User behavior and authentication analysis on smart devices

Smart devices, including smartphones, tablets, and wearable watches, have seamlessly integrated into daily life, gaining popularity due to their portability and user-friendly interfaces. The increased usage of these devices has catalyzed research in various domains. In these studies, the interactions between users and devices are investigated, leading to valuable insights into user habits and behaviors. These insights find applications in diverse fields, such as advertising, healthcare, and security, contributing to improving user experience and augmenting existing systems. However, there remains a need to address the limitations in current research, enhance their performance, and explore new opportunities. Therefore, this thesis focuses on analyzing user behaviors on smart devices, delving into three primary areas: age group detection, gender detection, and user authentication. Initially, a method for detecting user age groups utilizing smartphone accelerometer data is presented. Subsequently, four distinct gender detection schemes for smartphones are proposed. These schemes first involve machine learning approaches to detect gender across various user activities. Gender detection methods are then introduced based on deep learning approaches, comprising time-series to image transformation, continuous wavelet transform, and convolutional neural networks. Finally, two innovative methods for user authentication on smartphones are proposed. The first method employs traditional machine learning algorithms to analyze sensor data and touching behaviors. In contrast, the second method integrates data augmentation, discrete wavelet transform, and autoencoder models for user authentication. The proposed models demonstrate promising performance and address limitations in previous studies.