Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Great strides have been made within sensor fusion of wearable devices in recent years with the advent of smartphones and smartwatches/bands and their consumer applications. Accuracy and scope for automatic interpretation of a variety of user action detection and classification problems for these devices have improved dramatically. Progress has been driven by new sensor technologies, novel problem definitions and representations, access to volumes of multiple sensor data source types, and the resurgence of powerful signal processing and machine learning algorithms. However, current algorithms lack the capability to produce accurate data that permits detailed health understanding in the real-life setting and transforming such data into meaningful medical outcomes. Health-tracking wearable devices in clinical trials face significant hurdles and challenges that must be overcome in order for them to be adopted and used in healthcare.

The long-term goal of the proposed research program is to develop fundamental and principled approaches for fine-grained understanding of complex human activity from multi-modal health-tracking wearable devices for personalized, predictive, preventive, and precision healthcare Internet of Things (IoT). Our short-term goal is to develop automatic activity detection/classification/assessment algorithms for wrist-based inertial wearable devices that are targeted for accurate and continuous health monitoring of free-living older adults (our targeted domain). To achieve this, we propose five specific short-term objectives: (i) sensor fusion for health-tracking wearables: orientation estimation, (ii) physical activity classification for health-tracking wearables: deep learning, (iii) activity-based adaptive/dynamic power management for health-tracking wearables, (iv) physical mobility assessment using health-tracking wearables: walking speed estimation, and (v) physical activity energy expenditure estimation using health-tracking wearables. These algorithms that we develop in this research will enable data-rich and multi-modal health-tracking wearables to produce the necessary data accuracy and interpretation in order for them to be adopted and used in elder care (short-term) and other domains (long-term).

We are currently in the very earliest stages of medical grade, health-tracking, advanced wearables and the time frame could be accelerated by the proposed developments. The work proposed will facilitate a new generation of wearables that are reliable and accurate enough for realization of healthcare IoT. It will also enable early inroads for these devices being employed in clinical trials and meeting FDA approvals.