Robotic Controllers for Motor Learning After Neurological Injuries (HRCEML Trial)
Palo Alto (17 mi)Age: 18+
Sex: Any
Travel: May be covered
Time Reimbursement: Varies
Trial Phase: N/A
Recruiting
Sponsor: Shirley Ryan AbilityLab
No Placebo Group
Trial Summary
What is the purpose of this trial?The purpose of this study is to develop a new paradigm to understand how humans physically interact with each other at a single and at multiple joints, with multiple contact points, so as to synthesize robot controllers that can exhibit human-like behavior when interacting with humans (e.g., exoskeleton) or other co-robots. The investigators will develop models for a single joint robot (i.e. at the ankle joint) that can vary its haptic behavioral interactions at variable impedances, and replicate in a multi-joint robot (i.e. at the ankle, knee, and hip joints). The investigators will collect data from healthy participants and clinical populations to create a controller based on our models to implement in the robots. Then, the investigators will test our models via the robots to investigate the mechanisms underlying enhanced motor learning during different human-human haptic interaction behaviors (i.e. collaboration, competition, and cooperation. This study will be carried out in healthy participants, participants post-stroke, and participants with spinal cord injury (SCI).
Is the treatment in the trial 'Robotic Controllers for Motor Learning After Neurological Injuries' promising?Yes, the treatment is promising because robotic controllers can help people with neurological injuries relearn movements by providing repetitive and focused practice. These robots can adjust to the patient's progress, making the therapy more effective. They also encourage patients to actively participate in their recovery, which can lead to better results.12469
What safety data exists for robotic controllers in motor learning after neurological injuries?The research indicates that robotic controllers, including exoskeletons, have been part of clinical practice for over a decade. They are generally considered safe and feasible for use in rehabilitation, as evidenced by studies like the one involving the Tele-REINVENT system, which reported safe and enjoyable use over 10 weeks. However, the efficacy compared to conventional therapy is still debated, and more research is needed to establish standardized safety protocols and control strategies.458910
Do I need to stop taking my current medications to join the trial?The trial protocol does not specify if you need to stop taking your current medications. However, it does mention that participants should not have concurrent medical treatments or surgeries. It's best to discuss your specific medications with the trial coordinators.
What data supports the idea that Robotic Controllers for Motor Learning After Neurological Injuries is an effective treatment?The available research shows that robotic controllers can be effective for motor learning after neurological injuries. For example, wearable powered exoskeletons help stroke survivors relearn how to walk by providing intensive training with active participation. Another study found that robotic arm training and traditional therapy resulted in similar improvements in movement ability, suggesting both methods are effective. However, some research indicates that robotic therapy may not offer significant benefits over traditional physiotherapy when the same amount of practice is given. Overall, robotic controllers can be a useful tool in rehabilitation, but their effectiveness may depend on how they are used and combined with other therapies.34679
Eligibility Criteria
This trial is for individuals aged 18-80 with normal hearing and vision, who can understand English and give informed consent. It's suitable for healthy participants as well as those post-stroke or with spinal cord injury (SCI), provided they can walk over 10m independently. People with brain lesions, neurological disorders, abnormal limb movements, or outside the height range of 3'6" to 6'2" cannot join.Inclusion Criteria
I have never had a brain or skull lesion.
My hearing and vision are normal or corrected to normal.
I am between 18 and 80 years old.
I don't have conditions that cause abnormal limb movements.
I do not have any neurological disorders.
Treatment Details
The study aims to develop robot controllers that mimic human behavior in physical interactions. These will be tested on single joint (ankle) and multi-joint (ankle, knee, hip) robots using variable haptic behaviors like collaboration and competition to enhance motor learning in humans.
4Treatment groups
Experimental Treatment
Group I: Healthy Participants Bilateral Lower Limb Exoskeleton (H3/X2)Experimental Treatment3 Interventions
The investigators will look at how the task performance and motor performance of individuals in dyadic physical interactions are affected.
Group II: Healthy Participants Ankle Robot (M1)Experimental Treatment3 Interventions
The investigators will look at how the task performance and motor performance of individuals in dyadic physical interactions are affected.
Group III: Clinical Populations Bilateral Lower Limb Exoskeleton (H3/X2)Experimental Treatment4 Interventions
The investigators will look at how the task performance and motor performance of individuals in dyadic physical interactions are affected.
Group IV: Clinical Populations Ankle Robot (M1)Experimental Treatment4 Interventions
The investigators will look at how the task performance and motor performance of individuals in dyadic physical interactions are affected.
Find a clinic near you
Research locations nearbySelect from list below to view details:
Shirley Ryan AbilityLabChicago, IL
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Who is running the clinical trial?
