339 research outputs found

    evaluation of upper limb sense of position in healthy individuals and patients after stroke

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    The aims of this study were to develop and evaluate reliability of a quantitative assessment tool for upper limb sense of position on the horizontal plane. We evaluated 15 healthy individuals (controls) and 9 stroke patients. A robotic device passively moved one arm of the blindfolded participant who had to actively move his/her opposite hand to the mirror location in the workspace. Upper-limb's position was evaluated by a digital camera. The position of the passive hand was compared with the active hand's 'mirror' position. Performance metrics were then computed to measure the mean absolute errors, error variability, spatial contraction/expansion, and systematic shifts. No significant differences were observed between dominant and non-dominant active arms of controls. All performance parameters of the post-stroke group differed significantly from those of controls. This tool can provide a quantitative measure of upper limb sense of position, therefore allowing detection of changes due to rehabilitation

    Kinematic and Kinetic Comparisons of Arm and Hand Reaching Movements with Mild and Moderate Gravity-Supported, Computer-Enhanced Armeo®spring: A Case Study

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    Background: Stroke has been recognized as a leading cause of serious long-term disability in the United States (U.S.) with 795,000 people experience a new or recurrent stroke each year (Roger et al., 2011). The most apparent defect after stroke is motor impairments (Masiero, Armani, & Rosati, 2011). Statistically, half of stroke survivors suffer from upper extremity hemiparesis and approximately one quarter become dependent in activities of daily living (Sanchez et al., 2006). There is strong evidence that intensity and task specificity are the main drivers in an effective treatment program after stroke. In addition, this training should be repetitive, functional, meaningful, and challenging for a patient (Van Peppen et al., 2004). The use of robotic systems to complement standard poststroke multidisciplinary programs is a recent approach that looks very promising. Robotic devices can provide high-intensity, repetitive, task-specific, interactive treatment of the impaired limb and can monitor patients\u27 motor progress objectively and reliably, measuring changes in quantitative movement kinematics and forces (Masiero, Armani, & Rosati, 2011). Objective: The purpose of this study was to examine the role of Armeo®Spring (Hocoma, Inc.), a gravity-supported, computer-enhanced robotic devise, on reaching movements while using two different gravity-support levels (mild and moderate weight support) on individuals with stroke. Methods: One stroke subject and one gender-matched healthy control participated in this study after gaining their informed consent. Both subjects performed a computer-based game (picking apples successfully and placing them in a shopping cart) under two gravity weight-support conditions (mild and moderate) provided by the Armeo®Spring device. The game tasks were described as a reaching cycle which consisted of five phases (initiation, reaching, grasping, transporting, and releasing). Joint angles for the glenohumeral and elbow joints throughout the reaching cycle were found. Three kinematic parameters (completion time, moving velocity, acceleration) and one kinetic parameter (vertical force acting on the forearm) was calculated for various instances and phases of the reaching motion. In addition, the muscle activation patterns for anterior deltoid, middle deltoid, biceps, triceps, extensor digitorum, flexor digitorum, and brachioradialis were found and the mean magnitude of the electromyography (EMG) signal during each phase of the reaching cycle was found as a percentage of the subject\u27s maximum voluntary contraction (MVC). Results: Within the healthy control subject, results demonstrated no significant differences in mean completion time, moving velocity, or acceleration between mild to moderate gravity-support levels during all phases of the cycle. The stroke subject results revealed a significant decrease in the cycle mean completion time (p= 0.042) between the two gravity-support levels, specifically in mean completion time of the grasping phase. A significant increase was found in the initiation phase moving velocity (p=0.039) and a significant decrease was found in the grasping phase (p=0.048) between two gravity-support levels in the stroke subject. Between subjects, significant increase in the cycle mean completion time was found under both mild and moderate conditions (p\u3c.001 for both conditions). Additionally, significant decreases in the moving velocities were found in all phases of the cycle between the healthy control and the stroke subject under both conditions. With increasing weight support, the healthy control subject showed an increase in abduction and flexion degrees at the glenohumeral joint level, and an increase in flexion degrees of the elbow joint. On the other hand, the stroke subject showed a decrease in abduction degrees and an increase in flexion degrees at the glenohumeral joint level, and a decrease in flexion degrees of the elbow joint after increasing the weight-support level. Results demonstrated an increase in the mean of vertical forces when changing gravity-support levels from mild to moderate during all phases of the cycle in both stroke and healthy subjects. Last, the average EMG magnitude during the reaching cycle phases was reduced for muscles acting against gravity (anterior deltoid, middle deltoid, biceps, and brachioradialis) in both the healthy control and the stroke subject. Conclusion: The significant differences in movement performance between mild and moderate physical weight support suggested a preliminary result that the gravity-supported mechanism provides a mean to facilitate functional upper limb motor performance in individuals with stroke. Future studies should examine such effects with larger sample sizes

    Design, implementation, control, and user evaluations of assiston-arm self-aligning upper-extremity exoskeleton

