IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v12y2020i11p194-d442926.html
   My bibliography  Save this article

Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification

Author

Listed:
  • Ivan Miguel Pires

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
    Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
    UICISA:E Research Centre, School of Health, 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)

  • Petre Lameski

    (Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia)

  • Eftim Zdravevski

    (Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia)

Abstract

One class of applications for human activity recognition methods is found in mobile devices for monitoring older adults and people with special needs. Recently, many studies were performed to create intelligent methods for the recognition of human activities. However, the different mobile devices in the market acquire the data from sensors at different frequencies. This paper focuses on implementing four data normalization techniques, i.e., MaxAbsScaler, MinMaxScaler, RobustScaler, and Z-Score. Subsequently, we evaluate the impact of the normalization algorithms with deep neural networks (DNN) for the classification of the human activities. The impact of the data normalization was counterintuitive, resulting in a degradation of performance. Namely, when using the accelerometer data, the accuracy dropped from about 79% to only 53% for the best normalization approach. Similarly, for the gyroscope data, the accuracy without normalization was about 81.5%, whereas with the best normalization, it was only 60%. It can be concluded that data normalization techniques are not helpful in classification problems with homogeneous data.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:194-:d:442926
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/12/11/194/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/12/11/194/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deac Dan Stelian & Schebesch Klaus Bruno, 2018. "Market Forecasts and Client Behavioral Data: Towards Finding Adequate Model Complexity," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 28(3), pages 50-75, September.
    2. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    3. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    4. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Fabrizio De Caro & Amedeo Andreotti & Rodolfo Araneo & Massimo Panella & Antonello Rosato & Alfredo Vaccaro & Domenico Villacci, 2020. "A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data," Energies, MDPI, vol. 13(24), pages 1-25, December.
    6. Tymoteusz Miller & Grzegorz Mikiciuk & Anna Kisiel & Małgorzata Mikiciuk & Dominika Paliwoda & Lidia Sas-Paszt & Danuta Cembrowska-Lech & Adrianna Krzemińska & Agnieszka Kozioł & Adam Brysiewicz, 2023. "Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-16, August.
    7. Dongfen Li & Yundan Zheng & Xiaofang Liu & Jie Zhou & Yuqiao Tan & Xiaolong Yang & Mingzhe Liu, 2022. "Hierarchical Quantum Information Splitting of an Arbitrary Two-Qubit State Based on a Decision Tree," Mathematics, MDPI, vol. 10(23), pages 1-18, December.
    8. Jorge Omar Razo-De-Anda & Luis Lorenzo Romero-Castro & Francisco Venegas-Martínez, 2023. "Contagion Patterns Classification in Stock Indices: A Functional Clustering Analysis Using Decision Trees," Mathematics, MDPI, vol. 11(13), pages 1-27, July.
    9. Kamyab Karimi & Ali Ghodratnama & Reza Tavakkoli-Moghaddam, 2023. "Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: a comprehensive analysis," Annals of Operations Research, Springer, vol. 328(1), pages 665-700, September.
    10. Dimitar Haralampiev Popov, 2022. "SME Viability Assessment Methodology: Combining Altman's Z-Score with Big Data," Bulgarian Economic Papers bep-2022-04, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St Kliment Ohridski, revised Jun 2022.
    11. Abdul Majeed, 2019. "Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets," Annals of Data Science, Springer, vol. 6(4), pages 599-621, December.
    12. Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
    13. Jana Gerlach & Paul Hoppe & Sarah Jagels & Luisa Licker & Michael H. Breitner, 2022. "Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2139-2158, December.
    14. João R. B. Paiva & Alana S. Magalhães & Pedro H. F. Moraes & Júnio S. Bulhões & Wesley P. Calixto, 2021. "Stability Metric Based on Sensitivity Analysis Applied to Electrical Repowering System," Energies, MDPI, vol. 14(22), pages 1-21, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:194-:d:442926. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.