Upper limbs motion tracking for collaborative robotic applications

4noIn 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.partially_openembargoed_20210806Digo E.; Antonelli M.; Pastorelli S.; Gastaldi L.Digo, E.; Antonelli, M.; Pastorelli, S.; Gastaldi, L

Wearable MIMUs for the identification of upper limbs motion in an industrial context of human-robot interaction

The automation of human gestures is gaining increasing importance in manufacturing. Indeed, robots support operators by simplifying their tasks in a shared workspace. However, human-robot collaboration can be improved by identifying human actions and then developing adaptive control algorithms for the robot. Accordingly, the aim of this study was to classify industrial tasks based on accelerations signals of human upper limbs. Two magnetic inertial measurement units (MIMUs) on the upper limb of ten healthy young subjects acquired pick and place gestures at three different heights. Peaks were detected from MIMUs accelerations and were adopted to classify gestures through a Linear Discriminant Analysis. The method was applied firstly including two MIMUs and then one at a time. Results demonstrated that the placement of at least one MIMU on the upper arm or forearm is suitable to achieve good recognition performances. Overall, features extracted from MIMUs signals can be used to define and train a prediction algorithm reliable for the context of collaborative robotics