Vincent Crocher

Robotic Exoskeletons: A Perspective for the Rehabilitation of Arm Coordination in Stroke Patients

Upper-limb impairment after stroke is caused by weakness, loss of individual joint control, spasticity, and abnormal synergies. Upper-limb movement frequently involves abnormal, stereotyped, and fixed synergies, likely related to the increased use of sub-cortical networks following the stroke. The flexible coordination of the shoulder and elbow joints is also disrupted. New methods for motor learning, based on the stimulation of activity-dependent neural plasticity have been developed. These include robots that can adaptively assist active movements and generate many movement repetitions. However, most of these robots only control the movement of the hand in space. The aim of the present text is to analyze the potential of robotic exoskeletons to specifically rehabilitate joint motion and particularly inter-joint coordination. First, a review of studies on upper-limb coordination in stroke patients is presented and the potential for recovery of coordination is examined. Second, issues relating to the mechanical design of exoskeletons and the transmission of constraints between the robotic and human limbs are discussed. The third section considers the development of different methods to control exoskeletons: existing rehabilitation devices and approaches to the control and rehabilitation of joint coordinations are then reviewed, along with preliminary clinical results available. Finally, perspectives and future strategies for the design of control mechanisms for rehabilitation exoskeletons are discussed.

Constraining Upper Limb Synergies of Hemiparetic Patients Using a Robotic Exoskeleton in the Perspective of Neuro-Rehabilitation

The aim of this paper was to explore how an upper limb exoskeleton can be programmed to impose specific joint coordination patterns during rehabilitation. Based on rationale which emphasizes the importance of the quality of movement coordination in the motor relearning process, a robot controller was developed with the aim of reproducing the individual corrections imposed by a physical therapist on a hemiparetic patient during pointing movements. The approach exploits a description of the joint synergies using principal component analysis (PCA) on joint velocities. This mathematical tool is used both to characterize the patients movements, with or without the assistance of a physical therapist, and to program the exoskeleton during active-assisted exercises. An original feature of this controller is that the hand trajectory is not imposed on the patient: only the coordination law is modified. Experiments with hemiparetic patients using this new active-assisted mode were conducted. Obtained results demonstrate that the desired inter-joint coordination was successfully enforced, without significantly modifying the trajectory of the end point.

A Methodology to Quantify Alterations in Human Upper Limb Movement During Co-Manipulation With an Exoskeleton

While a large number of robotic exoskeletons have been designed by research teams for rehabilitation, it remains rather difficult to analyse their ability to finely interact with a human limb: no performance indicators or general methodology to characterize this capacity really exist. This is particularly regretful at a time when robotics are becoming a recognized rehabilitation method and when complex problems such as 3-D movement rehabilitation and joint rotation coordination are being addressed. The aim of this paper is to propose a general methodology to evaluate, through a reduced set of simple indicators, the ability of an exoskeleton to interact finely and in a controlled way with a human. The method involves measurement and recording of positions and forces during 3-D point to point tasks. It is applied to a 4 degrees-of-freedom limb exoskeleton by way of example.

Learning control in robot-assisted rehabilitation of motor skills – a review

The key idea in iterative learning control is captured by the intuition of ‘practice makes perfect’. The underlying learning is based on a gradient descent algorithm iteratively optimising an appropriate input–output measured criterion. How this paradigm is used to model quantitatively, at an input/output level, the learning that happens in the context of human motor skill learning is discussed in this note. Experimental studies of human motor learning, in robotically controlled environments, indicate that a model consisting of a classical (iterative) learning control augmented with an appropriate kinematic model of human motor motion fits the observed human learning behaviour well. In the context of the rehabilitation of motor skills, such models promise better human–machine interfaces that extend the capability and capacity of rehabilitation clinicians by creating effective robot–patient–clinician feedback loops. The economic promise of robot-assisted rehabilitation is to greatly extend the intervention...

An investigation into the reliability of upper-limb robotic exoskeleton measurements for clinical evaluation in neurorehabilitation

Robotic exoskeletons are increasingly being used for neurorehabilitation, due to a number of perceived advantages. Once such advantage is the potential to use the large amounts of previously unavailable measurements to provide continuous assessment of the patient. This study investigates the validity of such measurements through an experimental protocol. Reaching movements within and outside an upper-arm rehabilitation exoskeleton (ArmeoPower) of 10 healthy subjects are compared using five commonly-used kinematic metrics (Peak Speed, Time to Peak Speed, Curvature, Smoothness, Accuracy). The study finds that (1) the robotic exoskeleton significantly affects the reaching movements of healthy subjects, (2) the measurements of the exoskeleton accurately represent the movements of the wrist, and (3) evolution of the in-exoskeleton movements over multiple sessions is indicative of changes in movements outside the robot, even though differences remain - suggesting that evolution of this data may be used to monitor patient progress.