Shirley Ryan AbilityLabLead Sponsor
U.S. National Science FoundationCollaborator
References
Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. [2022]Based on evidence from recent experiments in motor learning and neurorehabilitation, we hypothesize that three desirable features for a controller for robot-aided movement training following stroke are high mechanical compliance, the ability to assist patients in completing desired movements, and the ability to provide only the minimum assistance necessary. This paper presents a novel controller that successfully exhibits these characteristics. The controller uses a standard model-based, adaptive control approach in order to learn the patient's abilities and assist in completing movements while remaining compliant. Assistance-as-needed is achieved by adding a novel force reducing term to the adaptive control law, which decays the force output from the robot when errors in task execution are small. Several tests are presented using the upper extremity robotic therapy device named Pneu-WREX to evaluate the performance of the adaptive, "assist-as-needed" controller with people who have suffered a stroke. The results of these experiments illustrate the "slacking" behavior of human motor control: given the opportunity, the human patient will reduce his or her output, letting the robotic device do the work for it. The experiments also demonstrate how including the "assist-as-needed" modification in the controller increases participation from the motor system.
Functional reorganization of upper-body movements for wheelchair control. [2020]In general, survivors of neuromotor disorders and injuries need to reorganize their body movements in order to achieve goals that used to be easy and natural. Often, disabled people are offered the option to control assistive devices that will facilitate the recovery of independence and capability in their daily lives. The knowledge acquired during the last few years in the motor control field can be used to study and enhance this learning process. Furthermore, this knowledge may aid in finding methods for optimizing the use of residual voluntary muscular control in disabled users and searching for an easily learnable map between body motor space and devices control space. To investigate movement reorganization we asked healthy subjects to control a cursor performing a reaching task using shoulders and upper arm movements. These movements were mapped to a lower dimensional space by principal components analysis and were used to control the cursor. We found that all subjects were able to learn to control the cursor with ease and precision while reducing the proportion of ineffective body movement components in favor of the components that mapped directly into the control space. Moreover, with practice the movements of the controlled device - the cursor - became faster, smother, more precise and with a nearly symmetric speed profile.
Do robotic and non-robotic arm movement training drive motor recovery after stroke by a common neural mechanism? Experimental evidence and a computational model. [2020]Different dose-matched, upper extremity rehabilitation training techniques, including robotic and non-robotic techniques, can result in similar improvement in movement ability after stroke, suggesting they may elicit a common drive for recovery. Here we report experimental results that support the hypothesis of a common drive, and develop a computational model of a putative neural mechanism for the common drive. We compared weekly motor control recovery during robotic and unassisted movement training techniques after chronic stroke (n = 27), as assessed with quantitative measures of strength, speed, and coordination. The results showed that recovery in both groups followed an exponential time course with a time constant of about 4-5 weeks. Despite the greater range and speed of movement practiced by the robot group, motor recovery was very similar between the groups. The premise of the computational model is that improvements in motor control are caused by improvements in the ability to activate spared portions of the damaged corticospinal system, as learned by a biologically plausible search algorithm. Robot-assisted and unassisted training would in theory equally drive this search process.
Robot therapy of the upper limb in stroke patients: preliminary experiences for the principle-based use of this technology. [2016]Robotic systems for neuromotor rehabilitation have been a part of clinical practice for more than a decade but the efficacy of this new technology is still debated. One reason for this, in our opinion, is that there is still no consensus on the most important features of these systems, or on the underlying theoretical basis, essential for the rational design of treatment protocols. The aim of this paper, born of our long experience in the study of the neural control of movement and the use of robots for characterizing motor control mechanisms, is to make a small contribution to clarifying this issue. What is needed in the future is a "research pipeline" encompassing experimentally validated models of neural control of movement, models of motor learning, models of functional recovery, and finally principle-based robot therapy control strategies. We believe this is a necessary prerequisite for carrying out well formulated comparisons of different control strategies as well as mixed strategies of robot/human treatment, in the framework of randomised, controlled clinical trials.
Rehabilitation exoskeletal robotics. The promise of an emerging field. [2010]Exoskeletons are wearable robots exhibiting a close cognitive and physical interaction with the human user. These are rigid robotic exoskeletal structures that typically operate alongside human limbs. Scientific and technological work on exoskeletons began in the early 1960s but have only recently been applied to rehabilitation and functional substitution in patients suffering from motor disorders. Key topics for further development of exoskeletons in rehabilitation scenarios include the need for robust human-robot multimodal cognitive interaction, safe and dependable physical interaction, true wearability and portability, and user aspects such as acceptance and usability. This discussion provides an overview of these aspects and draws conclusions regarding potential future research directions in robotic exoskeletons.
Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation. [2020]While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input. We tested the feasibility of three-dimensional robotic assistance for reaching movements with a multi-joint exoskeleton during motor imagery (MI)-related desynchronization of sensorimotor oscillations in the β-band. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function. Healthy subjects and stroke survivors showed similar patterns-but different aptitudes-of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e., when the orthosis was actually attached to the subject's arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task. Brain-robot interfaces (BRIs) may successfully link three-dimensional robotic training to the participants' efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration, a proposal that warrants further investigation in a larger cohort of stroke patients.
Overground wearable powered exoskeleton for gait training in subacute stroke subjects: clinical and gait assessments. [2020]Wearable powered exoskeletons provide intensive overground gait training with patient's active participation: these features promote a successful active motor relearning of ambulation in stroke survivors.
Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke. [2021]Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.
Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons. [2021]Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients' status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) "high-level" training modalities, 2) "low-level" control strategies, and 3) "hardware-level" implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.
Neurorehabilitation robotics: how much control should therapists have? [2023]Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.