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Human Posture Detection Method Based on Wearable Devices

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure sig...

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Detalles Bibliográficos
Autores principales: Li, Xiaoou, Zhou, Zhiyong, Wu, Jiajia, Xiong, Yichao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016574/
https://www.ncbi.nlm.nih.gov/pubmed/33833862
http://dx.doi.org/10.1155/2021/8879061
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author Li, Xiaoou
Zhou, Zhiyong
Wu, Jiajia
Xiong, Yichao
author_facet Li, Xiaoou
Zhou, Zhiyong
Wu, Jiajia
Xiong, Yichao
author_sort Li, Xiaoou
collection PubMed
description The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.
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spelling pubmed-80165742021-04-07 Human Posture Detection Method Based on Wearable Devices Li, Xiaoou Zhou, Zhiyong Wu, Jiajia Xiong, Yichao J Healthc Eng Research Article The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system. Hindawi 2021-03-24 /pmc/articles/PMC8016574/ /pubmed/33833862 http://dx.doi.org/10.1155/2021/8879061 Text en Copyright © 2021 Xiaoou Li et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Xiaoou
Zhou, Zhiyong
Wu, Jiajia
Xiong, Yichao
Human Posture Detection Method Based on Wearable Devices
title Human Posture Detection Method Based on Wearable Devices
title_full Human Posture Detection Method Based on Wearable Devices
title_fullStr Human Posture Detection Method Based on Wearable Devices
title_full_unstemmed Human Posture Detection Method Based on Wearable Devices
title_short Human Posture Detection Method Based on Wearable Devices
title_sort human posture detection method based on wearable devices
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016574/
https://www.ncbi.nlm.nih.gov/pubmed/33833862
http://dx.doi.org/10.1155/2021/8879061
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