  In this paper, we aim at improving human motion prediction during human-robot
collaboration in industrial facilities by exploiting contributions from both
physical and physiological signals. Improved human-machine collaboration could
prove useful in several areas, while it is crucial for interacting robots to
understand human movement as soon as possible to avoid accidents and injuries.
In this perspective, we propose a novel human-robot interface capable to
anticipate the user intention while performing reaching movements on a working
bench in order to plan the action of a collaborative robot. The proposed
interface can find many applications in the Industry 4.0 framework, where
autonomous and collaborative robots will be an essential part of innovative
facilities. A motion intention prediction and a motion direction prediction
levels have been developed to improve detection speed and accuracy. A Gaussian
Mixture Model (GMM) has been trained with IMU and EMG data following an
evidence accumulation approach to predict reaching direction. Novel dynamic
stopping criteria have been proposed to flexibly adjust the trade-off between
early anticipation and accuracy according to the application. The output of the
two predictors has been used as external inputs to a Finite State Machine (FSM)
to control the behaviour of a physical robot according to user's action or
inaction. Results show that our system outperforms previous methods, achieving
a real-time classification accuracy of $94.3\pm2.9\%$ after
$160.0msec\pm80.0msec$ from movement onset.
