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

Future Internet

Faisal Hussain¹  Petre Lameski²  Eftim Zdravevski²  IvanMiguel Pires³  NunoM. Garcia³ 

[1] Department of Computer Engineering, University of Engineering and Technology (UET), Taxila 47080, Pakistan;Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia;Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal;

关键词: human activities;    data normalization;    data classification;    sensors;    mobile devices;    data processing;   

DOI  :  10.3390/fi12110194

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown