Human Gait Dataset for Biometric Authentication

The Human Gait Dataset for Biometric Authentication consists of gait patterns collected from 87 individuals across multiple sessions using smartphone motion sensors. Each participant contributed data in two sessions, with three repetitions per session, and each session lasted approximately 1:30 minutes. The dataset was recorded using a self-developed web-based application, capturing a wide range of sensor-based features, including accelerometer, gyroscope, and rotation sensor data, along with demographic details such as gender, age, handedness, and educational qualification. The dataset includes linear acceleration measurements in the x, y, and z axes, rotational movement data from the gyroscope, gravitational force readings, and device orientation angles (Alpha, Beta, Gamma). These features provide valuable insights into gait-based authentication, making this dataset useful for developing biometric authentication systems, machine learning-based anomaly detection models, healthcare applications such as fall detection and rehabilitation analysis, and security applications for continuous authentication. The structured collection of gait data across multiple sessions ensures variability and robustness, allowing researchers to explore authentication strategies that leverage human movement patterns.