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Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information
In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412096/ https://www.ncbi.nlm.nih.gov/pubmed/32698482 http://dx.doi.org/10.3390/s20144025 |
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author | Hao, Zhanjun Duan, Yu Dang, Xiaochao Liu, Yang Zhang, Daiyang |
author_facet | Hao, Zhanjun Duan, Yu Dang, Xiaochao Liu, Yang Zhang, Daiyang |
author_sort | Hao, Zhanjun |
collection | PubMed |
description | In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language is widely used in life. The establishment of an effective sign language recognition system can help people with aphasia and hearing impairment to better interact with the computer and facilitate their daily life. For this reason, this paper proposes a contactless fine-grained gesture recognition method using Channel State Information (CSI), namely Wi-SL. This method uses a commercial WiFi device to establish the correlation mapping between the amplitude and phase difference information of the subcarrier level in the wireless signal and the sign language action, without requiring the user to wear any device. We combine an efficient denoising method to filter environmental interference with an effective selection of optimal subcarriers to reduce the computational cost of the system. We also use K-means combined with a Bagging algorithm to optimize the Support Vector Machine (SVM) classification (KSB) model to enhance the classification of sign language action data. We implemented the algorithms and evaluated them for three different scenarios. The experimental results show that the average accuracy of Wi-SL gesture recognition can reach 95.8%, which realizes device-free, non-invasive, high-precision sign language gesture recognition. |
format | Online Article Text |
id | pubmed-7412096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74120962020-08-25 Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information Hao, Zhanjun Duan, Yu Dang, Xiaochao Liu, Yang Zhang, Daiyang Sensors (Basel) Article In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language is widely used in life. The establishment of an effective sign language recognition system can help people with aphasia and hearing impairment to better interact with the computer and facilitate their daily life. For this reason, this paper proposes a contactless fine-grained gesture recognition method using Channel State Information (CSI), namely Wi-SL. This method uses a commercial WiFi device to establish the correlation mapping between the amplitude and phase difference information of the subcarrier level in the wireless signal and the sign language action, without requiring the user to wear any device. We combine an efficient denoising method to filter environmental interference with an effective selection of optimal subcarriers to reduce the computational cost of the system. We also use K-means combined with a Bagging algorithm to optimize the Support Vector Machine (SVM) classification (KSB) model to enhance the classification of sign language action data. We implemented the algorithms and evaluated them for three different scenarios. The experimental results show that the average accuracy of Wi-SL gesture recognition can reach 95.8%, which realizes device-free, non-invasive, high-precision sign language gesture recognition. MDPI 2020-07-20 /pmc/articles/PMC7412096/ /pubmed/32698482 http://dx.doi.org/10.3390/s20144025 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hao, Zhanjun Duan, Yu Dang, Xiaochao Liu, Yang Zhang, Daiyang Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information |
title | Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information |
title_full | Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information |
title_fullStr | Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information |
title_full_unstemmed | Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information |
title_short | Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information |
title_sort | wi-sl: contactless fine-grained gesture recognition uses channel state information |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412096/ https://www.ncbi.nlm.nih.gov/pubmed/32698482 http://dx.doi.org/10.3390/s20144025 |
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