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Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning

BACKGROUND: Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To d...

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Autores principales: Ollenschläger, Malte, Höfner, Patrick, Ullrich, Martin, Kluge, Felix, Greinwalder, Teresa, Loris, Evelyn, Regensburger, Martin, Eskofier, Bjoern M., Winkler, Jürgen, Gaßner, Heiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466820/
https://www.ncbi.nlm.nih.gov/pubmed/37644478
http://dx.doi.org/10.1186/s13023-023-02854-8
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author Ollenschläger, Malte
Höfner, Patrick
Ullrich, Martin
Kluge, Felix
Greinwalder, Teresa
Loris, Evelyn
Regensburger, Martin
Eskofier, Bjoern M.
Winkler, Jürgen
Gaßner, Heiko
author_facet Ollenschläger, Malte
Höfner, Patrick
Ullrich, Martin
Kluge, Felix
Greinwalder, Teresa
Loris, Evelyn
Regensburger, Martin
Eskofier, Bjoern M.
Winkler, Jürgen
Gaßner, Heiko
author_sort Ollenschläger, Malte
collection PubMed
description BACKGROUND: Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. METHODS: This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 [Formula: see text]  10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. RESULTS: The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. CONCLUSION: Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.
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spelling pubmed-104668202023-08-31 Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning Ollenschläger, Malte Höfner, Patrick Ullrich, Martin Kluge, Felix Greinwalder, Teresa Loris, Evelyn Regensburger, Martin Eskofier, Bjoern M. Winkler, Jürgen Gaßner, Heiko Orphanet J Rare Dis Research BACKGROUND: Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. METHODS: This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 [Formula: see text]  10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. RESULTS: The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. CONCLUSION: Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment. BioMed Central 2023-08-29 /pmc/articles/PMC10466820/ /pubmed/37644478 http://dx.doi.org/10.1186/s13023-023-02854-8 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
Ollenschläger, Malte
Höfner, Patrick
Ullrich, Martin
Kluge, Felix
Greinwalder, Teresa
Loris, Evelyn
Regensburger, Martin
Eskofier, Bjoern M.
Winkler, Jürgen
Gaßner, Heiko
Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
title Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
title_full Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
title_fullStr Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
title_full_unstemmed Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
title_short Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
title_sort automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466820/
https://www.ncbi.nlm.nih.gov/pubmed/37644478
http://dx.doi.org/10.1186/s13023-023-02854-8
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