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Assessing inertial measurement unit locations for freezing of gait detection and patient preference
BACKGROUND: Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842967/ https://www.ncbi.nlm.nih.gov/pubmed/35152881 http://dx.doi.org/10.1186/s12984-022-00992-x |
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author | O’Day, Johanna Lee, Marissa Seagers, Kirsten Hoffman, Shannon Jih-Schiff, Ava Kidziński, Łukasz Delp, Scott Bronte-Stewart, Helen |
author_facet | O’Day, Johanna Lee, Marissa Seagers, Kirsten Hoffman, Shannon Jih-Schiff, Ava Kidziński, Łukasz Delp, Scott Bronte-Stewart, Helen |
author_sort | O’Day, Johanna |
collection | PubMed |
description | BACKGROUND: Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. METHODS: Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. RESULTS: The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. CONCLUSIONS: Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-022-00992-x. |
format | Online Article Text |
id | pubmed-8842967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88429672022-02-16 Assessing inertial measurement unit locations for freezing of gait detection and patient preference O’Day, Johanna Lee, Marissa Seagers, Kirsten Hoffman, Shannon Jih-Schiff, Ava Kidziński, Łukasz Delp, Scott Bronte-Stewart, Helen J Neuroeng Rehabil Research BACKGROUND: Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. METHODS: Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. RESULTS: The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. CONCLUSIONS: Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-022-00992-x. BioMed Central 2022-02-13 /pmc/articles/PMC8842967/ /pubmed/35152881 http://dx.doi.org/10.1186/s12984-022-00992-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research O’Day, Johanna Lee, Marissa Seagers, Kirsten Hoffman, Shannon Jih-Schiff, Ava Kidziński, Łukasz Delp, Scott Bronte-Stewart, Helen Assessing inertial measurement unit locations for freezing of gait detection and patient preference |
title | Assessing inertial measurement unit locations for freezing of gait detection and patient preference |
title_full | Assessing inertial measurement unit locations for freezing of gait detection and patient preference |
title_fullStr | Assessing inertial measurement unit locations for freezing of gait detection and patient preference |
title_full_unstemmed | Assessing inertial measurement unit locations for freezing of gait detection and patient preference |
title_short | Assessing inertial measurement unit locations for freezing of gait detection and patient preference |
title_sort | assessing inertial measurement unit locations for freezing of gait detection and patient preference |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842967/ https://www.ncbi.nlm.nih.gov/pubmed/35152881 http://dx.doi.org/10.1186/s12984-022-00992-x |
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