Robot-Assisted Upper-Limb Rehabilitation Platform

Matteo Malosio, Nicola Pedrocchi, Lorenzo Molinari Tosatti Institute of Industrial Technologies and Automation

National Research Council Milan, Italy

matteo.malosio@itia.cnr.it

Abstract—This work presents a robotic platform for upper-limb rehabilitation robotics. It integrates devices for human multi-sensorial feedback for engaging and immersive therapies. Its modular software design and architecture allows the implementation of advanced control algorithms for effective and customized rehabilitations. A flexible communication infrastructure allows straightforward devices integration and system expandability.

Keywords: rehabilitation robotics; human-robot interaction; virtual haptics; open controller

I. INTRODUCTION Robot-assisted neuromuscular rehabilitation represents an

emerging important sector in the robotic field, and various studies has assessed its efficacy in the last decade [1-3]. On the other side, many uncertainties influence final results and few commercial solutions are currently available on the market so far, with high limitations in terms of potentialities.

Highlights in the history of rehabilitation robotics devices are the MIT-manus (MIT) [4,5], the MIME [6], followed by a number of other machines, here not reported for shortness. Even though the above-mentioned systems are advanced technological robotic system, it is worth to underline that the movements the patients can perform are usually simpler than the ones achievable by healthy people in real daily-life movements (many devices allow only planar movements). Few emphasis is put on control logics of the robot movement that as a result are simple, and the measure of force exerted by patient is usually integrated but not optimized.

It is within this global scenario that a multi-modal open robotic platform for the upper-arm rehabilitation is here presented. Effective neuromuscular rehabilitation needs not only passive movements of the human’s arm: robot and patient need really to collaborate to fulfill tasks of rehabilitation protocols. A force-based interaction between the patient and the robot is necessary in order to develop rehabilitative protocols based on the actual force generated by the patient. This imposes the integration in the robot controller of various and advanced control algorithms as force-based strategies and impedance algorithms; implementation of artificial potential fields on pre-defined trajectories, forces and virtual tunnel/elastic stripes in configuration space, on-line motion re-planning due to the posture of the patient during the task execution. The integration of multiple biosignal feedbacks (e.g. EMG) can enhance the level of rehabilitation outcomes, with,

on the other side, an increasing level of the complexity of implemented control strategies.

Since not only muscles but the whole neuromuscular apparatus need to be trained, multi-sensorial feedbacks to the patients are effective and enhance the rehabilitation quality [7]. In these terms the integration of an immersive virtual reality, for more and more engaging and daily-life task-oriented rehabilitation exercises with haptic feedbacks can have important relapses on the effectiveness of the performed exercises.

Authors have focused their activity in designing and implementing an open, modular and easy expandable control software architecture. This will allow to obtain a fast software implementation and testing of different control strategies, for the conception of new rehabilitation therapies and the adaptation of the control system to various robot and mechanical robotic structures.

Because of the strict interaction between the human and the robot, safety aspects cover an extremely important role and having in mind a sort of “flexibility” paradigm, a portable safety system, easily adaptable to various robotic systems, has been conceived, in order to allow human-robot collaboration and work-space sharing according to the guidelines of the new standard ISO 10218-2 in developing phase.

In the next sections the key features and functionalities of the rehabilitation platform in developing phase are illustrated. The platform is mainly composed by a robot arm, an open PC-based controller, various devices to allow human-robot interaction at different levels, and a safety system which guarantee the reliability of the whole system. In the last section conclusions and future developments are drawn.

II. SET-UP DESCRIPTION The robot arm employed in the platform is a Mitsubishi

PA-10-7DoFs robot. It is equipped with a 6-Axis force/torque sensor fixed at its end-effector to receive force feedbacks from the patient. Ergonomic devices connect the hand or the forearm of the human with the robotic arm.

The Pa10-robot control allows velocity/torque control of its motors. The robot is controlled by a Linux-based control system, exploiting the Xenomai real-time framework. Motor references are transmitted to robot drivers through the ARCnet protocol. The openness of the Pc-based control allows the

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implementation of complex control algorithms exploiting various patient feedbacks.

