiquitous, fine-grained personalized gait classification and analysis. The outcomes of this project will pave the way for timely interventions and more effective treatments in both home and clinical settings, reducing the costs and improving the accessibility of health care. This project is jointly funded by the CCSS program in the ECCS division of the Engineering Directorate, and the Established Program to Stimulate Competitive Research (EPSCoR).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.