Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Shoujiang, Tang, Qingfeng, Jin, Linpeng, Pan, Zhigeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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
_version_ 1783426968441585664
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
work_keys_str_mv AT xushoujiang acascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones
AT tangqingfeng acascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones
AT jinlinpeng acascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones
AT panzhigeng acascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones
AT xushoujiang cascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones
AT tangqingfeng cascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones
AT jinlinpeng cascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones
AT panzhigeng cascadeensemblelearningmodelforhumanactivityrecognitionwithsmartphones