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Predicting reactive stepping in response to perturbations by using a classification approach
BACKGROUND: People use various strategies to maintain balance, such as taking a reactive step or rotating the upper body. To gain insight in human balance control, it is useful to know what makes people switch from one strategy to another. In previous studies the transition from a non-stepping balan...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331196/ https://www.ncbi.nlm.nih.gov/pubmed/32616066 http://dx.doi.org/10.1186/s12984-020-00709-y |
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author | Emmens, Amber R. F. van Asseldonk, Edwin H. Prinsen, Vera der Kooij, Herman van |
author_facet | Emmens, Amber R. F. van Asseldonk, Edwin H. Prinsen, Vera der Kooij, Herman van |
author_sort | Emmens, Amber R. |
collection | PubMed |
description | BACKGROUND: People use various strategies to maintain balance, such as taking a reactive step or rotating the upper body. To gain insight in human balance control, it is useful to know what makes people switch from one strategy to another. In previous studies the transition from a non-stepping balance response to reactive stepping was often described by an (extended) inverted pendulum model using a limited number of features. The goal of this study is to predict whether people will take a reactive step to recover from a push and to investigate what features are most relevant for that prediction by using a data-driven approach. METHODS: Ten subjects participated in an experiment in which they received forward pushes to which they had to respond naturally with or without stepping. The collected kinematic and center of pressure data were used to train several classification algorithms to predict reactive stepping. The classification algorithms that performed best were used to determine the most important features through recursive feature elimination. RESULTS: The neural networks performed better than the other classification algorithms. The prediction accuracy depended on the length of the observation time window: the longer the allowed time between the push and the prediction, the higher the accuracy. Using a neural network with one hidden layer and eight neurons, and a feature set consisting of various kinematic and center of pressure related features, an accuracy of 0.91 was obtained for predictions made up until the moment of step leg unloading, in combination with a sensitivity of 0.79 and a specificity 0.97. The most important features were the acceleration and velocity of the center of mass, and the position of the cervical joint center. CONCLUSION: Using our classification-based method the occurrence of reactive stepping could be predicted with a high accuracy, higher than previous methods for predicting natural reactive stepping. The feature set used for that prediction was different from the ones reported in other step prediction studies. Given the high step prediction performance, our method has the potential to be used for triggering reactive stepping in balance controllers of bipedal robots (e.g. exoskeletons). |
format | Online Article Text |
id | pubmed-7331196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73311962020-07-06 Predicting reactive stepping in response to perturbations by using a classification approach Emmens, Amber R. F. van Asseldonk, Edwin H. Prinsen, Vera der Kooij, Herman van J Neuroeng Rehabil Research BACKGROUND: People use various strategies to maintain balance, such as taking a reactive step or rotating the upper body. To gain insight in human balance control, it is useful to know what makes people switch from one strategy to another. In previous studies the transition from a non-stepping balance response to reactive stepping was often described by an (extended) inverted pendulum model using a limited number of features. The goal of this study is to predict whether people will take a reactive step to recover from a push and to investigate what features are most relevant for that prediction by using a data-driven approach. METHODS: Ten subjects participated in an experiment in which they received forward pushes to which they had to respond naturally with or without stepping. The collected kinematic and center of pressure data were used to train several classification algorithms to predict reactive stepping. The classification algorithms that performed best were used to determine the most important features through recursive feature elimination. RESULTS: The neural networks performed better than the other classification algorithms. The prediction accuracy depended on the length of the observation time window: the longer the allowed time between the push and the prediction, the higher the accuracy. Using a neural network with one hidden layer and eight neurons, and a feature set consisting of various kinematic and center of pressure related features, an accuracy of 0.91 was obtained for predictions made up until the moment of step leg unloading, in combination with a sensitivity of 0.79 and a specificity 0.97. The most important features were the acceleration and velocity of the center of mass, and the position of the cervical joint center. CONCLUSION: Using our classification-based method the occurrence of reactive stepping could be predicted with a high accuracy, higher than previous methods for predicting natural reactive stepping. The feature set used for that prediction was different from the ones reported in other step prediction studies. Given the high step prediction performance, our method has the potential to be used for triggering reactive stepping in balance controllers of bipedal robots (e.g. exoskeletons). BioMed Central 2020-07-02 /pmc/articles/PMC7331196/ /pubmed/32616066 http://dx.doi.org/10.1186/s12984-020-00709-y Text en © The Author(s) 2020 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. |
spellingShingle | Research Emmens, Amber R. F. van Asseldonk, Edwin H. Prinsen, Vera der Kooij, Herman van Predicting reactive stepping in response to perturbations by using a classification approach |
title | Predicting reactive stepping in response to perturbations by using a classification approach |
title_full | Predicting reactive stepping in response to perturbations by using a classification approach |
title_fullStr | Predicting reactive stepping in response to perturbations by using a classification approach |
title_full_unstemmed | Predicting reactive stepping in response to perturbations by using a classification approach |
title_short | Predicting reactive stepping in response to perturbations by using a classification approach |
title_sort | predicting reactive stepping in response to perturbations by using a classification approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331196/ https://www.ncbi.nlm.nih.gov/pubmed/32616066 http://dx.doi.org/10.1186/s12984-020-00709-y |
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