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 @math after @math from movement onset.
In this paper, we have developed a neural network (NN) control enhanced teleoperation strategy which has been implemented on the Baxter robot. The upper limb motion of the human operator is captured by the inertial measurement unit (IMU) embedded in a pair of MYO armbands which are worn on the operator's forearm and upper arm, respectively. They are used to detect and to reconstruct the physical motion of shoulder and elbow joints of the operator. Given human operator's motion as reference trajectories, the robot is controlled using NN technique to compensate for its unknown dynamics. Adaptive law has been synthesized based on Lyapunov theory to enable effective NN learning. Preliminary experiments have been carried out to test the proposed method, which results in satisfactory performance on the Baxter robot teleoperation. Abstract This paper proposes the use of a non-immersive virtual reality rehabilitation system "ReHabgame" developed using Microsoft Kinect™ and the Thalmic™ Labs Myo gesture control armband. The ReHabgame was developed based on two third-person video games that provide a feasible possibility of assessing postural control and functional reach tests. It accurately quantifies specific postural control mechanisms including timed standing balance, functional reach tests using real-time anatomical landmark orientation, joint velocity, and acceleration while end trajectories were calculated using an inverse kinematics algorithm. The game was designed to help patients with neurological impairment to be subjected to physiotherapy activity and practice postures of daily activities. The subjective experience of the ReHabgame was studied through the development of an Engagement Questionnaire (EQ) for qualitative, quantitative and Rasch model. The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator algorithms were used to adapt the physical and gameplay intensity in the ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring System (HSS). Rasch analysis was conducted to assess the psychometric characteristics of the ReHabgame and to identify if these are any misfitting items in the game. Rasch rating scale model (RSM) was used to assess the engagement of players in the ReHabgame and evaluate the effectiveness and attractiveness of the game. The results showed that the scales assessing the rehabilitation process met Rasch expectations of reliability, and unidimensionality. Infit and outfit mean squares values are in the range of (0.68–1.52) for all considered 16 items. The Root Mean Square Residual (RMSR) and the person separation reliability were acceptable. The item person map showed that the persons and items were clustered symmetrically.