Title:
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AN EFFICIENT DATA IMPUTATION TECHNIQUE
FOR HUMAN ACTIVITY RECOGNITION |
Author(s):
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Ivan Miguel Pires, Faisal Hussain, Nuno M. Garcia and Eftim Zdravevski |
ISBN:
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978-989-8704-18-4 |
Editors:
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Mário Macedo |
Year:
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2020 |
Edition:
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Single |
Keywords:
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Daily Activities, Data Imputation, Sensors, Mobile Devices, Missing Data |
Type:
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Full |
First Page:
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47 |
Last Page:
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54 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual
reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous
applications. In recent years, many datasets have been proposed to train the machine learning models for efficient
monitoring and recognition of human daily living activities. However, the performance of machine learning models in
activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in
dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to
better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and
utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The
proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset. |
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