The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.

Stroke survivors with chronic paralysis often have difficulties to perform various activities of daily living (ADLs), such as preparing a meal or eating and drinking independently. Recently, it was shown that a brain/neural hand exoskeleton can restore hand and finger function, but many stroke survivors suffer from motor deficits affecting their whole upper limb. Therefore, novel hybrid electroencephalography/electrooculography (EEG/EOG)-based brain/neural control paradigms were developed for guiding a whole-arm exoskeleton. It was unclear, however, whether hemiplegic stroke survivors are able to reliably use such brain/neural-controlled device. Here, we tested feasibility, safety, and user-friendliness of EEG/EOG-based brain/neural robotic control across five hemiplegic stroke survivors engaging in a drinking task that consisted of several subtasks (e.g., reaching, grasping, manipulating, and drinking). Reliability was assumed when at least 75% of subtasks were initialized within 3 s. Fluent control was assumed if average “time to initialize” each subtask ranged below 3 s. System's safety and user-friendliness were rated using Likert-scales. All chronic stroke patients were able to operate the system reliably and fluently. No undesired side effects were reported. Four participants rated the system as very user-friendly. These results show that chronic stroke survivors are capable of using an EEG/EOG-controlled semiautonomous whole-arm exoskeleton restoring ADLs.

Tactile sensing is fundamental for the human hand to achieve high dexterity. Most prosthetic hands are still devoid of tactile sensors, implying that the user cannot perceive external stimulation nor react in a fine manner. As a consequence, unforeseen events, e.g., slippage, are difficult to manage. This article proposes an algorithm to perform slippage detection with tactile sensors integrated into prosthetic hands. The algorithm is based on the filtering of the tactile sensor output; rectification and envelope follow the filtering. A binary signal, relating to slippage, is finally computed. An electrical circuit has been designed and built to elaborate the tactile signals. These have been embedded in a bioinspired fingertip mounted on a prosthetic hand, which has been interfaced with a robotic arm to assess the algorithm capability to identify slippage. Eight different surfaces have been employed, while three sliding velocities have been tested with a random interaction force between the fingertip and the test surfaces. Finally, experiments in a closed-loop configuration have been conducted to demonstrate the algorithm effectiveness in dynamic manipulation. Results proved the adequacy of the algorithm in terms of slippage detection and short latency between onset of slippage, actual detection and hand reaction.

Physical therapy keeps exploiting more and more the capabilities of the robot of adapting the treatments to patients’ needs. This paper aims at presenting a psychophysiological-aware control strategy for upper limb robot-aided orthopedic rehabilitation. The main features are the capability of i) generating point-to-point trajectories inside an adaptable workspace, ii) providing assistance in guiding the patients’ limbs in accomplishing the assigned task allowing them to freely move with a certain degree of spatial and temporal autonomy and iii) tuning the control parameters according to the patients’ kinematics performance and psychophysiological state. The implemented control strategy is validated in a real clinical setting on eight orthopedic patients undergoing twenty daily robot-aided rehabilitation sessions. The psychophysiological-aware control strategy evidenced a positive impact on the enrolled participants since they are effectively conducted in a calmer condition with respect to the patients who did not receive the psychophysiological adaptation. Moreover, clinical performance indicators suggest that the proposed tailored control strategy improves motor functions.