Upper limbs motion tracking for collaborative robotic applications

In the perspective of Industry 4.0, the contemporary presence of workers and robots in the same workspace requires the development of human motion prediction algorithms for a safe and efficient interaction. In this context, the purpose of the present study was to perform an operation of sensor fusion, by creating a collection of spatial and inertial variables of human upper limbs kinematics of typical industrial movements. Spatial and inertial data of ten healthy young subjects performing three pick and place gestures at different heights were measured with a stereophotogrammetric system and Inertial Measurement Units, respectively. Elbow and shoulder angles estimated from both instruments according to a multibody approach showed very similar trends. Moreover, two variables of the database were identified as distinctive features able to differentiate among the three gestures of pick and place.