Review of control strategies for robotic movement training after neurologic injury

Robotic mirror therapy for stroke rehabilitation through virtual activities of daily living

Mirror therapy is a standard technique of rehabilitation for recovering motor and vision abilities of stroke patients, especially in the case of asymmetric limb function. To enhance traditional mirror therapy, robotic mirror therapy (RMT) has been proposed over the past decade, allowing for assisted bimanual coordination of paretic (affected) and contralateral (healthy) limbs. However, state-of-the-art RMT platforms predominantly target mirrored motions of trajectories, largely limited to 2-D motions. In this paper, an RMT platform is proposed, which can facilitate the patient to practice virtual activities of daily living (ADL) and thus enhance their independence. Two similar (but mirrored) 3D virtual environments are created in which the patients operate robots with both their limbs to complete ADL (such as writing and eating) with the assistance of the therapist. The recovery level of the patient is continuously assessed by monitoring their ability to track assigned trajectories. The patient's robots are programmed to assist the patient in following these trajectories based on this recovery level. In this paper, the framework to dynamically monitor recovery level and accordingly provide assistance is developed along with the nonlinear controller design to ensure position tracking, force control, and stability. Proof-of-concept studies are conducted with both 3D trajectory tracking and ADL. The results demonstrate the potential use of the proposed system to enhance the recovery of the patients.

Assessing the Effect of Cervical Transcutaneous Spinal Stimulation With an Upper Limb Robotic Exoskeleton and Surface Electromyography

Transcutaneous spinal stimulation (TSS) is a promising rehabilitative intervention to restore motor function and coordination for individuals with spinal cord injury (SCI). The effects of TSS are most commonly assessed by evaluating muscle response to stimulation using surface electromyography (sEMG). Given the increasing use of robotic devices to deliver therapy and the emerging potential of hybrid rehabilitation interventions that combine neuromodulation with robotic devices, there is an opportunity to leverage the on-board sensors of the robots to measure kinematic and torque changes of joints in the presence of stimulation. This paper explores the potential for robotic assessment of the effects of TSS delivered to the cervical spinal cord. We used a four degree-of-freedom exoskeleton to measure the torque response of upper limb (UL) joints during stimulation, while simultaneously recording sEMG. We analyzed joint torque and electromyography data generated during TSS delivered over individual sites of the cervical spinal cord in neurologically intact participants. We show that site-specific effects of TSS are manifested not only by modulation of the amplitude of spinally evoked motor potentials in UL muscles, but also by changes in torque generated by individual UL joints. We observed preferential resultant action of proximal muscles and joints with stimulation at the rostral site, and of proximal joints with rostral-lateral stimulation. Robotic assessment can be used to measure the effects of TSS, and could be integrated into complex control algorithms that govern the behavior of hybrid neuromodulation-robotic systems.

I-BaR: integrated balance rehabilitation framework

Neurological diseases are observed in approximately 1 billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of assist-as-needed paradigm and, with integrated rehabilitation process as whole, that is, ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of virtual reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback.

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.

Preliminary Virtual Constraint-Based Control Evaluation on a Pediatric Lower-Limb Exoskeleton

Pediatric gait rehabilitation and guidance strategies using robotic exoskeletons require a controller that encourages user volitional control and participation while guiding the wearer towards a stable gait cycle. Virtual constraint-based controllers have created stable gait cycles in bipedal robotic systems and have seen recent use in assistive exoskeletons. This paper evaluates a virtual constraint-based controller for pediatric gait guidance through comparison with a traditional time-dependent position tracking controller on a newly developed exoskeleton system. Walking experiments were performed with a healthy child subject wearing the exoskeleton under proportional-derivative control, virtual constraint-based control, and while unpowered. The participant questionnaires assessed the perceived exertion and controller usability measures, while sensors provided kinematic, control torque, and muscle activation data. The virtual constraint-based controller resulted in a gait similar to the proportional-derivative controlled gait but reduced the variability in the gait kinematics by 36.72% and 16.28% relative to unassisted gait in the hips and knees, respectively. The virtual constraint-based controller also used 35.89% and 4.44% less rms torque per gait cycle in the hips and knees, respectively. The user feedback indicated that the virtual constraint-based controller was intuitive and easy to utilize relative to the proportional-derivative controller. These results indicate that virtual constraint-based control has favorable characteristics for robot-assisted gait guidance.

Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation-A Systematic Review

This review aims to provide a systematic analysis of the literature focused on the use of intelligent control systems in robotics for physical rehabilitation, identifying trends in recent research and comparing the effectiveness of intelligence used in control, with the aim of determining important factors in robot-assisted rehabilitation and how intelligent controller design can improve them. Seven electronic research databases were searched for articles published in the years 2015 - 2022 with articles selected based on relevance to the subject area of intelligent control systems in rehabilitation robotics. It was found that the most common use of intelligent algorithms for control is improving traditional control strategies with optimization and learning techniques. Intelligent algorithms are also commonly used in sensor output mapping, model construction, and for various data learning purposes. Experimental results show that intelligent controllers consistently outperform non-intelligent controllers in terms of transparency, tracking accuracy, and adaptability. Active participation of the patients and lowered interaction forces are consistently mentioned as important factors in improving the rehabilitation outcome as well as the patient experience. However, there are limited examples of studies presenting experimental results with impaired participants suffering limited range of motion, so the effectiveness of therapy provided by these systems is often difficult to quantify. A lack of universal evaluation criteria also makes it difficult to compare control systems outside of articles which use their own comparison criteria.

Human-centered design of a novel soft exosuit for post-stroke gait rehabilitation

BACKGROUND

Stroke remains a major cause of long-term adult disability in the United States, necessitating the need for effective rehabilitation strategies for post-stroke gait impairments. Despite advancements in post-stroke care, existing rehabilitation often falls short, prompting the development of devices like robots and exoskeletons. However, these technologies often lack crucial input from end-users, such as clinicians, patients, and caregivers, hindering their clinical utility. Employing a human-centered design approach can enhance the design process and address user-specific needs.

OBJECTIVE

To establish a proof-of-concept of the human-centered design approach by refining the NewGait® exosuit device for post-stroke gait rehabilitation.

METHODS

Using iterative design sprints, the research focused on understanding the perspectives of clinicians, stroke survivors, and caregivers. Two design sprints were conducted, including empathy interviews at the beginning of the design sprint to integrate end-users' insights. After each design sprint, the NewGait device underwent refinements based on emerging issues and recommendations. The final prototype underwent mechanical testing for durability, biomechanical simulation testing for clinical feasibility, and a system usability evaluation, where the new stroke-specific NewGait device was compared with the original NewGait device and a commercial product, Theratogs®.

RESULTS

Affinity mapping from the design sprints identified crucial categories for stakeholder adoption, including fit for females, ease of donning and doffing, and usability during barefoot walking. To address these issues, a system redesign was implemented within weeks, incorporating features like a loop-backed neoprene, a novel closure mechanism for the shoulder harness, and a hook-and-loop design for the waist belt. Additional improvements included reconstructing anchors with rigid hook materials and replacing latex elastic bands with non-latex silicone-based bands for enhanced durability. Further, changes to the dorsiflexion anchor were made to allow for barefoot walking. Mechanical testing revealed a remarkable 10-fold increase in durability, enduring 500,000 cycles without notable degradation. Biomechanical simulation established the modularity of the NewGait device and indicated that it could be configured to assist or resist different muscles during walking. Usability testing indicated superior performance of the stroke-specific NewGait device, scoring 84.3 on the system usability scale compared to 62.7 for the original NewGait device and 46.9 for Theratogs.

CONCLUSION

This study successfully establishes the proof-of-concept for a human-centered design approach using design sprints to rapidly develop a stroke-specific gait rehabilitation system. Future research should focus on evaluating the clinical efficacy and effectiveness of the NewGait device for post-stroke rehabilitation.

