CAREER: Data Representation and Modeling for Unleashing the Potential of Multi-Modal Wearable Sensing Systems

最近可穿戴传感系统的种类和使用的增加允许对用户的健康和健康进行持续监测。这些系统的输出使个人能够改变他们的个人习惯，以尽量减少接触污染物并保持健康的锻炼水平。此外，医疗从业者正在使用这些系统来监测康复目的的适当活动水平，并监测心律失常等威胁性疾病。但是，为了最大限度地发挥影响，在便利处理和解释这些资料方面还有大量工作要做。这一建议发展了一个计算框架，对影响个人健康的生理和环境因素之间复杂的相互作用进行建模。该奖项的贡献将促进个人和医疗从业者广泛采用可穿戴传感平台和创新分析工具。该奖项开发了评估和预测生理反应和环境因素的方法，目的是使用户能够有效地改变他们的行为。为了实现这一目标，该框架将建立在统计分析、拓扑数据分析、优化理论和人类行为分析等工具的基础上。这个新颖的框架不仅将发展新的正式技术，而且还将作为这些跨学科领域之间的桥梁。特别是，所提出的分层计算框架有可能根据表示的粒度选择，在准确性和计算灵活性之间进行权衡。该奖项将：(1)开发用于推理目的的生理、运动和环境状态同时表示的方法；(2)开发不同系统之间映射表示的技术，以实现信息共享；(3)开发技术，以提出的数据表示为基础，最大限度地影响个人的行为。该算法的开发将通过集成嵌入式平台上由于内存、计算和功率能力以及需要板外处理时的传输成本而产生的限制来进行。所提议的技术将使用户和医疗从业者能够理解、分析并根据数据中的模式做出决策。该项目的成果将为可穿戴传感系统提供创新和有效的工具，从而增强医疗从业人员的能力，从而实现有效的模式识别、数据表示和可视化。除了训练学生直接从事这个项目外，所开发的数据集和算法将被纳入一个新的研究生课程，即生理和环境感知的计算技术。本科生将参与数据收集实验、reu和当地演示。代表性不足的本科生群体将通过在STEM领域知名的多样性会议上进行演示，接触到国家层面的研究。此外，K-12当地学生社区将通过为学生和教育工作者准备的夏季讲习班参与其中。

The recent increase in the variety and usage of wearable sensing systems allows for the continuous monitoring of health and wellness of users. The output of these systems enable individuals to make changes to their personal routines in order to minimize exposures to pollutants and maintain healthy levels of exercise. Furthermore, medical practitioners are using these systems to monitor proper activity levels for rehabilitation purposes and to monitor threatening conditions such as heart arrhythmias. However, there is substantial work to be done to facilitate the processing and interpretation of such information in order to maximize impact. This proposal develops a computational framework that models the complex interactions between physiological and environmental factors contributing to an individual's health. The contributions of this award will facilitate the broad adoption of wearable sensing platforms and innovative analytical tools by individuals and medical practitioners.This award develops methodology for the estimation and prediction of physiological responses and environmental factors, with the objective of enabling users to efficiently change their behavior. To accomplish this objective, the framework will build on tools from statistical analysis, topological data analysis, optimization theory and human behavior analysis. This novel framework will not only develop new formal techniques, but it will also serve as a bridge between these cross-disciplinary fields. In particular, the proposed hierarchical computational framework has the potential of providing a trade-off between accuracy and computational flexibility based on the choice of granularity of the representation. This award will: (1) develop methodology for the concurrent representation of physiological, kinematic and environmental states for inference purposes; (2) develop techniques for mapping representations between different systems to enable information sharing; and (3) develop techniques to maximize the impact on the behavior of individuals by building on the proposed data representation. The algorithm development will be informed by integration of limitations on embedded platforms due to memory, computational and power capabilities, and transmission costs when off-board processing is required. The proposed techniques will empower users and medical practitioners to understand, analyze, and make decisions based on patterns in the data. The outcomes of this project will empower medical practitioners by providing innovative and effective tools for wearable sensing systems which enable efficient pattern identification, data representation and visualization. Besides training students directly working on this project, the data sets and algorithms developed will be incorporated into a new graduate course on computational techniques for physiological and environmental sensing. Undergraduate students will be engaged by participating in data collection experiments, REUs, and local demonstrations. Underrepresented undergraduate student communities will be exposed to the research at the national level by presenting demos at well-known diversity conferences in the STEM fields. Furthermore, K-12 local student communities will be engaged via summer workshops that will be prepared for students and educators.