Developing natural control strategies represents an intriguing challenge in the design of human-robot interface (HRI) systems. The teleoperation of robotic grasping devices, especially in industrial, rescue, and aerospace applications, is mostly based on nonintuitive approaches, such as remote controllers. On the other hand, recent research efforts target solutions that mimic the human ability to manage multifinger grasps and finely modulate grasp impedance. Since electromyography (EMG) contains information about human motion control, it is possible to leverage such neuromuscular knowledge to teleoperate robotic hands for grasping tasks. In this paper, we present an HRI system based on eight fully differential EMG sensors connected to a wearable sensor node for acquisition and processing. By virtue of a novel bio-inspired approach, the embedded myocontroller merges pattern recognition and factorization techniques to combine a natural selection of the robotic hand configuration with the proportional control of the related grasps. The HRI system has been fully designed, implemented, and tested on two robotic hands: a dexterous anthropomorphic hand and a three-fingered industrial gripper mounted on a robotic manipulator. The results of the test performed on four able-bodied subjects show success rates greater than 90% reached in grasping objects that require different hand shapes and impedance regulations for the task completion. The outcomes also show that the users modulate the bio-inspired degrees of control in a natural manner, proving the pertinence of the proposed system for an effective human-like control of robotic grasping devices in a wearable form factor.

The scope of this work is to show the applicability of the Twisted String Actuators (TSAs) for lightweight, wear- able and assistive robotic applications. To this aim, we have developed a novel surface electromyography (sEMG)-driven soft ExoSuit using the TSAs to perform both single and dual- arm elbow assistive applications. The proposed ExoSuit, with an overall weight of 1650g, uses a pair of TSAs mounted in the back of the user, connected via tendons to the user's forearms to actuate each arm independently for supporting external loads. We confirm this new light-weight and customizable wearable solution via multiple user studies based on the biceps and tricep’ sEMG measurements. We demonstrate that user muscles can automatically activate and regulate the TSAs and compensate for the user's effort: by using our controller based on a Double Threshold Strategy (DTS) with a standard PID regulator, we report that the system was able to limit the biceps’ sEMG activity under an arbitrary target threshold, compensating a muscular activity equal to 220% (related to a single arm 3kg load) and 110% (related to a dual arm 4kg load) of the threshold value itself. Moreover, the triceps’ sEMG signal detects the external load and, depending on the threshold, returns the system to the initial state where it requires no assistance from the ExoSuit. The experimental results show the proposed ExoSuit's capabilities in both single and dual- arm load compensation tasks. Therefore, the applicability of the TSAs is experimentally demonstrated for a real-case assistive device, fostering future studies and developments of this kind of actuation strategy for wearable robotic systems.

The preliminary experimental study toward the implementation of an arm rehabilitation device based on a twisted string actuation module is presented. The actuation module is characterized by an integrated force sensor based on optoelectronic components. The adopted actuation system can be used for a wide set of robotic applications and is particularly suited for very compact, light-weight, and wearable robotic devices, such as wearable rehabilitation systems and exoskeletons. Thorough presentation and description of the proposed actuation module as well as the basic force sensor working principle are illustrated and discussed. A conceptual design of a wearable arm assistive system based on the proposed actuation module is presented. Moreover, the actuation module has been used in a simple assistive application, in which surface-electromyography signals are used to detect muscle activity of the user wearing the system and to regulate the support action provided to the user to reduce his effort, showing in this way the effectiveness of the approach.

In this article, the variety of approaches proposed in the literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macrocategories the great quantity of presented methods that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology, and declared goals of the mappings. First, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under the following six categories: direct joint, direct Cartesian, task-oriented, dimensionality reduction based, pose recognition based, and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors' point of view regarding future desirable trends are reported.

Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion.

In this article we present a sEMG-driven humanin-the-loop (HITL) control designed to allow an assistive robot produce proper support forces for both muscular effort compensations, i.e. for assistance in physical tasks, and muscular effort generations, i.e. for the application in muscle strength training exercises related to the elbow joint. By employing our control strategy based on a Double Threshold Strategy (DTS) with a standard PID regulator, we report that our approach can be successfully used to achieve a target, quantifiable muscle activity assistance. In this relation, an experimental concept validation was carried out involving four healthy subjects in physical and muscle strength training tasks, reporting with single-subject and global results that the proposed sEMG-driven control strategy was able to successfully limit the elbow muscular activity to an arbitrary level for effort compensation objectives, and to impose a lower bound to the sEMG signals during effort generation goals. Also, a subjective qualitative evaluation of the robotic assistance was carried out by means of a questionnaire. The obtained results open future possibilities for a simplified usage of the sEMG measurements to obtain a target, quantitatively defined, robot assistance for human joints and muscles.