Gender Detection Based on Gait Data: A Deep Learning Approach With Synthetic Data Generation and Continuous Wavelet Transform

Abstract

Smart devices equipped with various sensors enable the acquisition of users' behavioral biometrics. These sensor data capture variations in users' interactions with the devices, which can be analyzed to extract valuable information such as user activity, age group, and gender. In this study, we investigate the feasibility of using gait data for gender detection of users. To achieve this, we propose a novel gender detection scheme based on a deep learning approach, incorporating synthetic data generation and continuous wavelet transform (CWT). In this scheme, the real dataset is first divided into training and test datasets, and then synthetic data are intelligently generated using various techniques to augment the existing training data. Subsequently, CWT is used as the feature extraction module, and its outputs are fed into a deep learning model to detect the gender of users. Different deep learning models, including convolutional neural network (CNN) and long short-term memory (LSTM), are employed in classification. Consequently, we evaluate our proposed framework on different publicly available datasets. On the BOUN Sensor dataset, we obtain an accuracy of 94.83%, marking a substantial 6.5% enhancement over the prior highest rate of 88.33%. Additionally, we achieve 86.27% and 88.15% accuracy on the OU-ISIR Android and OU-ISIR Center IMUZ datasets, respectively. Our experimental results demonstrate that our proposed model achieves high detection rates and outperforms previous methods across all datasets. INDEX TERMSBiometrics, continuous wavelet transform, convolutional neural networks, frequency domain, gender detection, generative adversarial networks, human gait, motion sensors, smartphones. authors successfully determined user gender by analyzing smartphone accelerometer sensor data. Accelerometer sen-sor data have also been leveraged to detect behaviors associated with individuals' stress levels in [2]. Additionally, smartphone motion sensor data has been utilized in multiple studies to recognize and classify human daily activities [3]-[5]. On the other hand, the authors have employed sensor data from smart devices to enhance user identification and authentication systems in [6], [7].Gait analysis is a significant biometric feature that facilitates human identification and provides insights into physical and medical conditions. Due to the unique nature of an individual's gait, which reflects their walking style and physical abilities, it becomes challenging to mimic the gait pattern of others [8]. Thus, gait analysis finds applications across various domains, including security, sports, surveillance, and the medical field [9], [10]. For instance, in [11], the authors underscored using sensor-based gait analysis in clinical applications for monitoring and diagnosing conditions such as Parkinson's disease.Sensor data originating from touchscreen interactions and