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

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 in a near-production setting from 5 participants all familiar with carbon-fibre draping. 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 5 files for 5 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.