Investigating the impact of information sharing in human activity recognition

The accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning. Although the dataset contained records from various...

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Authors: Awais, Muhammad, Saurí Marchán, Sergi|||0000-0003-2019-5709
Format: article
Publication Date:2022
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/368054
Online Access:https://hdl.handle.net/2117/368054
https://dx.doi.org/10.3390/s22062280
Access Level:Open access
Keyword:Human activity recognition -- Data processing
Human activity recognition
Machine learning
Oversampling
Random forest
Smartphone
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
id ES_3d60ed2098e7d63a26caadcfce63f8f8
oai_identifier_str oai:upcommons.upc.edu:2117/368054
network_acronym_str ES
network_name_str España
repository_id_str
spelling Investigating the impact of information sharing in human activity recognitionAwais, MuhammadSaurí Marchán, Sergi|||0000-0003-2019-5709Human activity recognition -- Data processingHuman activity recognitionMachine learningOversamplingRandom forestSmartphoneAprenentatge automàticÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticThe accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning. Although the dataset contained records from various sensors, only accelerometer data were used in this study; thus, the developed methodology would preserve smartphone battery and incur low computation costs. A total of 175 different features were extracted from the pre-processed data. Data stratification was conducted in three ways to investigate the effect of information sharing between the training and testing datasets. After data balancing using only the training dataset, ten-fold and LOSO cross-validation were performed using several algorithms, including Support Vector Machine, XGBoost, Random Forest, Naïve Bayes, KNN, and Neural Network. A very simple post-processing algorithm was developed to improve the accuracy. The results reveal that XGBoost takes the least computation time while providing high prediction accuracy. Although Neural Network outperforms XGBoost, XGBoost demonstrates better accuracy with post-processing. The final detection accuracy ranges from 99.8% to 77.6% depending on the level of information sharing. This strongly suggests that when reporting accuracy values, the associated information sharing levels should be provided as well in order to allow the results to be interpreted in the correct context.This work was supported by the Severo Ochoa Center of Excellence (2019–2023) under the grant CEX2018-000797-S funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20222022-03-0120222022-06-03journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/368054https://dx.doi.org/10.3390/s22062280reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3680542026-05-27T15:37:01Z
dc.title.none.fl_str_mv Investigating the impact of information sharing in human activity recognition
title Investigating the impact of information sharing in human activity recognition
spellingShingle Investigating the impact of information sharing in human activity recognition
Awais, Muhammad
Human activity recognition -- Data processing
Human activity recognition
Machine learning
Oversampling
Random forest
Smartphone
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Investigating the impact of information sharing in human activity recognition
title_full Investigating the impact of information sharing in human activity recognition
title_fullStr Investigating the impact of information sharing in human activity recognition
title_full_unstemmed Investigating the impact of information sharing in human activity recognition
title_sort Investigating the impact of information sharing in human activity recognition
dc.creator.none.fl_str_mv Awais, Muhammad
Saurí Marchán, Sergi|||0000-0003-2019-5709
author Awais, Muhammad
author_facet Awais, Muhammad
Saurí Marchán, Sergi|||0000-0003-2019-5709
author_role author
author2 Saurí Marchán, Sergi|||0000-0003-2019-5709
author2_role author
dc.subject.none.fl_str_mv Human activity recognition -- Data processing
Human activity recognition
Machine learning
Oversampling
Random forest
Smartphone
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Human activity recognition -- Data processing
Human activity recognition
Machine learning
Oversampling
Random forest
Smartphone
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description The accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning. Although the dataset contained records from various sensors, only accelerometer data were used in this study; thus, the developed methodology would preserve smartphone battery and incur low computation costs. A total of 175 different features were extracted from the pre-processed data. Data stratification was conducted in three ways to investigate the effect of information sharing between the training and testing datasets. After data balancing using only the training dataset, ten-fold and LOSO cross-validation were performed using several algorithms, including Support Vector Machine, XGBoost, Random Forest, Naïve Bayes, KNN, and Neural Network. A very simple post-processing algorithm was developed to improve the accuracy. The results reveal that XGBoost takes the least computation time while providing high prediction accuracy. Although Neural Network outperforms XGBoost, XGBoost demonstrates better accuracy with post-processing. The final detection accuracy ranges from 99.8% to 77.6% depending on the level of information sharing. This strongly suggests that when reporting accuracy values, the associated information sharing levels should be provided as well in order to allow the results to be interpreted in the correct context.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-03-01
2022
2022-06-03
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/368054
https://dx.doi.org/10.3390/s22062280
url https://hdl.handle.net/2117/368054
https://dx.doi.org/10.3390/s22062280
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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