CRII: CSR: Multi-View Learning Solutions for Next-Generation Computationally-Autonomous Wearables

Washington State University, Pullman WA

Investigators

Abstract

Wearables have emerged as a revolutionary technology for many new applications in healthcare, fitness, and human-centered Internet-of-Things (IoT). Computational algorithms, including machine learning and signal processing techniques, are often used to extract valuable information from wearable sensor data continuously and in real-time. These algorithms, however, need to be retrained upon any changes in configuration of the system, such as addition/removal of a sensor to/from the network, sensor displacement/misplacement, sensor upgrade, adoption of the system by new users, and changes in physical and behavioral status of the user. Retraining of the computational algorithms requires collecting sufficient amount of labeled training data, a time consuming, labor-intensive, and expensive process that limits scalability and sustainability of wearable technologies. The goal of this research is to enable automatic reconfiguration of the computational algorithms without need for collecting new labeled data. This proposed research aims to design, develop and validate algorithms and tools for self-configuration of wearables through two overarching research trusts. First, this project investigates synchronous multi-view learning solutions for scenarios where source and target views observe the phenomena of interest simultaneously. In the synchronous learning, direct associations between observations made by the source view and those of the target view are established through context-sensitive learning processes that take the properties of physiological monitoring and human body into account for transfer learning purposes. Second, this research develops asynchronous multi-view learning algorithms to allow for automatic knowledge transfer even in absence of synchronous measurements in the source and target views. The asynchronous learning research devises feature mapping, instance transformation, and data labeling techniques to determine how data instances of the target view are associated with those of the source view while taking into consideration physical and contextual attributes of the user. This project will potentially result in highly sustainable and scalable wearables capable to self-monitor and self-configure in highly dynamic and uncontrolled environments. The true realization of computationally autonomous wearables will allow for conducting high-precision chronic disease management and contribute to availability of new wearable-based consumer applications. This can lead to the development of products and business around the concept of human-centered IoT and their use in automation of health management and many applications that are currently infeasible.