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

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

 M. Barsotti
∗
, D. Leonardis
∗
, C. Loconsole
 ∗
, M. Solazzi
∗
, E. Sotgiu
∗
C. Procopio
∗
, C. Chisari
§
, M. Bergamasco
∗
, A. Frisoli
∗
∗
PERCRO Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna , Pisa (Italy)
§
Neurorehabilitation Unit, Department of Neurosciences, Azienda Ospedaliera Universitaria Pisana, Pisa (Italy)

 Abstract

—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 preshaping for object affordability and it is driven by patient’s intentional control through a self-paced asynchronous Motor Imagery based Brain Computer Interface (MI-BCI). The developed anthropomorphic eight DoFs exoskeleton (two DoFs for the hand, two for the wrist and fourfor 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.

I. INTRODUCTION

The majority of stroke survivors suffers a poorly functioning hemiparetic arm and hand. Robotic-assisted rehabilitation in stroke, consisting of active and highly repetitive movements, is able to induce motor functional recovery with: (i) improved generation of movement (movement completion time, smoothness of motion); (i) more appropriate recruitment of joint muscle groups; and (iii) improved inter- joint coordination of elbow and shoulder joints ([1]–[3]). Furthermore, the assistance to both reaching and grasping movements which takes into account preshaping of hand for object affordability, uniquely combines rehabilitation training for grasping of both distal and proximal upper extremity segments.

Several robotic-assisted rehabilitation approaches, involving the whole hand and arm motor functions, have been proposed. For example, in [4], authors proposed a robotic extension of the planar MIT-MANUS device for supporting hand grasping through a cylindrical handle with varying radius. However, motor tasks in Activities of Daily Living(ADLs) require movements to be performed (and assisted) in the three dimensional real world. This feature is a distinctive prerogative of some rehabilitation devices, i.e. exoskeletons systems for both upper and lower limbs. By mimicking kinematics of the human body, exoskeletons allow to actively support human like movements at the level of single joints.

Focusing on upper limb exoskeletons, it is possible to distinguish between
 arm and hand exoskeletons
.
 Arm exoskeletons allow patients to perform arm movements requiring more complex inter-joint coordination and counter-balancing gravity. They can be grouped into active (usually
Fig. 1. The full upper limb exoskeleton integrated with the MI-BCI system
based on impedance control paradigm, e.g., Armin [5] and L-Exos [6]) and passive (Armeo, T-WREX [7]) exoskeletons.
 Hand exoskeletons, instead, range from multi-finger haptic devices (e.g., [8]) to commercial products (e.g., CyberGrasp www.vrlogic.de). Fontana et al.
 developed a hand exoskeleton for providing accurate force feedback on fingers [9],whereas Iqbal et al.
 developed an interface for hand motion assistance [10]. 

Integration of a arm and hand exoskeletons in neurorehabilitation scenarios may provide more effective rehabilitative outcomes. Ren
 et al.
 developed a whole arm exoskeleton robot with active hand opening and closing mechanism[11]. However, the specific design of the mechanism, did not allow the grasping of real objects, thus limiting the use of the rehabilitation paradigm to virtual environments. Such strong limitation is in contrast to common therapist assisted scenarios, in which the patient interacts with real objects receiving a rich sensory feedback close to the everyday experience.

Moreover, it has been proven that normal sensory feed-back congruent to motor intention can induce brain plasticity,improving restoration of more normal brain functions after stroke or brain injury [12], [13]. It follows that sensory feedback provided by the robotic-assisted motor execution has to be performed congruently with motor intention of the patient. Motor Imagery (MI) represents an intriguing rehabilitation approach for sensorimotor training. In this  context, research involving Brain-Computer Interface (BCI)based on MI is advancing very rapidly and is showing encouraging results [14]–[16]. An interesting study involving exoskeletons driven by MI-BCI is represented by [17], in which the results of a clinical study show evidence that MI-BCI-driven robotic rehabilitation can be effective in restoring motor control in stroke. A more recent study has been conducted by Ramos
 et al. [18], which confirms, with a clinical study involving 32 stroke patients, results obtained by Ang
 et al. in [17]. Both reported studies used MI-BCI asa trigger for robotic-assisted movement execution.

Following this line of research, in this work we propose a full upper limb robotic exoskeleton triggered by MI-BCI for the rehabilitation of reaching and grasping/releasing in stroke. The developed system supplies rich sensory feedback close to natural and common real world experience and has been envisaged for scenarios involving goal directed activities related to ADLs (e.g., pick-and-place tasks with real objects). The self-paced MI-BCI driving robotic assistance was introduced in order to encourage the patient to perform self initiated and controlled movements.

Finally, we evaluated the feasibility of the proposed ap-proach through experimental activity involving three chronic stroke patients. The proposed system, depicted in Figure 1, is composed of the full upper limb exoskeleton (named BRAVO exoskeleton) and a MI-BCI system. The logical scheme, instead, is reported in Figure 2. Next Sections describe the structure of the exoskeleton, the implemented control strategy and the MI-BCI system.

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