Postural Optimization for a Safe and Comfortable Human-Robot Interaction: Experiment Dataset

In human-robot collaboration the robot's behavior impacts the worker's safety, comfort, and his acceptance of the robotic system. In this paper we address the problem of how to improve the worker's posture during human-robot collaboration. Using postural assessment techniques, and a personalized human kinematic model, we optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures. We then derive a robotic behavior that leads the worker towards that improved posture. We validate our approach in an experiment involving a joint task with 39 human subjects and a Baxter torso-humanoid robot.

This repository contains the anonymized recorded data of our experiment. For all the subjects, we have included their recorded posture, using a motion capture system, and a video taken from a camera located on the robot head. Each data is divided by subjects and by tested conditions. In a separate file, we also include the result of the survey answered alongside the experiment.