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.
This lecture explores the various uses of surface electromyography in the field of biomechanics. Three groups of applications are considered: those involving the activation timing of muscles, the force EMG signal relationship, and the use of the EMG signal as a fatigue index. Technical considerations for recording the EMG signal with maximal fidelity are reviewed, and a compendium of all known factors that affect the information contained in the EMG signal is presented. Questions are posed to guide the practitioner in the proper use of surface electromyography. Sixteen recommendations are made regarding the proper detection, analysis, and interpretation of the EMG signal and measured force. Sixteen outstanding problems that present the greatest challenges to the advancement of surface electromyography are put forward for consideration. Finally, a plea is made for arriving at an international agreement on procedures commonly used in electromyography and biomechanics. 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.  This paper presents a novel approach for human-robot cooperation in tasks with dynamic uncertainties. The essential element of the proposed method is a multi-modal interface that provides the robot with the feedback about the human motor behaviour in real-time. The human muscle activity measurements and the arm force manipulability properties encode the information about the motion and impedance, and the intended configuration of the task frame, respectively. Through this human-in-the-loop framework, the developed hybrid controller of the robot can adapt its actions to provide the desired motion and impedance regulation in different phases of the cooperative task. We experimentally evaluate the proposed approach in a two-person sawing task that requires an appropriate complementary behaviour from the two agents. In this paper we describe and practically demonstrate a robotic arm hand system that is controlled in realtime in 6D Cartesian space through measured human muscular activity. The soft-robotics control architecture of the robotic system ensures safe physical human robot interaction as well as stable behaviour while operating in an unstructured environment. Muscular control is realised via surface electromyography, a non-invasive and simple way to gather human muscular activity from the skin. A standard supervised machine learning system is used to create a map from muscle activity to hand position, orientation and grasping force which then can be evaluated in real time—the existence of such a map is guaranteed by gravity compensation and low-speed movement. No kinematic or dynamic model of the human arm is necessary, which makes the system quickly adaptable to anyone. Numerical validation shows that the system achieves good movement precision. Live evaluation and demonstration of the system during a robotic trade fair is reported and confirms the validity of the approach, which has potential applications in muscle-disorder rehabilitation or in teleoperation where a close-range, safe master slave interaction is required, and or when optical magnetic position tracking cannot be enforced. All over the world, people find joy and amusement in playing hand-clapping games such as “Pat-a-cake” and “Slide.” Thus, as robots enter everyday human spaces and work together with people, we see potential for them to entertain, engage, and assist humans through cooperative clapping games. This paper explores how data recorded from a pair of commonly available inertial measurement units (IMUs) worn on a human's hands can contribute to the teaching of a hand-clapping robot. We identified representative hand-clapping activities, considered approaches to classify games, and conducted a study to record hand-clapping motion data. Analysis of data from fifteen participants indicates that support vector machines and Markov chain analysis can correctly classify 95.5 of the demonstrated hand-clapping motions (from ten discrete actions) and 92.3 of the hand-clapping game demonstrations recorded in the study. These results were calculated by withholding a participant's entire dataset for testing, so these results should represent general system behavior for new users. Overall, this research lays the groundwork for a simple and efficient method that people could use to demonstrate hand-clapping games to robots. The interaction with robotic devices by means of physiological human signals has become of great interest in the last years because of the capability of catching human intention of movement and translate it in a coherent action performed by a robotic platform. Due to the complexity of EMG signals, several studies have been carried out about models built on a single subject (subject-specific). However, the execution of a certain task presents a common underlying behaviour, even if it is performed by different people. This common behaviour leads to some constraints that could be extracted by looking to different interpretations of the task, obtaining a subject-independent model. The few attempts in literature showed the possibility of creating a multiuser interface able to adapt to novel users (subject-independent). Nevertheless, the majority of the studies focused on classification problems, that are only able to determine the type of movement. We improved the state-of-the-art by introducing an online subject-independent framework able to compute the actual trajectory of the robot motion through a regression technique. The framework is based on a Gaussian Mixture Model (GMM) trained through Surface Electromyography (sEMG) signals coming from human subjects. Wavelet Transform has been used to elaborate the sEMG signals in real time. The goodness of the proposed framework has been tested with two different dataset involving various joints for both upper and lower limbs. The achieved results show that our framework could obtain high performances in both accuracy and computational time by reaching significant correlation (≥ 0.8). The whole procedure has been tested on two robots, a simulated hand and a humanoid, by remapping the human motion to the robotic platforms in order to verify the proper execution of the original movement. AbstractBiomechanical analysis is a powerful tool in the evaluation of movement dysfunction in orthopaedic and neurologic populations. Three-dimensional (3D) motion capture systems are widely used, accurate systems, but are costly and not available in many clinical settings. The Microsoft Kinect™ has the potential to be used as an alternative low-cost motion analysis tool. The purpose of this study was to assess concurrent validity of the Kinect™ with Brekel Kinect software in comparison to Vicon Nexus during sagittal plane gait kinematics. Twenty healthy adults (nine male, 11 female) were tracked while walking and jogging at three velocities on a treadmill. Concurrent hip and knee peak flexion and extension and stride timing measurements were compared between Vicon and Kinect™. Although Kinect measurements were representative of normal gait, the Kinect™ generally under-estimated joint flexion and over-estimated extension. Kinect™ and Vicon hip angular displacement correlation was very low and error was l...