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

Author

Listed:
  • Ivan Miguel Pires

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
    Polytechnic Institute of Viseu, 3504-510 Viseu, 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)

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.

Suggested Citation

  • Ivan Miguel Pires & Faisal Hussain & Nuno M. Garcia & Eftim Zdravevski, 2020. "Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study," Future Internet, MDPI, vol. 12(9), pages 1-18, September.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:9:p:155-:d:414888
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    References listed on IDEAS

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    1. Antonio D’Ambrosio & Massimo Aria & Roberta Siciliano, 2012. "Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 227-258, July.
    2. Eftim Zdravevski & Biljana Risteska Stojkoska & Marie Standl & Holger Schulz, 2017. "Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-28, September.
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    Cited by:

    1. Ivan Miguel Pires & Faisal Hussain & Nuno M. Garcia & Petre Lameski & Eftim Zdravevski, 2020. "Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification," Future Internet, MDPI, vol. 12(11), pages 1-14, November.

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