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Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health
Movement health is understanding our body’s ability to perform movements during activities of daily living such as lifting, reaching, and bending. The benefits of improved movement health have long been recognized and are wide-ranging from improving athletic performance to helping ease of performing...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445016/ https://www.ncbi.nlm.nih.gov/pubmed/36065060 http://dx.doi.org/10.1038/s41746-022-00684-9 |
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author | Fanton, Michael Harari, Yaar Giffhorn, Matthew Lynott, Allie Alshan, Eli Mendley, Jonathan Czerwiec, Madeline Macaluso, Rebecca Ideses, Ianir Oks, Eduard Jayaraman, Arun |
author_facet | Fanton, Michael Harari, Yaar Giffhorn, Matthew Lynott, Allie Alshan, Eli Mendley, Jonathan Czerwiec, Madeline Macaluso, Rebecca Ideses, Ianir Oks, Eduard Jayaraman, Arun |
author_sort | Fanton, Michael |
collection | PubMed |
description | Movement health is understanding our body’s ability to perform movements during activities of daily living such as lifting, reaching, and bending. The benefits of improved movement health have long been recognized and are wide-ranging from improving athletic performance to helping ease of performing simple tasks, but only recently has this concept been put into practice by clinicians and quantitatively studied by researchers. With digital health and movement monitoring becoming more ubiquitous in society, smartphone applications represent a promising avenue for quantifying, monitoring, and improving the movement health of an individual. In this paper, we validate Halo Movement, a movement health assessment which utilizes the front-facing camera of a smartphone and applies computer vision and machine learning algorithms to quantify movement health and its sub-criteria of mobility, stability, and posture through a sequence of five exercises/activities. On a diverse cohort of 150 participants of various ages, body types, and ability levels, we find moderate to strong statistically significant correlations between the Halo Movement assessment overall score, metrics from sensor-based 3D motion capture, and scores from a sequence of 13 standardized functional movement tests. Further, the smartphone assessment is able to differentiate regular healthy individuals from professional movement athletes (e.g., dancers, cheerleaders) and from movement impaired participants, with higher resolution than that of existing functional movement screening tools and thus may be more appropriate than the existing tests for quantifying functional movement in able-bodied individuals. These results support using Halo Movement’s overall score as a valid assessment of movement health. |
format | Online Article Text |
id | pubmed-9445016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94450162022-09-07 Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health Fanton, Michael Harari, Yaar Giffhorn, Matthew Lynott, Allie Alshan, Eli Mendley, Jonathan Czerwiec, Madeline Macaluso, Rebecca Ideses, Ianir Oks, Eduard Jayaraman, Arun NPJ Digit Med Article Movement health is understanding our body’s ability to perform movements during activities of daily living such as lifting, reaching, and bending. The benefits of improved movement health have long been recognized and are wide-ranging from improving athletic performance to helping ease of performing simple tasks, but only recently has this concept been put into practice by clinicians and quantitatively studied by researchers. With digital health and movement monitoring becoming more ubiquitous in society, smartphone applications represent a promising avenue for quantifying, monitoring, and improving the movement health of an individual. In this paper, we validate Halo Movement, a movement health assessment which utilizes the front-facing camera of a smartphone and applies computer vision and machine learning algorithms to quantify movement health and its sub-criteria of mobility, stability, and posture through a sequence of five exercises/activities. On a diverse cohort of 150 participants of various ages, body types, and ability levels, we find moderate to strong statistically significant correlations between the Halo Movement assessment overall score, metrics from sensor-based 3D motion capture, and scores from a sequence of 13 standardized functional movement tests. Further, the smartphone assessment is able to differentiate regular healthy individuals from professional movement athletes (e.g., dancers, cheerleaders) and from movement impaired participants, with higher resolution than that of existing functional movement screening tools and thus may be more appropriate than the existing tests for quantifying functional movement in able-bodied individuals. These results support using Halo Movement’s overall score as a valid assessment of movement health. Nature Publishing Group UK 2022-09-06 /pmc/articles/PMC9445016/ /pubmed/36065060 http://dx.doi.org/10.1038/s41746-022-00684-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Fanton, Michael Harari, Yaar Giffhorn, Matthew Lynott, Allie Alshan, Eli Mendley, Jonathan Czerwiec, Madeline Macaluso, Rebecca Ideses, Ianir Oks, Eduard Jayaraman, Arun Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health |
title | Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health |
title_full | Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health |
title_fullStr | Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health |
title_full_unstemmed | Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health |
title_short | Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health |
title_sort | validation of amazon halo movement: a smartphone camera-based assessment of movement health |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445016/ https://www.ncbi.nlm.nih.gov/pubmed/36065060 http://dx.doi.org/10.1038/s41746-022-00684-9 |
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