Arm and finger paralysis, e.g. due to brain stem stroke, often results in the inability to perform activities of daily living (ADLs) such as eating and drinking. Recently, it was shown that a hybrid electroencephalography/electrooculography (EEG/EOG) brain/neural hand exoskeleton can restore hand function to quadriplegics, but it was unknown whether such control paradigm can be also used for fluent, reliable and safe operation of a semi-autonomous whole-arm exoskeleton restoring ADLs. To test this, seven abled-bodied participants (seven right-handed males, mean age 30 ± 8 years) were instructed to use an EEG/EOG-controlled whole-arm exoskeleton attached to their right arm to perform a drinking task comprising multiple sub-tasks (reaching, grasping, drinking, moving back and releasing a cup). Fluent and reliable control was defined as average ‘time to initialize’ (TTI) execution of each sub-task below 3 s with successful initializations of at least 75% of sub-tasks within 5 s. During use of the system, no undesired side effects were reported. All participants were able to fluently and reliably control the vision-guided autonomous whole-arm exoskeleton (average TTI 2.12 ± 0.78 s across modalities with 75% successful initializations reached at 1.9 s for EOG and 4.1 s for EEG control) paving the way for restoring ADLs in severe arm and hand paralysis.

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.

This article presents the design of a hand exoskeleton that features its modularity and the possibility of integrating a force sensor in its frame. The modularity is achieved by dividing the exoskeleton in separate units, each one driving a finger or pair of them. These units or “finger modules” have a single degree of freedom and may be easily attached or removed from the robot frame and human fingers by snap-in fixations. As for the force sensing capability, the device relies on a novel force sensor that uses optical elements to amplify and measure small elastic deformations in the robot structure. This sensor can be fully integrated as a structural element of the finger module. The proposed technology has been validated in two experimental sessions. A first study was performed in a clinical environment in order to check whether the hand exoskeleton (without the integrated force sensor) can successfully move an impaired hand in a “Mirror Therapy” environment. A second study was carried with healthy subjects to check the technical feasibility of using the integrated force sensor as a human–machine interface.

In this paper, the analysis of the intensity of muscle activations in different subjects when they perform an industrial task in a repetitive way assisted by a robotic upper-limb exoskeleton is presented. To do that, surface electromyography (EMG) signals were monitored with and without a robotic upper-limb exoskeleton for 10 subjects during a drilling task, a typical tedious maintenance or industrial task. Our results show that wearing the upper-limb exoskeleton substantially reduces muscle activity during a drilling task above head height. Specifically, there is statistically significant differences in the pectoralis major and rhomboids muscles between the groups wearing or not wearing the robotic upper-limb exoskeleton.

This work deals with the complex mechanical design task of converting a large pneumatic rehabilitation robot into an electric and compact system for in-home post-stroke therapies without losing performance. It presents the new HomeRehab robot that supports rehabilitation therapies in three dimensions with an adaptive controller that optimizes patient recovery. A preliminary usability test is also conducted to show that its performance resembles that found in RoboTherapist 2D commercial system designed for hospitals. The mechanical design of a novel and smart two-dimensional force sensor at the end-effector is also described.