Michele Barsotti

A New Gaze-BCI-Driven Control of an Upper Limb Exoskeleton for Rehabilitation in Real-World Tasks

This paper proposes a new multimodal architecture for gaze-independent brain-computer interface (BCI)-driven control of a robotic upper limb exoskeleton for stroke rehabilitation to provide active assistance in the execution of reaching tasks in a real setting scenario. At the level of action plan, the patients intention is decoded by means of an active vision system, through the combination of a Kinect-based vision system, which can online robustly identify and track 3-D objects, and an eye-tracking system for objects selection. At the level of action generation, a BCI is used to control the patients intention to move his/her own arm, on the basis of brain activity analyzed during motor imagery. The main kinematic parameters of the reaching movement (i.e., speed, acceleration, and jerk) assisted by the robot are modulated by the output of the BCI classifier so that the robot-assisted movement is performed under a continuous control of patients brain activity. The system was experimentally evaluated in a group of three healthy volunteers and four chronic stroke patients. Experimental results show that all subjects were able to operate the exoskeleton movement by BCI with a classification error rate of 89.4±5.0% in the robot-assisted condition, with no difference of the performance observed in stroke patients compared with healthy subjects. This indicates the high potential of the proposed gaze-BCI-driven robotic assistance for neurorehabilitation of patients with motor impairments after stroke since the earliest phase of recovery.

An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation

This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patients hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patients non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.

A novel BCI-SSVEP based approach for control of walking in Virtual Environment using a Convolutional Neural Network

A non-invasive Brain Computer Interface (BCI) based on a Convolutional Neural Network (CNN) is presented as a novel approach for navigation in Virtual Environment (VE). The developed navigation control interface relies on Steady State Visually Evoked Potentials (SSVEP), whose features are discriminated in real time in the electroencephalographic (EEG) data by means of the CNN. The proposed approach has been evaluated through navigation by walking in an immersive and plausible virtual environment (VE), thus enhancing the involvement of the participant and his perception of the VE. Results show that the BCI based on a CNN can be profitably applied for decoding SSVEP features in navigation scenarios, where a reduced number of commands needs to be reliably and rapidly selected. The participant was able to accomplish a waypoint walking task within the VE, by controlling navigation through of the only brain activity.

An emg-based robotic hand exoskeleton for bilateral training of grasp

This work presents the development and the preliminary experimental assessment of a novel EMG-driven robotic hand exoskeleton for bilateral active training of grasp motion in stroke. The system allows to control the grasping force required to lift a real object with an impaired hand, through the active guidance provided by a hand active exoskeleton, whose force is modulated by the EMG readings acquired on the opposite unimpaired arm. To estimate the grasping force, the system makes use of surface EMG recordings during grasping, developed on the opposite unimpaired arm, and of a neural network to classify the information. The design, integration and experimental characterization of the system during the grasp of two cylindrical objects is presented. The experimental results show that an optimal force tracking of the interaction force with the object can be achieved.

A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI

In this paper we propose a full upper limb exoskeleton for motor rehabilitation of reaching, grasping and releasing in post-stroke patients. The presented system takes into account the hand pre-shaping for object affordability and it is driven by patients intentional control through a self-paced asynchronous Motor Imagery based Brain Computer Interface (MI-BCI). The developed antropomorphic eight DoFs exoskeleton (two DoFs for the hand, two for the wrist and four for the arm) allows full support of the manipulation activity at the level of single upper limb joint. In this study, we show the feasibility of the proposed system through experimental rehabilitation sessions conducted with three chronic post-stroke patients. Results show the potential of the proposed system for being introduced in a rehabilitation protocol.

A neuromusculoskeletal model of the human upper limb for a myoelectric exoskeleton control using a reduced number of muscles

This paper presents a myoelectric control of an arm exoskeleton designed for rehabilitation. A four-muscles-based NeuroMusculoSkeletal (NMS) model was implemented and optimized using genetic algorithms to adapt the model to different subjects. The NMS model is able to predict the shoulder and elbow torques which are used by the control algorithm to ensure a minimal force of interaction. The accuracy of the method is assessed through validation experiments conducted with two healthy subjects performing free movements along the pseudo-sagittal plane. The experiments show promising results for our approach showing its potential for being introduced in a rehabilitation protocol.

A Novel Approach for Upper Limb Robotic Rehabilitation for Stroke Patients

This paper presents a novel neuro-rehabilitation system for recovery of arm and hand motor functions involved in reaching and grasping. The system provides arm weight support and robotic assistance of the hand closing/opening within specific exercises in virtual reality. A user interface allows the clinicians to perform an easy parametrization of the virtual scenario, customizing the exercises and the robotic assistance to the needs of the patient and encouraging training of the hand with proper recruitment of the residual motor functions. Feasibility of the proposed rehabilitation system was evaluated through an experimental rehabilitation session, conducted by clinicians with 4 healthy participants and 2 stroke patients. All subjects were able to perform the proposed exercises with parameters adapted to their specific motor capabilities. All patients were able to use the proposed system and to accomplishing the rehabilitation tasks following the suggestion of the clinicians. The effectiveness of the proposed neuro-rehabilitation will be evaluated in an imminent prolonged clinical study involving more stroke patients.

ADITHO – A Serious Game for Training and Evaluating Medical Ethics Skills

This paper presents “A Day In The HOspital”, a Digital Serious Game aiming at providing a technological tool for both evaluating and training ethical skills of medical staff personnel. During the game, the player interprets the role of a physician who has to perform a decision-making process that involves his ethical and medical skills. Usability and sense of Presence have been assessed through a specific post-game Likert-questionnaire.

Evaluating Virtual Embodiment with the ALEx Exoskeleton

The assessment of virtual embodiment has focused primarily on experimental paradigms based on multisensory congruent cues, such as auditory, tactile, visual and motor, mainly due to the technological limitations of haptic feedback. In this work virtual embodiment in an avatar is assessed by means of a new lightweight exoskeleton (ALEx) with a focus on the perception of danger and aggressive behavior. In particular an experiment has been designed assessing the effectiveness of haptic feedback while interacting with an opponent avatar. Experiments are evaluated based on physiological measures and questionnaires.

Effects of Continuous Kinaesthetic Feedback Based on Tendon Vibration on Motor Imagery BCI Performance

Background and objectives: Feedback plays a crucial role for using brain computer interface systems. This paper proposes the use of vibration-evoked kinaesthetic illusions as part of a novel multisensory feedback for a motor imagery (MI)-based BCI and investigates its contributions in terms of BCI performance and electroencephalographic (EEG) correlates. Methods: sixteen subjects performed two different right arm MI-BCI sessions: with the visual feedback only and with both visual and vibration-evoked kinaesthetic feedback, conveyed by the stimulation of the biceps brachi tendon. In both conditions, the sensory feedback was driven by the MI-BCI. The rich and more natural multisensory feedback was expected to facilitate the execution of MI, and thus to improve the performance of the BCI. The EEG correlates of the proposed feedback were also investigated with and without the performing of MI. Results and Conclusions: the contribution of vibration-evoked kinaesthetic feedback led to statistically higher BCI performance (Anova, F(1,14) = 18.1, p < .01) and more stable EEG event-related-desynchronization. Obtained results suggest promising application of the proposed method in neuro-rehabilitation scenarios: the advantage of an improved usability could make the MI-BCIs more applicable for those patients having difficulties in performing kinaesthetic imagery.