A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment

Human gait data have traditionally been recorded in controlled laboratory environments focusing on single aspects in isolation. In contrast, 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 ± 7.64 cm, 72.9 ± 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. We anticipate the data set to provide a foundation for research that considers natural everyday walk scenarios with transitional motions and the interaction between gait and gaze during walking.