Myoelectric control and virtual reality to enhance motor rehabilitation after stroke

Effective upper-limb rehabilitation for severely impaired stroke survivors is still missing. Recent studies endorse novel motor rehabilitation approaches such as robotic exoskeletons and virtual reality systems to restore the function of the paretic limb of stroke survivors. However, the optimal way to promote the functional reorganization of the central nervous system after a stroke has yet to be uncovered. Electromyographic (EMG) signals have been employed for prosthetic control, but their application to rehabilitation has been limited. Here we propose a novel approach to promote the reorganization of pathological muscle activation patterns and enhance upper-limb motor recovery in stroke survivors by using an EMG-controlled interface to provide personalized assistance while performing movements in virtual reality (VR). We suggest that altering the visual feedback to improve motor performance in VR, thereby reducing the effect of deviations of the actual, dysfunctional muscle patterns from the functional ones, will actively engage patients in motor learning and facilitate the restoration of functional muscle patterns. An EMG-controlled VR interface may facilitate effective rehabilitation by targeting specific changes in the structure of muscle synergies and in their activations that emerged after a stroke-offering the possibility to provide rehabilitation therapies addressing specific individual impairments.

The rehabilitation robot: factors influencing its use, advantages and limitations in clinical rehabilitation

PURPOSE

Despite the proven effectiveness of rehabilitation robots (RR) in the literature, they are still little used in clinical rehabilitation. The aim of this study was to analyse the factors influencing the use of RR and the perception of therapists who used RR.

METHOD

In order to characterize the factors influencing the use of RR by therapists, a semi-structured interview was conducted with 18 therapists. These interviews are based on an interview guide inspired by the Unified Theory of Acceptance and Use of Technology model. The interviews were recorded and then transcribed, summarized and finally synthesized cross-sectionally. In addition and in parallel, the System Usability Scale (SUS) was also proposed to clinicians in order to collect quantitative data.

RESULTS

The interviews highlight the facilitators perceived by the therapists, such as the intensity of the movement, the complementarity with conventional rehabilitation. The results also showed the possible barriers perceived, these can be sometimes inconclusive (e.g., bugs). The SUS results show no effect, either on the gender of the users, their therapists, or the duration of use of the tool.

CONCLUSION

Better communication on the functionality of the robot and the construction of achievable goals would lead to more results that are conclusive but also better patient care. To date, and despite the evidence for the effectiveness of RRs, therapists believe that there are still many barriers to their use. They agree, however, that if changes are made, RRs will become an integral part of therapy.IMPLICATIONS FOR REHABILITATIONThe study idenfied and highlighted the factors influencing the use of the rehabilitation robot in the clinics through metric and ergonomic evaluations.The study allowed to quantify the level of acceptance of the Lokomat among therapists.This study allowed to identify negative factors that could be resolved through the implementation of a structured and generalized protocol for patients and thus improve their care.

The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task

During the learning of a new sensorimotor task, individuals are usually provided with instructional stimuli and relevant information about the target task. The inclusion of haptic devices in the study of this kind of learning has greatly helped in the understanding of how an individual can improve or acquire new skills. However, the way in which the information and stimuli are delivered has not been extensively explored. We have designed a challenging task with nonintuitive visuomotor perturbation that allows us to apply and compare different motor strategies to study the teaching process and to avoid the interference of previous knowledge present in the naïve subjects. Three subject groups participated in our experiment, where the learning by repetition without assistance, learning by repetition with assistance, and task Segmentation Learning techniques were performed with a haptic robot. Our results show that all the groups were able to successfully complete the task and that the subjects' performance during training and evaluation was not affected by modifying the teaching strategy. Nevertheless, our results indicate that the presented task design is useful for the study of sensorimotor teaching and that the presented metrics are suitable for exploring the evolution of the accuracy and precision during learning.

Improving Outcomes in People with Spinal Cord Injury: Encouraging Results from a Multidisciplinary Advanced Rehabilitation Pathway

