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Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data

Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio‐temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicia...

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Autores principales: Drouin, Pierre, Stamm, Aymeric, Chevreuil, Laurent, Graillot, Vincent, Barbin, Laetitia, Gourraud, Pierre‐Antoine, Laplaud, David‐Axel, Bellanger, Lise
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108058/
https://www.ncbi.nlm.nih.gov/pubmed/36509423
http://dx.doi.org/10.1002/sim.9625
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author Drouin, Pierre
Stamm, Aymeric
Chevreuil, Laurent
Graillot, Vincent
Barbin, Laetitia
Gourraud, Pierre‐Antoine
Laplaud, David‐Axel
Bellanger, Lise
author_facet Drouin, Pierre
Stamm, Aymeric
Chevreuil, Laurent
Graillot, Vincent
Barbin, Laetitia
Gourraud, Pierre‐Antoine
Laplaud, David‐Axel
Bellanger, Lise
author_sort Drouin, Pierre
collection PubMed
description Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio‐temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicians with a valuable tool for assessing gait impairment. Motion sensor data are however complex and there is an urgent unmet need to develop sound statistical methods for analyzing such data and extracting clinically relevant information. In this article, we measure gait by following the hip rotation over time and the resulting statistical unit is a time series of unit quaternions. We explore the possibility to form groups of patients with similar walking impairment by taking into account their walking data and their global decease severity with semi‐supervised clustering. We generalize a compromise‐based method named hclustcompro to unit quaternion time series by combining it with the proper dissimilarity quaternion dynamic time warping. We apply this method on patients diagnosed with multiple sclerosis to form groups of patients with similar walking deficiencies while accounting for the clinical assessment of their overall disability. We also compare the compromise‐based clustering approach with the method mergeTrees that falls into a sub‐class of ensemble clustering named collaborative clustering. The results provide a first proof of both the interest of using wearable motion sensors for assessing gait impairment and the use of prior knowledge to guide the clustering process. It also demonstrates that compromise‐based clustering is a more appropriate approach in this context.
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spelling pubmed-101080582023-04-18 Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data Drouin, Pierre Stamm, Aymeric Chevreuil, Laurent Graillot, Vincent Barbin, Laetitia Gourraud, Pierre‐Antoine Laplaud, David‐Axel Bellanger, Lise Stat Med Research Articles Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio‐temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicians with a valuable tool for assessing gait impairment. Motion sensor data are however complex and there is an urgent unmet need to develop sound statistical methods for analyzing such data and extracting clinically relevant information. In this article, we measure gait by following the hip rotation over time and the resulting statistical unit is a time series of unit quaternions. We explore the possibility to form groups of patients with similar walking impairment by taking into account their walking data and their global decease severity with semi‐supervised clustering. We generalize a compromise‐based method named hclustcompro to unit quaternion time series by combining it with the proper dissimilarity quaternion dynamic time warping. We apply this method on patients diagnosed with multiple sclerosis to form groups of patients with similar walking deficiencies while accounting for the clinical assessment of their overall disability. We also compare the compromise‐based clustering approach with the method mergeTrees that falls into a sub‐class of ensemble clustering named collaborative clustering. The results provide a first proof of both the interest of using wearable motion sensors for assessing gait impairment and the use of prior knowledge to guide the clustering process. It also demonstrates that compromise‐based clustering is a more appropriate approach in this context. John Wiley & Sons, Inc. 2022-12-12 2023-02-20 /pmc/articles/PMC10108058/ /pubmed/36509423 http://dx.doi.org/10.1002/sim.9625 Text en © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research Articles
Drouin, Pierre
Stamm, Aymeric
Chevreuil, Laurent
Graillot, Vincent
Barbin, Laetitia
Gourraud, Pierre‐Antoine
Laplaud, David‐Axel
Bellanger, Lise
Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data
title Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data
title_full Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data
title_fullStr Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data
title_full_unstemmed Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data
title_short Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data
title_sort semi‐supervised clustering of quaternion time series: application to gait analysis in multiple sclerosis using motion sensor data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108058/
https://www.ncbi.nlm.nih.gov/pubmed/36509423
http://dx.doi.org/10.1002/sim.9625
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