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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-9995757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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