Louis N. Awad, PT, DPT, PhD - US grants

We propose to develop a novel, distributed sensor platform that continuously assesses movement in the background of one's life with the goal of helping people age in place and avoid expensive and lengthy hospitalizations. On the one hand, the platform will combine measurements from a heterogeneous and complementary set of inertial, physiological , and vision sensors with state-of-the-art techniques from robotics and machine learning, together with clinically informed dynamic models of human motion. On the other hand, the platform will use these data to target the prompt detection of the mobility deficits that often precipitate the onset of frailty, with the goal of facilitating personalized caregiver alerts if a decline in functional status is detected. Moreover, the platform will provide context-aware control inputs to facilitate unconstrained use of powered assistive technologies in the home. This project has three main thrusts: assessment, control, and home intervention. In the assessment component, our work will extend well-proven techniques of multi-modal sensor fusion for mapping and localization of robots to home-based movement monitoring and intervention. The novelty of this work lies in the tight integration of machine learning modules for real-time activity recognition and movement dysfunction diagnosis. In the control component, our work will push the boundaries of what is possible with current powered assistive devices by developing novel control mechanisms that take advantage of the new capabilities provided by the estimation component (e.g., adapting control to changes in activities and environmental contexts). In the home intervention component, we will collect data that will refine the sensing and control algorithms and involve caregivers in alerts. A patient-in-the-loop development approach will be utilized where domain-informed protocols will generate the data necessary to train and evaluate our system, both in the clinic and in the home. By enabling timely detection of movement dysfunction and facilitating unconstrained use of powered assistive technologies, this foundational technology has paradigm-disrupting potential to prevent the onset of frailty and alter the treatment options for frail individuals. In parallel, the estimation component of the system could be used in clinical settings to automate and standardize time-intensive and highly subjective functional movement assessments, allowing more accurate diagnoses while freeing clinicians for other important tasks. RELEVANCE (See instructions): Frail older adults constitute the sickest, most expensive, and fastest growing segment of the US population. Home-based technologies that facilitate aging in place and reduce high-cost, hospital- and institution-based interventions are desperately needed. Our proposed distributed sensor platform has the potential to address this need by enabling the timely detection of the mobility deficits that often precipitate the onset of frailty and proactive caregiver and technological interventions that can delay, or prevent, mobility loss.