Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/14073
Title: Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
Authors: Pires, Ivan Miguel
Hussain, Faisal
Garcia, Nuno M.
Zdravevski, Eftim 
Issue Date: 17-Sep-2020
Publisher: MDPI AG
Journal: Future Internet
Abstract: <jats:p>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.</jats:p>
URI: http://hdl.handle.net/20.500.12188/14073
DOI: 10.3390/fi12090155
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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