Movement data set for trust assessment (Drapebot robot cell/Profactor)

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016). For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see XSENS manual for a detailed description of avaiable measurements). Data organization There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: Segment Orientation - Quat Segment Orientation - Euler Segment Position Segment Velocity Segment Acceleration Segment Angular Velocity Segment Angular Acceleration Joint Angles ZXY Joint Angles XZY Ergonomic Joint Angles ZXY Ergonomic Joint Angles XZY Center of Mass Sensor Free Acceleration Sensor Magnetic Field Sensor Orientation - Quat Sensor Orientation - Euler See also: https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training. The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores). Items for Trust perception scale - HRI, Schaefer 2016: Which % of time does the robot Function successfully Act consistently Communicate with people Provide feedback Malfunction Follow directions Meet the needs of the mission Perform exactly as instructed Have errors Which % of the time is the robot: Unresponsive Dependable Reliable Predictable Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016: The way the robot moved made me uncomfortable I felt I could rely on the robot to do what it was supposed to do The speed at which the gripper picked up and released the components made me uneasy I felt safe interacting with the robot I knew the gripper would not drop the components The size of the robot did not intimidate me The robot gripper did not look reliable I was comfortable the robot would not hurt me I trusted that the robot was safe to cooperate with The gripper seemed like it could be trusted K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218. G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.