Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and...
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doaj-de5a77b9a8a343d2bdf800d4bce7593c2020-11-25T03:07:35ZengMDPI AGFuture Internet1999-59032020-09-011215515510.3390/fi12090155Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental StudyIvan Miguel Pires0Faisal Hussain1Nuno M. Garcia2Eftim Zdravevski3Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalDepartment of Computer Engineering, University of Engineering and Technology (UET), Taxila 47080, PakistanInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North MacedoniaThe automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation.https://www.mdpi.com/1999-5903/12/9/155human activitiesdata imputationdata classificationsensorsmobile devicesmissing data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ivan Miguel Pires Faisal Hussain Nuno M. Garcia Eftim Zdravevski |
spellingShingle |
Ivan Miguel Pires Faisal Hussain Nuno M. Garcia Eftim Zdravevski Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study Future Internet human activities data imputation data classification sensors mobile devices missing data |
author_facet |
Ivan Miguel Pires Faisal Hussain Nuno M. Garcia Eftim Zdravevski |
author_sort |
Ivan Miguel Pires |
title |
Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study |
title_short |
Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study |
title_full |
Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study |
title_fullStr |
Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study |
title_full_unstemmed |
Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study |
title_sort |
improving human activity monitoring by imputation of missing sensory data: experimental study |
publisher |
MDPI AG |
series |
Future Internet |
issn |
1999-5903 |
publishDate |
2020-09-01 |
description |
The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation. |
topic |
human activities data imputation data classification sensors mobile devices missing data |
url |
https://www.mdpi.com/1999-5903/12/9/155 |
work_keys_str_mv |
AT ivanmiguelpires improvinghumanactivitymonitoringbyimputationofmissingsensorydataexperimentalstudy AT faisalhussain improvinghumanactivitymonitoringbyimputationofmissingsensorydataexperimentalstudy AT nunomgarcia improvinghumanactivitymonitoringbyimputationofmissingsensorydataexperimentalstudy AT eftimzdravevski improvinghumanactivitymonitoringbyimputationofmissingsensorydataexperimentalstudy |
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