CAREER: Autonomous Wearable Computing for Personalized Healthcare

可穿戴设备将通过自动化经济、客观、持续、实时和远程的健康监测和干预来改变健康和健康。这些技术利用机器学习算法来检测重要的健康事件并预测即将到来的医疗并发症。然而，目前这些系统的机器学习算法是基于传感器数据设计的，这些传感器数据是在受控环境中收集并标记/注释的，例如实验室和诊所。这种数据收集、数据注释和算法训练的过程对可穿戴系统的可扩展性造成了极大的障碍，因为(1)收集和标记足够大量的传感器数据是一项耗时、费力、昂贵且往往不可行的任务；(2)可穿戴设备部署在终端用户的高度动态环境中，其物理、行为、社会和环境环境经历了一致的变化。这些变化导致在受控环境中训练的机器学习算法的准确性急剧下降。因此，重要的是开发可自主重新配置的机器学习算法，如可穿戴传感器、使用它们的设置以及它们的配置变化。这个项目引入了计算自主性作为一种总体解决方案，用于在高度动态、不可预测和不可控制的环境中训练准确的机器学习算法，而无需人工监督。这项工作的成功完成将使未来的可穿戴设备能够就地自主学习，在野外可靠地操作，并自动适应用户不断变化的环境。该项目将通过以下研究目标为基于可穿戴设备的健康监测和干预建立计算自主性的基础：(1)通过设计跨主题、跨上下文、跨平台和跨通道的传感器数据映射的组合优化方法，研究新环境下基于在不同环境中收集的已标记传感器数据的自动和自主标记的方法；(2)开发非参数标签精化算法，基于从另一个潜在的不可靠和异质的传感器获得的不确定和零星的知识，在新的环境下可靠地推断标签；(3)探索训练机器学习算法的方法，这些算法对源传感器的未知参数具有健壮性，并适应新设置的动态变化的信号属性；以及(4)通过实验室实验和野外用户研究来验证所开发的算法和工具。这一跨学科项目不仅将解决开发高性能可穿戴系统的技术挑战，还将使各种人群的实际监控成为可能。这项工作对进行高精度的慢性病管理和基于可穿戴设备的消费者应用程序的可用性具有重大而广泛的影响。这有可能导致围绕计算自主的概念开发产品，并将其用于健康管理的自动化，以及尚未设想的应用程序。这项工作的跨学科性质将为综合研究和教育提供独特的机会。为此，教育目标将侧重于开发一项新的大使计划，以提高未被充分代表的少数族裔社区大学生对科学、技术、工程和数学(STEM)职业，特别是对计算机科学和工程职业的兴趣，开发一项新的赞助人计划，通过学生和家长接触基于可穿戴设备的健康监测研究，提高未被充分代表的少数族裔学生的留存能力，让本科生参与研究，并建立基于计算自主的跨学科研究课程。在这个项目过程中产生的所有数据，包括设计方法、软件算法和工具、实验数据、出版物和课程，都将在http://epsl.eecs.wsu.edu/.上公开提供这些数据将存储和托管在本地服务器上，并在外部公共网络服务器上复制。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。

Wearables are poised to transform health and wellness through automation of cost-effective, objective, continuous, real-time, and remote health monitoring and interventions. These technologies utilize machine learning algorithms to detect important health events and to predict impending medical complications. Currently, however, machine learning algorithms for these systems are designed based on sensor data that are collected and labeled/annotated in controlled environments such as laboratory settings and clinics. This process of data collection, data annotation, and algorithm training has created great impediments to scalability of wearable systems because (1) collecting and labeling sufficiently large amounts of sensor data is a time consuming, labor-intensive, expensive, and often infeasible task; and (2) wearables are deployed in highly dynamic environments of the end-users whose physical, behavioral, social, and environmental context undergo consistent changes. Such changes result in drastic decline in the accuracy of the machine learning algorithms trained in controlled environments. Therefore, it is important to develop autonomously reconfigurable machine learning algorithms as wearable sensors, settings in which they are utilized, and their configuration changes. This project introduces computational autonomy as an overarching solution for training accurate machine learning algorithms, without human supervision, in highly dynamic, unpredictable, and uncontrolled settings. The successful conclusion of this work will enable future wearables to learn in-situ autonomously, operate in-the-wild reliably, and adapt to the changing context of their users automatically. This project will develop foundations of computational autonomy for wearable-based health monitoring and interventions through the following research objectives: (1) investigating methods of automatic and autonomous labeling of sensor data in a new setting based on labeled sensor data collected in a different setting by designing combinational optimization methodologies for cross-subject, cross-context, cross-platform, and cross-modality sensor data mapping; (2) developing non-parametric label refinement algorithms to reliably infer labels in a new setting based on uncertain and sporadic knowledge obtained from another, potentially unreliable and heterogeneous, sensor; (3) exploring methodologies for training machine learning algorithms that are robust to unknown parameters of a source sensor and adaptive to dynamically changing signal attributes of the new setting; and (4) validating the developed algorithms and tools through both in-lab experiments and in-the-wild user studies.This interdisciplinary project will not only address the technical challenges in developing highly performant wearable systems but will also enable actual monitoring of a variety of populations. The work has major broader impacts on conducting high-precision chronic disease management and on the availability of wearable-based consumer applications. This has the potential to lead to the development of products around the concept of computational autonomy and its use in automation of health management, as well as, applications yet to be envisioned. The interdisciplinary nature of this work will provide unique opportunities for integrated research and education. To this end, the educational objectives will focus on developing a new ambassador program to increase interest of underrepresented minority community college students in Science, Technology, Engineering and Math (STEM) careers in general and in computer science and engineering careers in particular, developing a novel patron program to improve retention of transferred underrepresented minority students through student and parental exposure to wearable-based health monitoring research, engaging undergraduate students in research, and establishment of an interdisciplinary research-based curriculum on computational autonomy. All the data produced over the course of this project, including design methodologies, software algorithms and tools, experimental data, publications, and curriculum will be made publicly available at http://epsl.eecs.wsu.edu/. The data will be stored and hosted on local servers and replicated on external public web servers.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.