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471 Using Computer Vision and Wearable Devices to Improve Care of Parkinson’s Disease
Abstract   Open access   Peer reviewed

471 Using Computer Vision and Wearable Devices to Improve Care of Parkinson’s Disease

Jacob Simmering, Nandakumar Narayanan and Philip Polgreen
Journal of clinical and translational science, Vol.8(s1), pp.139-139
04/01/2024
DOI: 10.1017/cts.2024.399
PMCID: PMC11035197
url
https://doi.org/10.1017/cts.2024.399View
Published (Version of record) Open Access

Abstract

OBJECTIVES/GOALS: Inexpensive, accurate home monitoring is the standard-of-care in many diseases like hypertension or diabetes; however, it has yet to be widely used in neurodegenerative diseases. We used wearable activity monitors and computer-vision evaluated assessments to estimate Parkinson’s disease (PD)-related disease burden. METHODS/STUDY POPULATION: We recruited 22 people from the University of Iowa Movement Disorders Clinic. Each person completed a standardized set of 3 fine motor tasks using their hands. We recorded a video of this activity, which was evaluated using MediaPipe - an open-source pose classification program from Alphabet - as well as had an nurse-practitioner evaluate the performance on a validated scale (UPDRS). Participants wore a Fitbit Inspire 3 activity tracker at home for the next two weeks. We quantified disease burden using the Parkinson’s Disease Questionnaire 39 - a validated 39-item survey about the intensity of PD-related impairment. Using data from the videos and activity trackers, we estimated 1) the standardized UPDRS assessment of motor impairment and 2) the total PDQ-39 score. RESULTS/ANTICIPATED RESULTS: We found observationally recorded fastest sustained (at least 5 minutes) walking speed was a strong predictor of PDQ-39, explaining over one third of the variability in the measure. Range of motion in the videos was a significant predictor of UPDRS scores; however, was only weakly related to the overall PDQ-39 score. Further processing of the signals from the video, including wavelets and frequency domain analysis, may provide better predictive capabilities. PDQ-39 subscores (e.g., cognition, social support, mobility) will be the subject of further analysis. DISCUSSION/SIGNIFICANCE: Home monitoring has become the standard in other fields because of the better generalizability of home measurements. Improving the detection and evaluation of PD using home monitoring will lead to more timely and accurately changes in medication and less need for clinic visits - especially off levodopa.
 Disease Neurodegenerative Diseases Signal Processing Cognition Diabetes mellitus Levodopa Movement disorders Parkinson's disease Population studies Social interactions

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