Spinal cord injury (SCI) consists of damage to any segment of the spinal cord extending to potential harm to nerves in the cauda equina. Rehabilitative efforts for SCI can involve conventional physiotherapy, innovative technologies, as well as cognitive treatment and psychological support. The aim of this study is to evaluate the feasibility of a dedicated, multidisciplinary, and integrated intervention path for SCI, encompassing both conventional and technological interventions, while observing their impact on cognitive, motor, and behavioral outcomes and the overall quality of life for individuals with SCI. Forty-two patients with SCI were included in the analysis utilizing electronic recovery system data. The treatment regimen included multidisciplinary rehabilitation approaches, such as traditional physiotherapy sessions, speech therapy, psychological support, robotic devices, advanced cognitive rehabilitation, and other interventions. Pre-post comparisons showed a significant improvement in lower limb function (Fugl Meyer Assessment-FMA < 0.001), global cognitive functioning (Montreal Cognitive Assessment-MoCA p < 0.001), and perceived quality of life at both a physical and mental level (Short Form-12-SF-12 p < 0.001). Furthermore, we found a significant reduction in depressive state (Beck Depression Inventory-BDI p < 0.001). In addition, we assessed patient satisfaction using the Short Form of the Patient Satisfaction Questionnaire (PSQ), offering insights into the subjective evaluation of the intervention. In conclusion, this retrospective study provides positive results in terms of improvements in motor function, cognitive functions, and quality of life, highlighting the importance of exploring multidisciplinary approaches.

Effects of high-intensity gait training with and without soft robotic exosuits in people post-stroke: a development-of-concept pilot crossover trial

INTRODUCTION

High-intensity gait training is widely recognized as an effective rehabilitation approach after stroke. Soft robotic exosuits that enhance post-stroke gait mechanics have the potential to improve the rehabilitative outcomes achieved by high-intensity gait training. The objective of this development-of-concept pilot crossover study was to evaluate the outcomes achieved by high-intensity gait training with versus without soft robotic exosuits.

METHODS

In this 2-arm pilot crossover study, four individuals post-stroke completed twelve visits of speed-based, high-intensity gait training: six consecutive visits of Robotic Exosuit Augmented Locomotion (REAL) gait training and six consecutive visits without the exosuit (CONTROL). The intervention arms were counterbalanced across study participants and separated by 6 + weeks of washout. Walking function was evaluated before and after each intervention using 6-minute walk test (6MWT) distance and 10-m walk test (10mWT) speed. Moreover, 10mWT speeds were evaluated before each training visit, with the time-course of change in walking speed computed for each intervention arm. For each participant, changes in each outcome were compared to minimal clinically-important difference (MCID) thresholds. Secondary analyses focused on changes in propulsion mechanics and associated biomechanical metrics.

RESULTS

Large between-group effects were observed for 6MWT distance (d = 1.41) and 10mWT speed (d = 1.14). REAL gait training resulted in an average pre-post change of 68 ± 27 m (p = 0.015) in 6MWT distance, compared to a pre-post change of 30 ± 16 m (p = 0.035) after CONTROL gait training. Similarly, REAL training resulted in a pre-post change of 0.08 ± 0.03 m/s (p = 0.012) in 10mWT speed, compared to a pre-post change of 0.01 ± 06 m/s (p = 0.76) after CONTROL. For both outcomes, 3 of 4 (75%) study participants surpassed MCIDs after REAL training, whereas 1 of 4 (25%) surpassed MCIDs after CONTROL training. Across the training visits, REAL training resulted in a 1.67 faster rate of improvement in walking speed. Similar patterns of improvement were observed for the secondary gait biomechanical outcomes, with REAL training resulting in significantly improved paretic propulsion for 3 of 4 study participants (p < 0.05) compared to 1 of 4 after CONTROL.

CONCLUSION

Soft robotic exosuits have the potential to enhance the rehabilitative outcomes produced by high-intensity gait training after stroke. Findings of this development-of-concept pilot crossover trial motivate continued development and study of the REAL gait training program.

Gait training using a wearable robotic hip device for incomplete spinal cord injury: A preliminary study

CONTEXT/OBJECTIVE

To explore changes in gait functions for patients with chronic spinal cord injury (SCI) before and after standard rehabilitation and rehabilitation with a wearable hip device, explore the utility of robot-assisted gait training (RAGT), and evaluate the safety and dose of RAGT.

DESIGN

Single-arm, open-label, observational study.

SETTING

A rehabilitation hospital.

PARTICIPANTS

Twelve patients with SCI.

INTERVENTIONS

Standard rehabilitation after admission in the first phase. RAGT for two weeks in the second phase.

OUTCOME MEASURES

Self-selected walking speed (SWS), step length, cadence, and the 6-minute walking distance were the primary outcomes. Walking Index for SCI score, lower extremity motor score, and spasticity were measured. Walking abilities were compared between the two periods using a generalized linear mixed model (GLMM). Correlations between assessments and changes in walking abilities during each period were analyzed.

