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A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone

As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide t...

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Detalles Bibliográficos
Autores principales: Qi, Wen, Su, Hang, Yang, Chenguang, Ferrigno, Giancarlo, De Momi, Elena, Aliverti, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749356/
https://www.ncbi.nlm.nih.gov/pubmed/31470521
http://dx.doi.org/10.3390/s19173731
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author Qi, Wen
Su, Hang
Yang, Chenguang
Ferrigno, Giancarlo
De Momi, Elena
Aliverti, Andrea
author_facet Qi, Wen
Su, Hang
Yang, Chenguang
Ferrigno, Giancarlo
De Momi, Elena
Aliverti, Andrea
author_sort Qi, Wen
collection PubMed
description As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%.
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spelling pubmed-67493562019-09-27 A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone Qi, Wen Su, Hang Yang, Chenguang Ferrigno, Giancarlo De Momi, Elena Aliverti, Andrea Sensors (Basel) Article As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%. MDPI 2019-08-29 /pmc/articles/PMC6749356/ /pubmed/31470521 http://dx.doi.org/10.3390/s19173731 Text en © 2019 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
Qi, Wen
Su, Hang
Yang, Chenguang
Ferrigno, Giancarlo
De Momi, Elena
Aliverti, Andrea
A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone
title A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone
title_full A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone
title_fullStr A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone
title_full_unstemmed A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone
title_short A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone
title_sort fast and robust deep convolutional neural networks for complex human activity recognition using smartphone
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749356/
https://www.ncbi.nlm.nih.gov/pubmed/31470521
http://dx.doi.org/10.3390/s19173731
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