# SCH: Multimodal,Task-Aware Movement Assessment and Control: Clinic to Home

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2020 · $290,216

## Abstract

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
frail...

## Key facts

- **NIH application ID:** 10019455
- **Project number:** 5R01AG067394-02
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Louis N Awad
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $290,216
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10019455

## Citation

> US National Institutes of Health, RePORTER application 10019455, SCH: Multimodal,Task-Aware Movement Assessment and Control: Clinic to Home (5R01AG067394-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10019455. Licensed CC0.

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