Gait analysis

This custom dataset was created to support gait recognition research using Inertial Measurement Units (IMUs), which capture acceleration, angular velocity, and orientation data from key body locations (e.g., ankles, waist, wrists). It includes recordings from [insert number] participants performing various walking tasks under different conditions, such as normal and fast walking or navigating obstacles. The dataset provides time-series data suitable for both traditional feature-based analysis and deep learning approaches. It aims to facilitate research in biometric authentication, activity recognition, abnormality detection, and wearable healthcare applications, offering a flexible resource for developing real-world gait recognition systems.