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An automated approach for video-based motor assessment in Parkinson's disease

Award Number: R21HD110686
ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/01/2023
PERIOD OF PERFORMANCE END DATE: 04/30/2026
AWARD TERMINATION DATE: 06/10/2025

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An automated approach for video-based motor assessment in Parkinson's disease - Project Summary Parkinson’s disease (PD) is neurodegenerative movement disorder that causes a series of motor deficits. Due to the degenerative nature of PD, effective management of motor symptoms requires frequent and accurate motor assessment. Current approaches for PD motor assessment rely on subjectively rated clinical scales or research-grade equipment that is expensive or data-limited. These limitations significantly restrict the ability to perform frequent, granular assessments of motor function and highlight an important need for new approaches that enable objective motor assessments in any setting with minimal costs of time, money, or effort. Here, we propose an automated, video-based approach to PD motor assessment by using a state of the art pose estimation algorithm. Briefly, pose estimation is an emerging technology that is capable of quantitatively tracking human movement from simple digital videos recorded using common household devices (e.g., smartphones, tablets). The development of pose estimation algorithms for tracking human movement has progressed rapidly in the artificial intelligence community, and there is now significant untapped potential for leveraging this technology to perform rapid, automated motor assessments directly in home or clinic. In Aim 1.1, we will test a pose estimation approach for gait assessment in persons with PD. Gait dysfunction is common in PD, but it remains difficult to assess gait quantitatively in the home or clinic. Gait assessment is not represented adequately in clinical scales of motor function in PD, and there are no good methods for objective measurement of whole-body gait kinematics that can be used directly in the home or clinic. We will address this need by using a video-based pose estimation workflow to perform gait assessments in persons with PD. We will compare these results to motion capture measurements to examine how well the pose estimation approach approximates ground-truth measurements. In Aim 1.2, we will use our pose estimation approach to track repetitive movements in persons with PD. PD often causes difficulty in performing repetitive movements (e.g., finger tapping); accordingly, assessment of repetitive movements constitutes a significant portion of the MDS-Unified Parkinson’s Disease Rating Scale (the standard clinical scale for motor assessment in PD). This assessment is done through subjective human ratings; here, we will test an automated, objective, video-based approach for tracking repetitive movements in persons with PD and again compare to ground-truth motion capture measurements. In Aim 2, we will compare and contrast motor deficits identified by pose estimation and human assessment. Subjective visual assessment is the most common means of assessing motor function in PD, but computer vision approaches (e.g., pose estimation) have significant potential to automate and objectivize this process. Here, we will compare how well deficits in gait and repetitive movements detected by our pose estimation approach align with neurologist ratings of these tasks on the Unified Parkinson’s Disease Rating Scale.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $0 )
2026	2024	HUGO W. MOSER RESEARCH INSTITUTE AT KENNEDY KRIEGER, INC.	707 N BROADWAY	BALTIMORE	MD	21205	BALTIMORE CITY	USA	Child Health and Human Development Extramural Research	000	2	5/28/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $0 )
2025	2024	HUGO W. MOSER RESEARCH INSTITUTE AT KENNEDY KRIEGER, INC.	707 N BROADWAY	BALTIMORE	MD	21205	BALTIMORE CITY	USA	Child Health and Human Development Extramural Research	000	2	6/9/2025	TERMINATION	$0
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $308,063 )
2024	2024	HUGO W. MOSER RESEARCH INSTITUTE AT KENNEDY KRIEGER, INC.	707 N BROADWAY	BALTIMORE	MD	21205	BALTIMORE CITY	USA	Child Health and Human Development Extramural Research	001	2	8/27/2024	SUPPLEMENT FOR EXPANSION	$64,837
2024	2024	HUGO W. MOSER RESEARCH INSTITUTE AT KENNEDY KRIEGER, INC.	707 N BROADWAY	BALTIMORE	MD	21205	BALTIMORE CITY	USA	Child Health and Human Development Extramural Research	000	2	5/6/2024	NON-COMPETING CONTINUATION	$243,226
														Subtotal = $308,063

Issue Date FY: 2023 ( Subtotal = $202,725 )
2023	2023	HUGO W. MOSER RESEARCH INSTITUTE AT KENNEDY KRIEGER, INC.	707 N BROADWAY	BALTIMORE	MD	21205	BALTIMORE CITY	USA	Child Health and Human Development Extramural Research	000	1	4/21/2023	NEW	$202,725
														Subtotal = $202,725

Grand Total All Awards = $510,788

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An automated approach for video-based motor assessment in Parkinson's disease

Award Number: R21HD110686

ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 05/01/2023

PERIOD OF PERFORMANCE END DATE: 04/30/2026

AWARD TERMINATION DATE: 06/10/2025

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