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Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease

Freezing-of-gait (FOG) and impaired walking are common features of Parkinson’s disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosens...

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Autores principales: Li, Dongli, Hallack, Andre, Gwilym, Sophie, Li, Dongcheng, Hu, Michele T., Cantley, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10652624/
https://www.ncbi.nlm.nih.gov/pubmed/37974260
http://dx.doi.org/10.1186/s12938-023-01167-y
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author Li, Dongli
Hallack, Andre
Gwilym, Sophie
Li, Dongcheng
Hu, Michele T.
Cantley, James
author_facet Li, Dongli
Hallack, Andre
Gwilym, Sophie
Li, Dongcheng
Hu, Michele T.
Cantley, James
author_sort Li, Dongli
collection PubMed
description Freezing-of-gait (FOG) and impaired walking are common features of Parkinson’s disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosensory cueing systems have the potential to deliver personalised, discrete cues at the appropriate time, without requiring user input. Further development of cue delivery methods and FOG-detection are required to achieve this. In this feasibility study, we aimed to test if FOG-initiated vibration cues applied to the lower-leg via wearable devices can improve gait in PD, and to develop real-time FOG-detection algorithms. 17 participants with Parkinson’s disease and daily FOG were recruited. During 1 h study sessions, participants undertook 4 complex walking circuits, each with a different intervention: continuous rhythmic vibration cueing (CC), responsive cueing (RC; cues initiated by the research team in response to FOG), device worn with no cueing (NC), or no device (ND). Study sessions were grouped into 3 stages/blocks (A-C), separated by a gap of several weeks, enabling improvements to circuit design and the cueing device to be implemented. Video and onboard inertial measurement unit (IMU) data were analyzed for FOG events and gait metrics. RC significantly improved circuit completion times demonstrating improved overall performance across a range of walking activities. Step frequency was significantly enhanced by RC during stages B and C. During stage C, > 10 FOG events were recorded in 45% of participants without cueing (NC), which was significantly reduced by RC. A machine learning framework achieved 83% sensitivity and 80% specificity for FOG detection using IMU data. Together, these data support the feasibility of closed-loop cueing approaches coupling real-time FOG detection with responsive somatosensory lower-leg cueing to improve gait in PD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12938-023-01167-y.
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spelling pubmed-106526242023-11-16 Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease Li, Dongli Hallack, Andre Gwilym, Sophie Li, Dongcheng Hu, Michele T. Cantley, James Biomed Eng Online Research Freezing-of-gait (FOG) and impaired walking are common features of Parkinson’s disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosensory cueing systems have the potential to deliver personalised, discrete cues at the appropriate time, without requiring user input. Further development of cue delivery methods and FOG-detection are required to achieve this. In this feasibility study, we aimed to test if FOG-initiated vibration cues applied to the lower-leg via wearable devices can improve gait in PD, and to develop real-time FOG-detection algorithms. 17 participants with Parkinson’s disease and daily FOG were recruited. During 1 h study sessions, participants undertook 4 complex walking circuits, each with a different intervention: continuous rhythmic vibration cueing (CC), responsive cueing (RC; cues initiated by the research team in response to FOG), device worn with no cueing (NC), or no device (ND). Study sessions were grouped into 3 stages/blocks (A-C), separated by a gap of several weeks, enabling improvements to circuit design and the cueing device to be implemented. Video and onboard inertial measurement unit (IMU) data were analyzed for FOG events and gait metrics. RC significantly improved circuit completion times demonstrating improved overall performance across a range of walking activities. Step frequency was significantly enhanced by RC during stages B and C. During stage C, > 10 FOG events were recorded in 45% of participants without cueing (NC), which was significantly reduced by RC. A machine learning framework achieved 83% sensitivity and 80% specificity for FOG detection using IMU data. Together, these data support the feasibility of closed-loop cueing approaches coupling real-time FOG detection with responsive somatosensory lower-leg cueing to improve gait in PD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12938-023-01167-y. BioMed Central 2023-11-16 /pmc/articles/PMC10652624/ /pubmed/37974260 http://dx.doi.org/10.1186/s12938-023-01167-y Text en © The Author(s) 2023 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 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
Li, Dongli
Hallack, Andre
Gwilym, Sophie
Li, Dongcheng
Hu, Michele T.
Cantley, James
Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease
title Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease
title_full Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease
title_fullStr Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease
title_full_unstemmed Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease
title_short Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease
title_sort investigating gait-responsive somatosensory cueing from a wearable device to improve walking in parkinson’s disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10652624/
https://www.ncbi.nlm.nih.gov/pubmed/37974260
http://dx.doi.org/10.1186/s12938-023-01167-y
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