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Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment

Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called “wearables”) are powerful too...

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Autores principales: Pham, Minh H., Warmerdam, Elke, Elshehabi, Morad, Schlenstedt, Christian, Bergeest, Lu-Marie, Heller, Maren, Haertner, Linda, Ferreira, Joaquim J., Berg, Daniela, Schmidt, Gerhard, Hansen, Clint, Maetzler, Walter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6104484/
https://www.ncbi.nlm.nih.gov/pubmed/30158894
http://dx.doi.org/10.3389/fneur.2018.00652
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author Pham, Minh H.
Warmerdam, Elke
Elshehabi, Morad
Schlenstedt, Christian
Bergeest, Lu-Marie
Heller, Maren
Haertner, Linda
Ferreira, Joaquim J.
Berg, Daniela
Schmidt, Gerhard
Hansen, Clint
Maetzler, Walter
author_facet Pham, Minh H.
Warmerdam, Elke
Elshehabi, Morad
Schlenstedt, Christian
Bergeest, Lu-Marie
Heller, Maren
Haertner, Linda
Ferreira, Joaquim J.
Berg, Daniela
Schmidt, Gerhard
Hansen, Clint
Maetzler, Walter
author_sort Pham, Minh H.
collection PubMed
description Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called “wearables”) are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment. Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90–180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation. Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001). Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed.
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spelling pubmed-61044842018-08-29 Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment Pham, Minh H. Warmerdam, Elke Elshehabi, Morad Schlenstedt, Christian Bergeest, Lu-Marie Heller, Maren Haertner, Linda Ferreira, Joaquim J. Berg, Daniela Schmidt, Gerhard Hansen, Clint Maetzler, Walter Front Neurol Neurology Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called “wearables”) are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment. Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90–180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation. Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001). Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed. Frontiers Media S.A. 2018-08-10 /pmc/articles/PMC6104484/ /pubmed/30158894 http://dx.doi.org/10.3389/fneur.2018.00652 Text en Copyright © 2018 Pham, Warmerdam, Elshehabi, Schlenstedt, Bergeest, Heller, Haertner, Ferreira, Berg, Schmidt, Hansen and Maetzler. http://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 Neurology
Pham, Minh H.
Warmerdam, Elke
Elshehabi, Morad
Schlenstedt, Christian
Bergeest, Lu-Marie
Heller, Maren
Haertner, Linda
Ferreira, Joaquim J.
Berg, Daniela
Schmidt, Gerhard
Hansen, Clint
Maetzler, Walter
Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment
title Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment
title_full Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment
title_fullStr Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment
title_full_unstemmed Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment
title_short Validation of a Lower Back “Wearable”-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment
title_sort validation of a lower back “wearable”-based sit-to-stand and stand-to-sit algorithm for patients with parkinson's disease and older adults in a home-like environment
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6104484/
https://www.ncbi.nlm.nih.gov/pubmed/30158894
http://dx.doi.org/10.3389/fneur.2018.00652
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