Context-aware gender and age recognition from smartphone sensors

Smartphones include multiple sensors to track a device’s movement. This research investigated the capability of smartphone motion sensors to determine the user’s gender and age in different contexts. A subject’s context is an action they engage in, such as sitting or standing. This paper is based on the differences in behavior between male and female smartphone users, precisely, how they hold and manage their devices. To build our approach, we use the MotionSense Dataset. This dataset contains data from accelerometer and gyroscope sensors over time (attitude, gravity, acceleration, and rotationRate). In this study, we consider multiple contexts such as walking, sitting, standing, and jogging. Our method proposes to use smartphone sensors to detect an individual’s age and gender with an accuracy of 99.89% if they are seated.