Neurologic Disorder, Stroke
Conditions
Keywords
Neurorehabilitation, Robotic Rehabilitation, Motor Learning
Brief summary
The objective of this study is to develop and evaluate novel robotic training strategies that modulate errors based on the subjects' individual motor and cognitive needs. For this purpose, healthy adults and neurologic patients will participate in robotic motor learning experiments. Patients have a diagnosis of a neurological disease (i.e., stroke, spinal cord injury, multiple sclerosis, Guillain-Barré syndrome) limiting arm motor function.
Detailed description
Neurological patients (e.g., after stroke) engage in intensive and expensive neurorehabilitation therapy to regain part of their former motor functional ability to perform everyday activities with often limited and unsatisfactory outcome. Robots became a promising supplement or even alternative for neurorehabilitation therapy, providing cost-effective, high repetition and task-oriented training. However, results of an initial body of work comparing the effectiveness of robotic training strategies are highly inconclusive. A possible explanation is that most current robotic systems cover only one neurorehabilitation strategy (e.g. reducing or augmenting movement errors) and may thus insufficiently address the subjects' individual needs and the characteristics of the task to be learned. In this study, Investigators will perform several motor learning experiments with healthy adult and neurological patients in order to evaluate the relative motor and cognitive benefits of newly developed robotic training strategies that modulate errors based on the subject's age, skill level and tasks characteristics. The effects of the new strategies will be compared to classical robotic assistance, and to non-robotic feedback approaches, such as visual feedback. The culmination of this work may help to optimize training benefits of already existing rehabilitation robots.
Interventions
BEHAVIORALRobotic motor training
The experiments consist in performing motor tasks with upper-limb robotic devices.
Sponsors
University of Bern
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
BASIC_SCIENCE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Aged ≥18 years * Informed Consent as documented by signature (Informed Consent form) * Bodyweight \<120 kg * Ability to communicate effectively with the examiner so that the validity of the patient's data could not be compromised
Exclusion criteria
* Excessive spasticity of the affected arm (Ashworth Scale ≥3) * Serious medical or psychiatric disorder * Orthopaedic, rheumatological, or other disease restricting movements of the paretic arm * Shoulder subluxation * Skin ulcerations at the paretic arm * Cyber-sickness (i.e., nausea when looking at a screen or playing computer games) * Serious cognitive defects or aphasia preventing effective use of the robotic devices * Severe visual and auditory impairments
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in kinematic performance assessed by the robot | Baseline, training (immediately after baseline), retention (1-2 days after the training) | Motion changes from baseline in the kinematic variables assessed by the robot and motion trackers during the motor learning task. The kinematic performance analysis consists of end-effector position in the x, y, and z-axis, in meters, and joint angles in degrees. |
| Change in kinetic performance assessed by the robot | Baseline, training (immediately after baseline), retention (1-2 days after the training) | Force changes from baseline in the kinetic variables assessed by the robot using force sensors during the motor learning task. Kinetic performance analysis consists of interaction forces in x, y, and z-axis, in N and applied robot joint torques by the motors, in Nm. |
| Spatial analysis of changes in evoked potentials as assessed by Electroencephalography (EEG) measurement | Baseline, training (immediately after baseline), retention (1-2 days after the training) | Electroencephalographical assessment of changes in evoked potentials i.e. the electrical activity of the brain in response to stimulation of specific sensory nerve pathways. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in Cognitive Load as assessed by National Aeronautics and Space Administration (NASA) (Raw) Task Load Index | Immediately after the end of intervention, At the end of the session | Self-reported cognitive load during a task, Self-administered National Aeronautics and Space Administration (Raw) Task Load Index (TLX), analog scale mapped from 0 to 100 (Endpoints: Low/High, Good/Poor) |
| Change in embodiment | Before Intervention, Immediately after the end of intervention | Virtual Reality (VR) Embodiment Scale, Self administered Likert scale of 1-7 (Strongly Disagree to Strongly Agree) |
| System Usability as assessed by System Usability Scale (SUS) | Immediately after the end of intervention, At the end of the session | Self reported system usability assessed by System Usability Scale (SUS) Likert scale of 1-5 (Strongly agree to Strongly disagree) |
| Spatial analysis of changes in Task-Based Brain Connectivity as assessed by Electroencephalography (EEG) measurement | Baseline, training (immediately after baseline or 1-2 days after baseline), retention (1-2 days after the training) | Changes in Task-Based Brain Connectivity from baseline in electroencephalography measurement |
| Change in Motivation as assessed by Intrinsic Motivation Inventory (IMI) | Before Intervention, Immediately after the end of intervention, at the end of the session | Intrinsic Motivation Inventory, Self administered. Likert scale of 1-7 (1: not at all true - 4: somewhat true - 7: very true) |
Countries
Switzerland
Outcome results
None listed