RESULTS

After standard rehabilitation for 66.1 ± 36.9 days, a period of 17.6 ± 3.3 days of RAGT was safely performed. SWS increased during both periods. GLMM showed that the increase in cadence was influenced by standard rehabilitation, whereas the limited step length increase was influenced by RAGT. During RAGT, the increase in step length was related to an increase in hip flexor function.

CONCLUSIONS

Gait speed in patients with SCI increased after rehabilitation, including RAGT, in the short-term. This increase was associated with improved muscle function in hip flexion at the start of RAGT.Trial Registration: This study was registered with the UMIN Clinical Trials Registry (UMIN-CTR; UMIN000042025).

Monitoring Active Patient Participation During Robotic Rehabilitation: Comparison Between a Robot-Based Metric and an EMG-Based Metric

While rehabilitation robots present a much-needed solution to improving early mobilization therapy in demanding clinical settings, they also present new challenges and opportunities in patient monitoring. Aside from the fundamental challenge of quantifying a patient's voluntary contribution during robot-led therapy motion, many sensors cannot be used in clinical settings due to time and space limitations. In this paper, we present and compare two metrics for monitoring a patient's active participation in the motion. The two metrics, each derived from first principles, have the same biomechanical interpretability, i.e., active work by the patient during the robotic mobilization therapy, but are calculated in two different spaces (Cartesian vs. muscle space). Furthermore, the sensors used to quantify these two metrics are fully independent from each other and the associated measurements are unrelated. Specifically, the robot-based work metric utilizes robot-integrated force sensors, while the EMG-based work metric requires electrophysiological sensors. We then apply the two metrics to therapy performed using a clinically certified, commercially available robotic system and compare them against the specific instructions given to the healthy subjects as well as against each other. Both metric outputs qualitatively match the expected behavior of the healthy subjects. Additionally, strong correlations (median [Formula: see text]) are shown between the two metrics, not only for healthy subjects (n = 12) but also for patients (n = 2), providing solid evidence for their validity and translatability. Importantly, the robot-based work metric does not rely on any sensors outside of those integrated into the robot, thus making it ideal for application in clinical settings.

Advanced rehabilitation in ischaemic stroke research

At present, due to the rapid progress of treatment technology in the acute phase of ischaemic stroke, the mortality of patients has been greatly reduced but the number of disabled survivors is increasing, and most of them are elderly patients. Physicians and rehabilitation therapists pay attention to develop all kinds of therapist techniques including physical therapy techniques, robot-assisted technology and artificial intelligence technology, and study the molecular, cellular or synergistic mechanisms of rehabilitation therapies to promote the effect of rehabilitation therapy. Here, we discussed different animal and in vitro models of ischaemic stroke for rehabilitation studies; the compound concept and technology of neurological rehabilitation; all kinds of biological mechanisms of physical therapy; the significance, assessment and efficacy of neurological rehabilitation; the application of brain-computer interface, rehabilitation robotic and non-invasive brain stimulation technology in stroke rehabilitation.

A rehabilitation robot control framework with adaptation of training tasks and robotic assistance

Robot-assisted rehabilitation has exhibited great potential to enhance the motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic motion can be regulated through physical human-robot interaction for comfortable support and appropriate assistance level. However, it is hardly possible, especially for patients with severe motor disabilities, to continuously exert force to guide the robot to complete the prescribed training task. Conversely, reduced task difficulty cannot facilitate stimulating patients' potential movement capabilities. Moreover, challenging more difficult tasks with minimal robotic assistance is usually ignored when subjects show improved performance. In this paper, a control framework is proposed to simultaneously adjust both the training task and robotic assistance according to the subjects' performance, which can be estimated from the users' electromyography signals. Concretely, a trajectory deformation algorithm is developed to generate smooth and compliant task motion while responding to pHRI. An assist-as-needed (ANN) controller along with a feedback gain modification algorithm is designed to promote patients' active participation according to individual performance variance on completing the training task. The proposed control framework is validated using a lower extremity rehabilitation robot through experiments. The experimental results demonstrate that the control scheme can optimize the robotic assistance to complete the subject-adaptation training task with high efficiency.

