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

Ivan Miguel Pires, Faisal Hussain, Nuno M. Garcia, Petre Lameski and Eftim Zdravevski
Additional contact information
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
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

Future Internet, 2020, vol. 12, issue 11, 1-14

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.

Keywords: human activities; data normalization; data classification; sensors; mobile devices; data processing (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:

Downloads: (external link)
https://www.mdpi.com/1999-5903/12/11/194/pdf (application/pdf)
https://www.mdpi.com/1999-5903/12/11/194/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:12:y:2020:i:11:p:194-:d:442926

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().