A Method for measuring the upper limb motion and computing a compatible exoskeleton trajectory

This paper deals with the problem of computing trajectories for an exoskeleton that match a motion recorded on a given subject. Literature suggests that this problem can be solved by reconstructing the subjects joint motion using one of the numerous models available, and then feeding the exoskeleton with the joint trajectories. This is founded on the assumption that the exoskeleton kinematics reproduces the human kinematics. In practice, though, mismatches are unavoidable and lead to inaccuracies. We thus developed a method that is primarily based on an appropriate mechanical design: passive mechanisms are used to connect the exoskeleton with splints wore by the subject, in such a way that, within the workspace, there always exists a posture of the exoskeleton compatible with a given position and orientation of the splints. The trajectory computing method, by itself, consists of recording the position and orientation of the splints thanks to a conventional 3D motion tracker and to exploit standard robotics tools in order to compute an exoskeleton posture compatible with the measured human posture. Conclusive experimental results involving an existing 4 DoF upper-limb exoskeleton are shown.

Modifying Kinect placement to improve upper limb joint angle measurement accuracy

STUDY DESIGN Repeated measures. INTRODUCTION The Kinect (Microsoft, Redmond, WA) is widely used for telerehabilitation applications including rehabilitation games and assessment. PURPOSE OF THE STUDY To determine effects of the Kinect location relative to a person on measurement accuracy of upper limb joint angles. METHODS Kinect error was computed as difference in the upper limb joint range of motion (ROM) during target reaching motion, from the Kinect vs 3D Investigator Motion Capture System (NDI, Waterloo, Ontario, Canada), and compared across 9 Kinect locations. RESULTS The ROM error was the least when the Kinect was elevated 45° in front of the subject, tilted toward the subject. This error was 54% less than the conventional location in front of a person without elevation and tilting. The ROM error was the largest when the Kinect was located 60° contralateral to the moving arm, at the shoulder height, facing the subject. The ROM error was the least for the shoulder elevation and largest for the wrist angle. DISCUSSION Accuracy of the Kinect sensor for detecting upper limb joint ROM depends on its location relative to a person. CONCLUSION This information facilitates implementation of Kinect-based upper limb rehabilitation applications with adequate accuracy. LEVEL OF EVIDENCE 3b.

Changing human upper-limb synergies with an exoskeleton using viscous fields

Robotic exoskeletons can apply forces distributed on the limbs of the subject they are connected to. This offers a great potential in the field of neurorehabilitation, to address the impairment of interjoint coordination in hemiparetic stroke patients. In these patients, the normal flexible joint rotation synergies are replaced by pathological fixed patterns of rotation. In this paper, we investigate how the concept of synergy can be exploited in the control of an upper limb exoskeleton. The long term goal is to develop a device capable of changing the joint synchronization of a patient performing exercises during rehabilitation.

Effects of robotic exoskeleton dynamics on joint recruitment in a neurorehabilitation context

Increasing research has been conducted into the use of robotic devices for neurorehabilitation. One advantage of these devices over traditional rehabilitation is the availability of measured data, which can be used to inform potential patient-specific protocol for recovery or simply to provide higher frequency feedback to the patients and therapists. It has previously been identified that such devices may have unplanned effects on the movement of patients. However, the exact nature of these effects are unknown, which makes the meaning of any measured data less clear. As such, this study investigates the effect of the mechanical dynamics of a robotic exoskeleton (ArmeoPower, Hocoma, Switzerland) on the movements of healthy subjects - particularly with respect to the movements of the shoulder, and joint utilisation. The study finds that the exoskeleton may encourage changes in shoulder movement in both magnitude and direction and changes in the joints recruited for the movement. Furthermore, the effects of the robot on joint utilisation are not consistent across reaching directions, however, the peak joint velocities are decreased across all joints and reaching directions.

A Tool to Address Movement Quality Outcomes of Post-stroke Patients

A new neuro-rehabilitation system is proposed to address the movement quality of post-stroke patients. The system is designed to be used concurrently with existing upper-extremity virtual rehabilitation devices, and to aide correction of compensatory trunk and shoulder movements. A 3D sensor is utilised to estimate the movement of the shoulder, and an auditory cue is given to the patient when the system estimates that a compensatory movement has been made. The results of preliminary trials of this system on a single patient are presented.