The signals of a recording belonging to the Frontside task are shown, the first two corresponding to the electromyography sensors while the others correspond to the infrared and red LED readings of the pulse oximetry sensor.

SWAN is a large-scale outdoor point cloud semantic segmentation, instance segmentation and object detection dataset.  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

This data was collected during a validation study of our Torso-Dynamics Estimation System (TES). The TES consisted of a Force Sensing Seat (FSS) and an inertial measurement unit (IMU) that measured the kinetics and kinematics of the subject's torso motions. The FSS estimated the 3D forces, 3D moments, and 2D COPs while the IMU estimated the 3D torso angles. To validate the TES, the FSS and IMU estimates were compared to gold standard research equipment (AMTI force plate and Qualisys motion capture system, respectively). 

The dataset for the vehicles collusion detection in internet of vehicles is generated from internet of vehicles enviornment using VSSIM. The detail procedure of the data collection and the preprocessing procedure can be found in the publihsed paper - https://doi.org/10.1016/j.eswa.2022.119033.