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Data-Driven Prediction of Freezing of Gait Events From Stepping Data
Freezing of gait (FoG) is typically a symptom of advanced Parkinson's disease (PD) that negatively influences the quality of life and is often resistant to pharmacological interventions. Novel treatment options that make use of auditory or sensory cues might be optimized by prediction of freezi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8757792/ https://www.ncbi.nlm.nih.gov/pubmed/35047881 http://dx.doi.org/10.3389/fmedt.2020.581264 |
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author | Parakkal Unni, Midhun Menon, Prathyush P. Livi, Lorenzo Wilson, Mark R. Young, William R. Bronte-Stewart, Helen M. Tsaneva-Atanasova, Krasimira |
author_facet | Parakkal Unni, Midhun Menon, Prathyush P. Livi, Lorenzo Wilson, Mark R. Young, William R. Bronte-Stewart, Helen M. Tsaneva-Atanasova, Krasimira |
author_sort | Parakkal Unni, Midhun |
collection | PubMed |
description | Freezing of gait (FoG) is typically a symptom of advanced Parkinson's disease (PD) that negatively influences the quality of life and is often resistant to pharmacological interventions. Novel treatment options that make use of auditory or sensory cues might be optimized by prediction of freezing events. These predictions might help to trigger external sensory cues—shown to improve walking performance—when behavior is changed in a manner indicative of an impending freeze (i.e., when the user needs it the most), rather than delivering cue information continuously. A data-driven approach is proposed for predicting freezing events using Random Forrest (RF), Neural Network (NN), and Naive Bayes (NB) classifiers. Vertical forces, sampled at 100 Hz from a force platform were collected from 9 PD subjects as they stepped in place until they at least had one freezing episode or for 90 s. The F1 scores of RF/NN/NB algorithms were computed for different IL (input to the machine learning algorithm), and GL (how early the freezing event is predicted). A significant negative correlation between the F1 scores and GL, highlighting the difficulty of early detection is found. The IL that maximized the F1 score is approximately equal to 1.13 s. This indicates that the physiological (and therefore neurological) changes leading to freezing take effect at-least one step before the freezing incident. Our algorithm has the potential to support the development of devices to detect and then potentially prevent freezing events in people with Parkinson's which might occur if left uncorrected. |
format | Online Article Text |
id | pubmed-8757792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87577922022-01-18 Data-Driven Prediction of Freezing of Gait Events From Stepping Data Parakkal Unni, Midhun Menon, Prathyush P. Livi, Lorenzo Wilson, Mark R. Young, William R. Bronte-Stewart, Helen M. Tsaneva-Atanasova, Krasimira Front Med Technol Medical Technology Freezing of gait (FoG) is typically a symptom of advanced Parkinson's disease (PD) that negatively influences the quality of life and is often resistant to pharmacological interventions. Novel treatment options that make use of auditory or sensory cues might be optimized by prediction of freezing events. These predictions might help to trigger external sensory cues—shown to improve walking performance—when behavior is changed in a manner indicative of an impending freeze (i.e., when the user needs it the most), rather than delivering cue information continuously. A data-driven approach is proposed for predicting freezing events using Random Forrest (RF), Neural Network (NN), and Naive Bayes (NB) classifiers. Vertical forces, sampled at 100 Hz from a force platform were collected from 9 PD subjects as they stepped in place until they at least had one freezing episode or for 90 s. The F1 scores of RF/NN/NB algorithms were computed for different IL (input to the machine learning algorithm), and GL (how early the freezing event is predicted). A significant negative correlation between the F1 scores and GL, highlighting the difficulty of early detection is found. The IL that maximized the F1 score is approximately equal to 1.13 s. This indicates that the physiological (and therefore neurological) changes leading to freezing take effect at-least one step before the freezing incident. Our algorithm has the potential to support the development of devices to detect and then potentially prevent freezing events in people with Parkinson's which might occur if left uncorrected. Frontiers Media S.A. 2020-11-20 /pmc/articles/PMC8757792/ /pubmed/35047881 http://dx.doi.org/10.3389/fmedt.2020.581264 Text en Copyright © 2020 Parakkal Unni, Menon, Livi, Wilson, Young, Bronte-Stewart and Tsaneva-Atanasova. 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 | Medical Technology Parakkal Unni, Midhun Menon, Prathyush P. Livi, Lorenzo Wilson, Mark R. Young, William R. Bronte-Stewart, Helen M. Tsaneva-Atanasova, Krasimira Data-Driven Prediction of Freezing of Gait Events From Stepping Data |
title | Data-Driven Prediction of Freezing of Gait Events From Stepping Data |
title_full | Data-Driven Prediction of Freezing of Gait Events From Stepping Data |
title_fullStr | Data-Driven Prediction of Freezing of Gait Events From Stepping Data |
title_full_unstemmed | Data-Driven Prediction of Freezing of Gait Events From Stepping Data |
title_short | Data-Driven Prediction of Freezing of Gait Events From Stepping Data |
title_sort | data-driven prediction of freezing of gait events from stepping data |
topic | Medical Technology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8757792/ https://www.ncbi.nlm.nih.gov/pubmed/35047881 http://dx.doi.org/10.3389/fmedt.2020.581264 |
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