Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study

Ivan Miguel Pires, Faisal Hussain, Nuno M. Garcia and Eftim Zdravevski
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Ivan Miguel Pires: Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
Faisal Hussain: Department of Computer Engineering, University of Engineering and Technology (UET), Taxila 47080, Pakistan
Nuno M. Garcia: Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
Eftim Zdravevski: Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia

Future Internet, 2020, vol. 12, issue 9, 1-18

Abstract: 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.

Keywords: human activities; data imputation; data classification; sensors; mobile devices; missing data (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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