Gyroscope and Accelerometer Dataset for Smartphone

The widespread adoption of smartphones has transformed how users engage with digital content, particularly for reading. Unlike desktop systems, which rely on peripherals like a mouse and keyboard, reading on smartphones involves direct interaction with the touchscreen. Actions such as pinch-to-zoom, tapping, scrolling, changing screen orientation, and taking screenshots are key components of smartphone reading behavior. While studies on desktop peripherals have provided insights into implicit feedback from user interactions, similar research for smartphones remains underexplored.

Smartphones, equipped with sensors like accelerometers and gyroscopes, capture data passively, offering an opportunity to analyze user gestures without requiring explicit permissions. These gestures can reveal valuable information about reading habits and user intent. To facilitate such studies, a dataset has been compiled, organized into accelerometer and gyroscope data directories. This dataset serves as a foundation for training, testing, and validating machine learning models to better understand smartphone reading behaviors and infer user intent.