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Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms
Decoding natural hand movements is of interest for human–computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412700/ https://www.ncbi.nlm.nih.gov/pubmed/36015862 http://dx.doi.org/10.3390/s22166101 |
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author | Reichert, Christoph Klemm, Lisa Mushunuri, Raghava Vinaykanth Kalyani, Avinash Schreiber, Stefanie Kuehn, Esther Azañón, Elena |
author_facet | Reichert, Christoph Klemm, Lisa Mushunuri, Raghava Vinaykanth Kalyani, Avinash Schreiber, Stefanie Kuehn, Esther Azañón, Elena |
author_sort | Reichert, Christoph |
collection | PubMed |
description | Decoding natural hand movements is of interest for human–computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time. |
format | Online Article Text |
id | pubmed-9412700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94127002022-08-27 Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms Reichert, Christoph Klemm, Lisa Mushunuri, Raghava Vinaykanth Kalyani, Avinash Schreiber, Stefanie Kuehn, Esther Azañón, Elena Sensors (Basel) Article Decoding natural hand movements is of interest for human–computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time. MDPI 2022-08-15 /pmc/articles/PMC9412700/ /pubmed/36015862 http://dx.doi.org/10.3390/s22166101 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Reichert, Christoph Klemm, Lisa Mushunuri, Raghava Vinaykanth Kalyani, Avinash Schreiber, Stefanie Kuehn, Esther Azañón, Elena Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms |
title | Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms |
title_full | Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms |
title_fullStr | Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms |
title_full_unstemmed | Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms |
title_short | Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms |
title_sort | discriminating free hand movements using support vector machine and recurrent neural network algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412700/ https://www.ncbi.nlm.nih.gov/pubmed/36015862 http://dx.doi.org/10.3390/s22166101 |
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