Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients’ status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) “high-level” training modalities, 2) “low-level” control strategies, and 3) “hardware-level” implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.

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To develop and evaluate a hybrid robotic system for arm recovery after stroke, combining EMG-triggered Functional Electrical Stimulation (FES) with a passive exoskeleton for upper limb suspension. Methods: The system was used in a structured exercise program resembling activities of daily life. Exercises execution was continuously controlled using angle sensor data and Radio-Frequency IDentification (RFID) technology. The training program consisted of 27 sessions lasting 30 minutes each. Seven post-acute stroke patients were recruited from two clinical sites. The efficacy of the system was evaluated in terms of Action Research Arm Test, Motricity Index, Motor Activity Log, and Box & Blocks tests. Furthermore, kinematicsbased and EMG-based outcome measures were derived directly from data collected during training sessions. Results: All patients showed an improvement of motor functions at the end of the training program. After training, the exercises were in most cases executed faster, smoother and with an increased range of motion. Subjects were able to trigger FES, but in some cases, they did not maintain the voluntary effort during task execution. All subjects but one considered the system usable. Conclusion: The preliminary results showed that the system can be used in a clinical environment with positive effects on arm functional recovery. However, only the final results of the currently ongoing clinical trial will unveil the system full potential. Significance: The presented hybrid robotic system is highly customizable, allows to monitor the daily performance, requires low supervision of the therapist and might have the potential to enhance arm recovery after stroke.

In this study, we address the inverse kinematics problem for an upper-limb exoskeleton by presenting a novel method that guarantees the satisfaction of joint-space constraints, and solves closed-chain mechanisms in a serial robot configuration. Starting from the conventional differential kinematics method based on the inversion of the Jacobian matrix, we describe and test two improved algorithms based on the Projected-Gradient method, that take into account jointspace equality constraints. We use the Harmony exoskeleton as a platform to demonstrate the method. Specifically, we address the joint constraints that the robot maintains in order to match anatomical shoulder movement and the closed-chain mechanisms used for the robot's joint control. Results show good performances of the proposed algorithms, which are confirmed by the ability of the robot to follow the desired taskspace trajectory while ensuring the fulfilment of joint-space constraints, with a maximum error of about 0.05 degrees.

The aim of this study concerns the evaluation and comparison of different Human-Machine Interfaces for the control of an upper limb motorized exoskeleton for severely impaired patients. Different approaches (i.e. manual, vocal, visual control) are tested in a simulation environment on three subjects affected by muscular dystrophy with the aim of assessing the capability of the system to interact with the user and vice versa. A Graphical User Interface shows the simulated behavior of the exoskeleton to the user which has to perform reaching tasks in the space by moving the exoskeleton endeffector to defined virtual targets that are displayed on the screen. Specific assessment of the interaction of the user with each control interface is achieved, while a quantitative evaluation of the usability of all the three approaches is provided by a System Usability Scale (SUS) questionnaire. All patients were able to interact with all control interfaces without difficulties and to complete reaching tasks in simulation. SUS scores showed overall good usability of the Human-Machine Control Interfaces suggesting that the manual and the vocal control interfaces are preferred by the subjects.