On the basis of force/torque sensor signal the patient can freely move the robot in the operating space. Complex path and motion laws (i.e. trajectory) can be recorded and executed. In details, paths are described as 3-dimensional analytical splines defined by a set of nodes, each of them defined by spatial position and orientation (as quaternion) of the robot end-effector. Motion laws can be freely assigned to each path, through a spline-interpolated set of velocities associated to curvilinear coordinate values. The spline-based description of paths and motion laws allows the execution of complex functional rehabilitative trajectories (e.g. functional daily-life movements), without being constrained to simple paths (e.g. linear or circular movements) or predefined motion-law profiles (e.g. constant velocity, trapezoidal velocity, etc.). Trajectories can be defined also using tracking systems used in rehabilitation clinics (e.g. passive-marker stereo-vision systems) and saved in a proper XML format. Direction and magnitude of the force feedback are used to scale/modify in real time both nominal predefined paths and associated motion laws. Both impedance-control and force-field based exercises can be easily implemented and tested by the patient.

As well as force signals various types of feedback information flow bidirectionally between the patient and the robotic platform: audio-visual feedbacks are received by the patient, while bio-signals are received by the robot control to allow the implementation and execution of more comprehensive customized control algorithms.

The whole system is interfaced with a tridimensional virtual graphic engine in order to develop engaging and immersive rehabilitative exercises. In order to increase the level of realism of virtual scenes the platform is provided with a tridimensional haptic feedback. Exploiting collision detection techniques embedded in the graphic engine and a bidirectional communication between the robot controller and the graphic engine, information about collisions occurred in the virtual world are used by the robot controller to give the users the sensation of a really occurred collision.

The platform incorporates a stereo-vision system with a twofold purpose: completely track upper-limb and trunk movements for clinical and control feedbacks; track robot movements for safety feedback (refer to the next section).

A graphic user interface has been implemented to properly control and monitor the robotic platform functionalities. It has been designed taking into account physiotherapist ergonomics using a touch-screen device.

Overall system flexibility and expandability is based on an object-oriented middleware, namely ZeroC™ Ice™ [8]. The above mentioned devices are integrated through properly designed software interfaces, allowing them to communicate among themselves and exchange application data. More and more devices are being connected while the project is developed, in order to increase the level of immersion of the patient and the quantity of feedbacks to the robot control.

III. SAFETY A safe PLC is in charge of guaranteeing the reliability of

the robotic platform. Two safety aspects are crucial and supervised by the safe PLC: 1) correct robot position with respect to control position reference; 2) workspace safe limitation, in order to avoid robot collisions against the patient or robot movements which can result dangerous. The assessment of correct robot position is obtained comparing the robot position computed by the robot control and the actual robot position observed by the 3-dimensional vision tracking system. Moreover geometrical algorithms verify the correct position of the end-effector in an allowed workspace zone. In case any of the assessment test has a negative outcome the safety chain would be opened and the robot safely halted.

IV. CONCLUSIONS AND FUTURE DEVELOPMENTS Preliminary tests have been performed on healthy subjects

to verify the usability of the system and to correlate electromyographical signals with humans’ arm movements.

Next control developments will include the implementation of a gravity compensation algorithm to let the system be used by patients not able to support the weight of their own arms because of neuromuscular impairments, and the implementation of control algorithms integrating biofeedback signals (e.g. EMG, EEG) and stimulation techniques as FES. The controller will also be endowed with a biomechanical model of the upper-limb, integrated with signals received from electromyographic sensors. The modularity of the developed control architecture will allow it to be interfaced with an innovative exoskeleton in developing phase for the upper limb rehabilitation.

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[7] B. I. Molier, E. H. F. v. Asseldonk, H. Hermens, M. J.A. Jannink, “Effect of different types of augmented feedback in stroke rehabilitation training: a systematic review”, Technically Assisted Rehabilitation Conference (TAR) 2009, March, 2009 Berlin.

[8] Ice™ ZeroC™ - http://www.zeroc.com/

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