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Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition
Human activity recognition is becoming increasingly important. As contact with oneself and the environment accompanies almost all human activities, a Smart-Sleeve, made of soft and stretchable textile pressure sensor matrix, is proposed to sense human contact with the surroundings and identify perfo...
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/PMC8914988/ https://www.ncbi.nlm.nih.gov/pubmed/35270849 http://dx.doi.org/10.3390/s22051702 |
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author | Xu, Guanghua Wan, Quan Deng, Wenwu Guo, Tao Cheng, Jingyuan |
author_facet | Xu, Guanghua Wan, Quan Deng, Wenwu Guo, Tao Cheng, Jingyuan |
author_sort | Xu, Guanghua |
collection | PubMed |
description | Human activity recognition is becoming increasingly important. As contact with oneself and the environment accompanies almost all human activities, a Smart-Sleeve, made of soft and stretchable textile pressure sensor matrix, is proposed to sense human contact with the surroundings and identify performed activities in this work. Additionally, a dataset including 18 activities, performed by 14 subjects in 10 repetitions, is generated. The Smart-Sleeve is evaluated over six classical machine learning classifiers (support vector machine, k-nearest neighbor, logistic regression, random forest, decision tree and naive Bayes) and a convolutional neural network model. For classical machine learning, a new normalization approach is proposed to overcome signal differences caused by different body sizes and statistical, geometric, and symmetry features are used. All classification techniques are compared in terms of classification accuracy, precision, recall, and F-measure. Average accuracies of 82.02% (support vector machine) and 82.30% (convolutional neural network) can be achieved in 10-fold cross-validation, and 72.66% (support vector machine) and 74.84% (convolutional neural network) in leave-one-subject-out validation, which shows that the Smart-Sleeve and the proposed data processing method are suitable for human activity recognition. |
format | Online Article Text |
id | pubmed-8914988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89149882022-03-12 Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition Xu, Guanghua Wan, Quan Deng, Wenwu Guo, Tao Cheng, Jingyuan Sensors (Basel) Article Human activity recognition is becoming increasingly important. As contact with oneself and the environment accompanies almost all human activities, a Smart-Sleeve, made of soft and stretchable textile pressure sensor matrix, is proposed to sense human contact with the surroundings and identify performed activities in this work. Additionally, a dataset including 18 activities, performed by 14 subjects in 10 repetitions, is generated. The Smart-Sleeve is evaluated over six classical machine learning classifiers (support vector machine, k-nearest neighbor, logistic regression, random forest, decision tree and naive Bayes) and a convolutional neural network model. For classical machine learning, a new normalization approach is proposed to overcome signal differences caused by different body sizes and statistical, geometric, and symmetry features are used. All classification techniques are compared in terms of classification accuracy, precision, recall, and F-measure. Average accuracies of 82.02% (support vector machine) and 82.30% (convolutional neural network) can be achieved in 10-fold cross-validation, and 72.66% (support vector machine) and 74.84% (convolutional neural network) in leave-one-subject-out validation, which shows that the Smart-Sleeve and the proposed data processing method are suitable for human activity recognition. MDPI 2022-02-22 /pmc/articles/PMC8914988/ /pubmed/35270849 http://dx.doi.org/10.3390/s22051702 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 Xu, Guanghua Wan, Quan Deng, Wenwu Guo, Tao Cheng, Jingyuan Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition |
title | Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition |
title_full | Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition |
title_fullStr | Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition |
title_full_unstemmed | Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition |
title_short | Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition |
title_sort | smart-sleeve: a wearable textile pressure sensor array for human activity recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914988/ https://www.ncbi.nlm.nih.gov/pubmed/35270849 http://dx.doi.org/10.3390/s22051702 |
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