Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients

Nouriani, Ali and Jonason, Alec and Sabal, Luke T. and Hanson, Jacob T. and Jean, James N. and Lisko, Thomas and Reid, Emma and Moua, Yeng and Rozeboom, Shane and Neverman, Kaiser and Stowe, Casey and Rajamani, Rajesh and McGovern, Robert A. (2023) Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients. Frontiers in Aging Neuroscience, 15. ISSN 1663-4365

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Abstract

The use of wearable sensors in movement disorder patients such as Parkinson’s disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects. The algorithm is then verified with ground truth by collecting video footage of our patients wearing the sensors at home. Our activity recognition algorithm showed >95% sensitivity in detection of activities. Extracted features from our home monitoring system showed significantly better correlation (~69%) with prospectively measured fall frequency of our subjects compared to the standard clinical tests (~30%) or other quantitative gait metrics used in past studies when attempting to predict future falls over 1 year of prospective follow-up. Although detecting near-falls at home is difficult, our proposed model suggests that near-fall frequency is the most predictive criterion in fall detection through correlation analysis and fitting regression models.

Item Type: Article
Subjects: AP Academic Press > Medical Science
Depositing User: Unnamed user with email support@apacademicpress.com
Date Deposited: 04 Sep 2024 03:48
Last Modified: 04 Sep 2024 03:48
URI: http://info.openarchivespress.com/id/eprint/1817

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