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A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones
Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566970/ https://www.ncbi.nlm.nih.gov/pubmed/31109126 http://dx.doi.org/10.3390/s19102307 |
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author | Xu, Shoujiang Tang, Qingfeng Jin, Linpeng Pan, Zhigeng |
author_facet | Xu, Shoujiang Tang, Qingfeng Jin, Linpeng Pan, Zhigeng |
author_sort | Xu, Shoujiang |
collection | PubMed |
description | Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer’s classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient. |
format | Online Article Text |
id | pubmed-6566970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65669702019-06-17 A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones Xu, Shoujiang Tang, Qingfeng Jin, Linpeng Pan, Zhigeng Sensors (Basel) Article Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer’s classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient. MDPI 2019-05-19 /pmc/articles/PMC6566970/ /pubmed/31109126 http://dx.doi.org/10.3390/s19102307 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 Xu, Shoujiang Tang, Qingfeng Jin, Linpeng Pan, Zhigeng A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones |
title | A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones |
title_full | A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones |
title_fullStr | A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones |
title_full_unstemmed | A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones |
title_short | A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones |
title_sort | cascade ensemble learning model for human activity recognition with smartphones |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566970/ https://www.ncbi.nlm.nih.gov/pubmed/31109126 http://dx.doi.org/10.3390/s19102307 |
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