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    Physical rehabilitation therapy is indispensable for treating neurological disabilities. The use of robotic devices for rehabilitation holds high promise, since these devices can bear the physical burden of rehabilitation exercises during intense therapy sessions, while therapists are employed as decision makers. Robot-assisted rehabilitation devices are advantageous as they can be applied to patients with all levels of impairment, allow for easy tuning of the duration and intensity of therapies and enable customized, interactive treatment protocols. Moreover, since robotic devices are particularly good at repetitive tasks, rehabilitation robots can decrease the physical burden on therapists and enable a single therapist to supervise multiple patients simultaneously; hence, help to lower cost of therapies. While the intensity and quality of manually delivered therapies depend on the skill and fatigue level of therapists, high-intensity robotic therapies can always be delivered with high accuracy. Thanks to their integrated sensors, robotic devices can gather measurements throughout therapies, enable quantitative tracking of patient progress and development of evidence-based personalized rehabilitation programs. In this dissertation, we present the design, control, characterization and user evaluations of AssistOn-Arm, a powered, self-aligning exoskeleton for robotassisted upper-extremity rehabilitation. AssistOn-Arm is designed as a passive back-driveable impedance-type robot such that patients/therapists can move the device transparently, without much interference of the device dynamics on natural movements. Thanks to its novel kinematics and mechanically transparent design, AssistOn-Arm can passively self-align its joint axes to provide an ideal match between human joint axes and the exoskeleton axes, guaranteeing ergonomic movements and comfort throughout physical therapies. The self-aligning property of AssistOn-Arm not only increases the usable range of motion for robot-assisted upper-extremity exercises to cover almost the whole human arm workspace, but also enables the delivery of glenohumeral mobilization (scapular elevation/depression and protraction/retraction) and scapular stabilization exercises, extending the type of therapies that can be administered using upper-extremity exoskeletons. Furthermore, the self-alignment property of AssistOn-Arm signi cantly shortens the setup time required to attach a patient to the exoskeleton. As an impedance-type device with high passive back-driveability, AssistOn- Arm can be force controlled without the need of force sensors; hence, high delity interaction control performance can be achieved with open-loop impedance control. This control architecture not only simpli es implementation, but also enhances safety (coupled stability robustness), since open-loop force control does not su er from the fundamental bandwidth and stability limitations of force-feedback. Experimental characterizations and user studies with healthy volunteers con- rm the transparency, range of motion, and control performance of AssistOn- Ar

    Computational neurorehabilitation: modeling plasticity and learning to predict recovery

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    Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

    Rehabilitation of Stroke Patients with Sensor-based Systems

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    Robotic exoskeletons: A perspective for the rehabilitation of arm coordination in stroke patients

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    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

    Upper limb proprioceptive sensitivity in three-dimensional space: effects of direction, posture, and exogenous neuromodulation

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    abstract: Proprioception is the sense of body position, movement, force, and effort. Loss of proprioception can affect planning and control of limb and body movements, negatively impacting activities of daily living and quality of life. Assessments employing planar robots have shown that proprioceptive sensitivity is directionally dependent within the horizontal plane however, few studies have looked at proprioceptive sensitivity in 3d space. In addition, the extent to which proprioceptive sensitivity is modifiable by factors such as exogenous neuromodulation is unclear. To investigate proprioceptive sensitivity in 3d we developed a novel experimental paradigm employing a 7-DoF robot arm, which enables reliable testing of arm proprioception along arbitrary paths in 3d space, including vertical motion which has previously been neglected. A participant’s right arm was coupled to a trough held by the robot that stabilized the wrist and forearm, allowing for changes in configuration only at the elbow and shoulder. Sensitivity to imposed displacements of the endpoint of the arm were evaluated using a “same/different” task, where participant’s hands were moved 1-4 cm from a previously visited reference position. A measure of sensitivity (d’) was compared across 6 movement directions and between 2 postures. For all directions, sensitivity increased monotonically as the distance from the reference location increased. Sensitivity was also shown to be anisotropic (directionally dependent) which has implications for our understanding of the planning and control of reaching movements in 3d space. The effect of neuromodulation on proprioceptive sensitivity was assessed using transcutaneous electrical nerve stimulation (TENS), which has been shown to have beneficial effects on human cognitive and sensorimotor performance in other contexts. In this pilot study the effects of two frequencies (30hz and 300hz) and three electrode configurations were examined. No effect of electrode configuration was found, however sensitivity with 30hz stimulation was significantly lower than with 300hz stimulation (which was similar to sensitivity without stimulation). Although TENS was shown to modulate proprioceptive sensitivity, additional experiments are required to determine if TENS can produce enhancement rather than depression of sensitivity which would have positive implications for rehabilitation of proprioceptive deficits arising from stroke and other disorders.Dissertation/ThesisDoctoral Dissertation Neuroscience 201

    Dynamics of neurological and behavioural recovery after stroke

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