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

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 specifi...

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Autores principales: Nouriani, Ali, Jonason, Alec, Sabal, Luke T., Hanson, Jacob T., Jean, James N., Lisko, Thomas, Reid, Emma, Moua, Yeng, Rozeboom, Shane, Neverman, Kaiser, Stowe, Casey, Rajamani, Rajesh, McGovern, Robert A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995757/
https://www.ncbi.nlm.nih.gov/pubmed/36909945
http://dx.doi.org/10.3389/fnagi.2023.1117802
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author Nouriani, Ali
Jonason, Alec
Sabal, Luke T.
Hanson, Jacob T.
Jean, James N.
Lisko, Thomas
Reid, Emma
Moua, Yeng
Rozeboom, Shane
Neverman, Kaiser
Stowe, Casey
Rajamani, Rajesh
McGovern, Robert A.
author_facet Nouriani, Ali
Jonason, Alec
Sabal, Luke T.
Hanson, Jacob T.
Jean, James N.
Lisko, Thomas
Reid, Emma
Moua, Yeng
Rozeboom, Shane
Neverman, Kaiser
Stowe, Casey
Rajamani, Rajesh
McGovern, Robert A.
author_sort Nouriani, Ali
collection PubMed
description 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.
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spelling pubmed-99957572023-03-10 Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients Nouriani, Ali Jonason, Alec Sabal, Luke T. Hanson, Jacob T. Jean, James N. Lisko, Thomas Reid, Emma Moua, Yeng Rozeboom, Shane Neverman, Kaiser Stowe, Casey Rajamani, Rajesh McGovern, Robert A. Front Aging Neurosci Aging Neuroscience 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. Frontiers Media S.A. 2023-02-23 /pmc/articles/PMC9995757/ /pubmed/36909945 http://dx.doi.org/10.3389/fnagi.2023.1117802 Text en Copyright © 2023 Nouriani, Jonason, Sabal, Hanson, Jean, Lisko, Reid, Moua, Rozeboom, Neverman, Stowe, Rajamani and McGovern. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Aging Neuroscience
Nouriani, Ali
Jonason, Alec
Sabal, Luke T.
Hanson, Jacob T.
Jean, James N.
Lisko, Thomas
Reid, Emma
Moua, Yeng
Rozeboom, Shane
Neverman, Kaiser
Stowe, Casey
Rajamani, Rajesh
McGovern, Robert A.
Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients
title Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients
title_full Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients
title_fullStr Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients
title_full_unstemmed Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients
title_short Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients
title_sort real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995757/
https://www.ncbi.nlm.nih.gov/pubmed/36909945
http://dx.doi.org/10.3389/fnagi.2023.1117802
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