Frontiers in Neurorobotics

Robot-based rehabilitation is consolidated as a viable and efficient practice to speed up and improve the recovery of lost functions. Several studies highlight that patients are encouraged to undergo their therapies and feel more involved in the process when collaborating with a user-friendly robotic environment. Object manipulation is a crucial element of hand rehabilitation treatments; however, as a standalone process may result in being repetitive and unstimulating in the long run. In this view, robotic devices, like hand exoskeletons, do arise as an excellent tool to boost both therapy's outcome and patient participation, especially when paired with the advantages offered by interacting with virtual reality (VR). Indeed, virtual environments can simulate real-life manipulation tasks and real-time assign a score to the patient's performance, thus providing challenging exercises while promoting training with a reward-based system. Besides, they can be easily reconfigured to match the patient's needs by manipulating exercise intensity, e.g., Assistance-As-Needed (AAN) and the required tasks. Modern VR can also render interaction forces when paired to wearable devices to give the user some sort of proprioceptive force or tactile feedback. Motivated by these considerations, a Hand Exoskeleton System (HES) has been designed to be interfaced with a variable admittance control to achieve VR-based rehabilitation tasks. The exoskeleton assists the patient's movements according to force feedback and following a reference value calculated inside the VR. Whenever the patient grasps a virtual object, the HES provides the user with a force feedback sensation. In this paper, the virtual environment, developed within the Webots framework and rendering a HES digital-twin mapping and mimicking the actual HES motion, will be described in detail. Furthermore, the admittance control strategy, which continuously varies the control parameters to best render the force sensation and adapt to the user's motion intentions, will be investigated. The proposed approach has been tested on a single subject in the framework of a pilot study.

Brain-computer interface (BCI) for robotic arm control has been studied to improve the life quality of people with severe motor disabilities. There are still challenges for robotic arm control in accomplishing a complex task with a series of actions. An efficient switch and a timely cancel command are helpful in the application of robotic arm. Based on the above, we proposed an asynchronous hybrid BCI in this study. The basic control of a robotic arm with six degrees of freedom was a steady-state visual evoked potential (SSVEP) based BCI with fifteen target classes. We designed an EOG-based switch which used a triple blink to either activate or deactivate the flash of SSVEP-based BCI. Stopping flash in the idle state can help to reduce visual fatigue and false activation rate (FAR). Additionally, users were allowed to cancel the current command simply by a wink in the feedback phase to avoid executing the incorrect command. Fifteen subjects participated and completed the experiments. The cue-based experiment obtained an average accuracy of 92.09%, and the information transfer rates (ITR) resulted in 35.98 bits/min. The mean FAR of the switch was 0.01/min. Furthermore, all subjects succeeded in asynchronously operating the robotic arm to grasp, lift, and move a target object from the initial position to a specific location. The results indicated the feasibility of the combination of EOG and SSVEP signals and the flexibility of EOG signal in BCI to complete a complicated task of robotic arm control.