Autonomy support encourages use of more-affected arm post-stroke

BACKGROUND

Autonomy support, which involves providing individuals the ability to control their own behavior, is associated with improved motor control and learning in various populations in clinical and non-clinical settings. This study aimed to investigate whether autonomy support combined with an information technology (IT) device facilitated success in using the more-affected arm during training in individuals with stroke. Consequently, we examined whether increased success influenced the use of the more-affected arm in mild to moderate subacute to chronic stroke survivors.

METHODS

Twenty-six participants with stroke were assigned to the autonomy support or control groups. Over a 5-week period, training and test sessions were conducted using the Individualized Motivation Enhancement System (IMES), a device developed specifically for this study. In the autonomy support group, participants were able to adjust the task difficulty parameter, which controlled the time limit for reaching targets. The control group did not receive this option. The evaluation of the more-affected arm's use, performance, and impairment was conducted through clinical tests and the IMES. These data were then analyzed using mixed-effect models.

RESULTS

In the IMES test, both groups showed a significant improvement in performance (p < 0.0001) after the training period, without any significant intergroup differences (p > 0.05). However only the autonomy support group demonstrated a significant increase in the use of the more-affected arm following the training (p < 0.001). Additionally, during the training period, the autonomy support group showed a significant increase in successful experiences with using the more-affected arm (p < 0.0001), while the control group did not exhibit the same level of improvement (p > 0.05). Also, in the autonomy support group, the increase in the use of the more-affected arm was associated with the increase in the successful experience significantly (p = 0.007).

CONCLUSIONS

Combining autonomy support with an IT device is a practical approach for enhancing performance and promoting the use of the more-affected upper extremity post-stroke. Autonomy support facilitates the successful use of the more-affected arm, thereby increasing awareness of the training goal of maximizing its use.

TRIAL REGISTRATION

The study was registered retrospectively with the Clinical Research Information Service (KCT0008117; January 13, 2023; https://cris.nih.go.kr/cris/search/detailSearch.do/23875 ).

Model-Based Shared Control of a Hybrid FES-Exoskeleton: An Application in Participant-Specific Robotic Rehabilitation

Hybrid exoskeleton, comprising an exoskeleton interfaced with functional electrical stimulation (FES) technique, is conceptualized to complement the weakness of each other in automated neuro-rehabilitation of sensory-motor deficits. The externally actuating exoskeleton cannot directly influence neurophysiology of the patients, while FES is difficult to use in functional or goal-oriented tasks. The latter challenge is largely inherited from the fact that the dynamics of the muscular response to FES is complex, and it is highly user- and state-dependent. Due to the retardation of the muscular contraction response to the FES profile, furthermore, a commonly used model-free control scheme, such as PID control, suffers performance. The challenge in FES control is exacerbated especially in the presence of the actuation redundancy between the volitional activity of the user, powered exoskeleton, and FES-induced muscle contractions. This study therefore presents trajectory tracking performance of the hybrid exoskeleton in a novel model-based hybrid exoskeleton scheme which entices user-specific FES model-predictive control.

Automated Patient-Robot Task Assignment in a Simulated Stochastic Rehabilitation Gym

Rehabilitation after neurological injury can be provided by robots that help patients perform different exercises. Multiple such robots can be combined in a rehabilitation robot gym to allow multiple patients to perform a diverse range of exercises simultaneously. In pursuit of better multipatient supervision, we aim to develop an automated assignment system that assigns patients to different robots during a training session to maximize their skill development. Our previous work was designed for simplified simulated environments where each patient's skill development is known beforehand. The current work improves upon that work by changing the deterministic environment into a stochastic environment where part of the skill development is random and the assignment system must estimate each patient's predicted skill development using a neural network based on the patient's previous training success rate with that robot. These skill development estimates are used to create patient-robot assignments on a timestep-by-timestep basis to maximize the skill development of the patient group. Results from simplified simulation trials show that the schedules produced by our assignment system outperform multiple baseline schedules (e.g., schedules where patients never switch robots and schedules where patients only switch robots once halfway through the session). Additionally, we discuss how some of our simplifications could be addressed in the future.

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Introduction

Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface.

