A multi-camera and multimodal dataset for posture and gait analysis

Gait and posture analysis while using assisting robotic devices is of utmost
importance to attain effective assistance. This work provides a multi-camera,
multimodal, and detailed dataset for vision-based applications using a wheeled
robotic walker equipped with a pair of affordable cameras. Depth data was
acquired at 30 fps from a total of 14 healthy participants walking at 3
different gait speeds, across 3 different walking scenarios/paths at 3
different locations. Simultaneously, accurate skeleton joint data was recorded
using an inertial-based commercial motion capture system that provides a
reliable ground-truth for classical or novel (i.e., machine learning-based)
vision-based applications. In total, the database contains approximately 166K
frames of synchronized data, which amounts to 92 minutes of total recording
time. This dataset may contribute to the development and evaluation of: i)
classic or data-driven vision-based pose estimation algorithms; ii)
applications in human detection and tracking, and movement forecasting; iii)
and gait/posture metrics analysis using a rehabilitation device.