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.在Drapebot项目中，一名工人与大型工业机械臂协同完成两项任务：碳纤维贴片协同转运与协同铺覆。为实现数据驱动的信任评估，工人穿戴动作捕捉服，身体运动数据由两份标准信任问卷的评分进行标注：1. 人机交互信任感知量表（Trust perception scale - HRI，Schaefer 2016）；2. 工业人机协作信任量表（Trust in industrial human robo collaboration，Charalambous等人，2016）。 本数据集收集了21名熟悉大型工业机械臂操作的参与者的铺覆任务相关数据。所有采集会话均采用Xsens MVN Awinda动作捕捉服（Xsens MVN Awinda）完成身体追踪。该设备包含紧身衣、手套、头带，以及用于将17个IMU（惯性测量单元，Inertial Measurement Unit）固定于参与者身体的一系列绑带。经过校准后，系统通过逆运动学以60赫兹的频率追踪并记录参与者的运动数据。测量项包含每个骨骼追踪点的线速度、角速度、速率与加速度，详细可用测量项的说明请参考XSENS官方手册。 数据组织：本数据集包含21个文件，对应21名参与者，文件名为PID01，其中01为参与者编号。所有文件均为xlsx格式，每个Excel文件内的工作表包含以下不同类型的数据：段朝向-四元数（Segment Orientation - Quat）、段朝向-欧拉角（Segment Orientation - Euler）、段位置（Segment Position）、段速度（Segment Velocity）、段加速度（Segment Acceleration）、段角速度（Segment Angular Velocity）、段角加速度（Segment Angular Acceleration）、ZXY顺序关节角（Joint Angles ZXY）、XZY顺序关节角（Joint Angles XZY）、ZXY顺序人体工程学关节角（Ergonomic Joint Angles ZXY）、XZY顺序人体工程学关节角（Ergonomic Joint Angles XZY）、质心（Center of Mass）、传感器自由加速度（Sensor Free Acceleration）、传感器磁场（Sensor Magnetic Field）、传感器朝向-四元数（Sensor Orientation - Quat）、传感器朝向-欧拉角（Sensor Orientation - Euler）。更多关于各数据项及传感器的详细信息，请参考XSENS官方手册，链接为：https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US。 数据标注：每个xlsx文件的第一个工作表名为"Markers"，用于标注各任务的起始帧，标注项包括取件（pickup）、铺覆（draping）、返回（return），部分文件可能额外包含"fail"标注。失败的任务样本不应纳入模型训练。 文件trustscores.xlsx包含各参与者的信任问卷结果，涵盖各条目得分与计算得到的总信任得分。 Schaefer 2016年提出的人机交互信任感知量表条目如下：机器人功能正常运行的时间占比、动作一致性、与人员沟通、提供反馈、发生故障、遵循指令、契合任务需求、严格按照指令执行、出现错误；机器人无响应、可靠、值得信赖、可预测的时间占比。 Charalambous等人2016年提出的工业人机协作信任量表条目如下：机器人的移动方式令我不适、我认为可以信赖机器人完成其预设任务、夹爪拾取和释放部件的速度令我不安、与该机器人交互时我感到安全、我确信夹爪不会掉落部件、机器人的尺寸未令我感到威慑、机器人夹爪看起来不可靠、我确信机器人不会伤及我、我信任该机器人协作的安全性、夹爪看起来值得信赖。 参考文献： 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。