Aybuke KececiArmagan YildirakKaan OzyaziciGulsen AyluctarhanOnur AgbulutIbrahim ZincirOzyazici, KaanKececi, AybukeAyluctarhan, GulsenZincir, IbrahimYildirak, ArmaganAgbulut, Onur2025-10-0620202215-098610.1016/j.jestch.2020.01.0052-s2.0-85079130497http://dx.doi.org/10.1016/j.jestch.2020.01.005https://gcris.yasar.edu.tr/handle/123456789/5952https://doi.org/10.1016/j.jestch.2020.01.005The basis of biometric authentication is that each person's physical and behavioural characteristics can be accurately defined. Many authentication techniques were developed over the years. Human gait recognition is one of these techniques. This article explores machine learning techniques for user authentication on HugaDB database which is a human gait data collection for analysis and activity recognition (Chereshnev and Kertesz-Farkas 2017). The activities recorded in this dataset are walking running sitting and standing. The data were collected with devices such as wearable accelerometer and gyroscope. In total the data describe 18 individuals thus we considered each individual as a different class. 10 commonly used machine learning algorithms have been implemented over the HugaDB. The proposed system achieved more than 99% in accuracy via IB1 Random Forest and Bayesian Net algorithms. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.Englishinfo:eu-repo/semantics/openAccessMachine learning, Security, Gait recognition, Human detection, BiometricSecurityGait RecognitionMachine LearningHuman DetectionBiometricArticle