CAREER: Autonomous Wearable Computing for Personalized Healthcare

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.