Database (single file)

The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.