Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments
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Publication Date: | 2021 |
Other Authors: | , , , , , , , , , |
Format: | Article |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10451/49396 |
Summary: | © The Author(s). 2021 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 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/. The Creative Commons Public Domain Dedication waiver (http://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. |
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Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environmentsParkinson’s diseaseGait analysisPathological gaitWearable devicesSmartphoneKinematics© The Author(s). 2021 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 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/. The Creative Commons Public Domain Dedication waiver (http://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.Background: Gait impairments are among the most common and impactful symptoms of Parkinson’s disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. Objective: We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. Methods: Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. Results: Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. Conclusions: We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.Springer NatureRepositório da Universidade de LisboaAlberto, SaraCabral, SílviaProença, JoãoPona-Ferreira, FilipaLeitão, MarianaBouça-Machado, RaquelAzevedo Kauppila, LindaVeloso, AntónioCosta, Rui M.Ferreira, Joaquim JMatias, Ricardo2021-09-02T10:57:32Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/49396engBMC Neurol. 2021 Aug 28;21(1):33110.1186/s12883-021-02354-x1471-2377info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T14:36:48Zoai:repositorio.ulisboa.pt:10451/49396Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T03:17:00.622140Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments |
title |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments |
spellingShingle |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments Alberto, Sara Parkinson’s disease Gait analysis Pathological gait Wearable devices Smartphone Kinematics |
title_short |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments |
title_full |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments |
title_fullStr |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments |
title_full_unstemmed |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments |
title_sort |
Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments |
author |
Alberto, Sara |
author_facet |
Alberto, Sara Cabral, Sílvia Proença, João Pona-Ferreira, Filipa Leitão, Mariana Bouça-Machado, Raquel Azevedo Kauppila, Linda Veloso, António Costa, Rui M. Ferreira, Joaquim J Matias, Ricardo |
author_role |
author |
author2 |
Cabral, Sílvia Proença, João Pona-Ferreira, Filipa Leitão, Mariana Bouça-Machado, Raquel Azevedo Kauppila, Linda Veloso, António Costa, Rui M. Ferreira, Joaquim J Matias, Ricardo |
author2_role |
author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Alberto, Sara Cabral, Sílvia Proença, João Pona-Ferreira, Filipa Leitão, Mariana Bouça-Machado, Raquel Azevedo Kauppila, Linda Veloso, António Costa, Rui M. Ferreira, Joaquim J Matias, Ricardo |
dc.subject.por.fl_str_mv |
Parkinson’s disease Gait analysis Pathological gait Wearable devices Smartphone Kinematics |
topic |
Parkinson’s disease Gait analysis Pathological gait Wearable devices Smartphone Kinematics |
description |
© The Author(s). 2021 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 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/. The Creative Commons Public Domain Dedication waiver (http://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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-02T10:57:32Z 2021 2021-01-01T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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http://hdl.handle.net/10451/49396 |
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http://hdl.handle.net/10451/49396 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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BMC Neurol. 2021 Aug 28;21(1):331 10.1186/s12883-021-02354-x 1471-2377 |
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Springer Nature |
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Springer Nature |
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