CAREER: Closed-loop Health Behavior Interventions in Multi-device Environments

由于照顾者负担的增加和远程健康行为监测方面的挑战，拟议的研究将使目标人群能够对动态变化的健康行为做出有效的辅助干预。为了有效和有影响力，辅助机制需要捕捉和回应人类微妙和不断变化的背景。然而，由于人类行为的复杂性以及不断变化的物理、社会和环境背景，学习人类行为具有挑战性。最近，随着用户采用各种设备来帮助他们监控与健康相关的参数，可穿戴设备的出现填补了这一空白。考虑到它们的无处不在，可穿戴设备处于理想的位置，可以提供旨在改善用户健康结果的有说服力的内容。然而，即使用户的环境和测量他们行为的设备随着时间的推移而变化，也需要一种全面的方法来推断人类的健康行为。这项拟议的研究有可能通过持续、廉价和不引人注目地捕获和响应细粒度的行为信息来改变人类健康结果。这种人在环路系统将促进健康应用的快速发展，为针对不同健康人群的适应性和个性化干预提供基础，以实现对所有人的辅助护理。本研究的目标是开发人在环路网络物理系统，该系统可以模拟人类行为，并在稀疏的多设备环境中实现辅助干预。这项研究将产生：(I)在稀疏的多设备环境中对人体运动进行建模；(Ii)学习运动衍生的行为测量(语言、身体和心理)；(Iii)向人类提供干预并在需要时征求他们的反馈的人在回路中的模型；以及(Iv)针对目标健康人群开发和评估拟议的技术。我们的主要想法是开发新的技术，在稀疏的多设备环境中学习粗粒度和细粒度的人体运动，并从人体运动中推断出身体、语言和心理行为。这最终会提供给人在环路中的CPS模型，以便在正确的时间为目标行为结果提供正确的干预。这项工作的研究成果将被整合到我们的综合教育计划中，并将影响多学科交叉的教育学。该奖项反映了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。

Motivated by the rising caregiver burden and challenges in remote health behavior monitoring, the proposed research will enable effective assistive interventions in response to dynamically changing health behaviors for target populations. To be effective and impactful, assistive mechanisms need to capture and respond to the subtle and changing context of the human. Human behaviors, however, are challenging to learn due to their complexity and the constantly changing physical, social, and environmental context. Recently, wearables have emerged to fill this gap as users are adopting a variety of devices to help them monitor health related parameters. Given their ubiquity, wearables are positioned ideally to deliver persuasive content aimed at improving users’ health outcomes. However, there is a need for a holistic approach to infer human health behaviors, even as the user's context and the devices measuring their behavior vary over time. The proposed research has the potential to transform human health outcomes by capturing and responding to fine-grained behavioral information continuously, inexpensively, and unobtrusively. This human-in-the-loop system will facilitate rapid development of Health applications by providing the foundations for using adaptive and personalized interventions for diverse health populations to enable assistive care for all.The objective of this research is to develop human-in-the-loop cyber-physical systems that can model human behaviors and enable assistive interventions in sparse multi-device environments. This research will engender: (i) modeling human motion in sparse multi-device environments; (ii) learning motion-derived behavioral measures (verbal, physical, and psychological); (iii) a human-in-the-loop model that delivers interventions to the human and solicits their feedback when needed; and (iv) development and evaluation of the proposed techniques with target health populations. Our key idea is to develop novel techniques for learning coarse and fine-grained human motion in sparse multi-device environments, and infer physical, verbal, and psychological behaviors from human motion. This ultimately feeds into a human-in-the-loop CPS model to deliver the right interventions at the right time for target behavioral outcomes. The research outcomes from this work will be integrated into our comprehensive education plan and will influence pedagogy at the intersection of multiple disciplines.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.