Methods

To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model.

Results

Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed.

Discussion

The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

Applying an Artificial Neuromolecular System to the Application of Robotic Arm Motion Control in Assisting the Rehabilitation of Stroke Patients-An Artificial World Approach

Stroke patients cannot use their hands as freely as usual. However, recovery after a stroke is a long road for many patients. If artificial intelligence can assist human arm movement, it is believed that the possibility of stroke patients returning to normal hand movement can be significantly increased. In this study, the artificial neuromolecular system (ANM system) developed by our laboratory is used as the core motion control system to learn to control the mechanical arm, produce similar human rehabilitation actions, and assist patients in transiting between different activities. The strength of the ANM system lies in its ability to capture and process spatiotemporal information by exploiting the dynamic information processing inside neurons. Five experiments are conducted in this research: continuous learning, dimensionality reduction, moving problem domains, transfer learning, and fault tolerance. The results show that the ANM system can find out the arm movement trajectory when people perform different rehabilitation actions through the ability of continuous learning and reduce the activation of multiple muscle groups in stroke patients through the learning method of reducing dimensions. Finally, using the ANM system can reduce the learning time and performance required to switch between different actions through transfer learning.

Home-based upper limb stroke rehabilitation mechatronics: challenges and opportunities

Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivors post-discharge. Home-based stroke rehabilitation devices could improve access to rehabilitation for stroke survivors, but the home environment presents unique challenges compared to clinics. The present study undertakes a scoping review of designs for at-home upper limb stroke rehabilitation mechatronic devices to identify important design principles and areas for improvement. Online databases were used to identify papers published 2010-2021 describing novel rehabilitation device designs, from which 59 publications were selected describing 38 unique designs. The devices were categorized and listed according to their target anatomy, possible therapy tasks, structure, and features. Twenty-two devices targeted proximal (shoulder and elbow) anatomy, 13 targeted distal (wrist and hand) anatomy, and three targeted the whole arm and hand. Devices with a greater number of actuators in the design were more expensive, with a small number of devices using a mix of actuated and unactuated degrees of freedom to target more complex anatomy while reducing the cost. Twenty-six of the device designs did not specify their target users' function or impairment, nor did they specify a target therapy activity, task, or exercise. Twenty-three of the devices were capable of reaching tasks, 6 of which included grasping capabilities. Compliant structures were the most common approach of including safety features in the design. Only three devices were designed to detect compensation, or undesirable posture, during therapy activities. Six of the 38 device designs mention consulting stakeholders during the design process, only two of which consulted patients specifically. Without stakeholder involvement, these designs risk being disconnected from user needs and rehabilitation best practices. Devices that combine actuated and unactuated degrees of freedom allow a greater variety and complexity of tasks while not significantly increasing their cost. Future home-based upper limb stroke rehabilitation mechatronic designs should provide information on patient posture during task execution, design with specific patient capabilities and needs in mind, and clearly link the features of the design to users' needs.

Design and Development of a Spherical 5-Bar Thumb Exoskeleton Mechanism for Poststroke Rehabilitation

This paper presents the kinematic design and development of a two degree-of-freedom (2DOF) spherical 5-bar thumb exoskeleton to augment the finger individuating grasp exercise robot (FINGER) rehabilitation robot, which assists the index and middle fingers individually in naturalistic grasping. The thumb module expands the capabilities of FINGER, allowing for broader proprioceptive training and assessment of hand function. The design process started by digitizing thumb-grasping motions to the index and the middle fingers separately, recorded from multiple healthy subjects utilizing a motion capture system. Fitting spheres to trajectory data of each subject allowed normalization of all subjects' data to a common center and radius. A two-revolute joint serial-chain mechanism was synthesized (intermediate optimization step) to reach the normalized trajectories. Next, the two resulting grasping trajectories were spatially sampled as targets for the 2DOF spherical 5-bar synthesis. Optimization of the spherical 5-bar included symmetry constraints and cost-function penalties for poor manipulability. The resulting exoskeleton assists both flexion/extension and abduction/adduction of the thumb enabling a wide range of motions. Consistent with FINGER, the parallel structure of the spherical 5-bar places the actuators at the base of the module, allowing for desirable characteristics, including high backdrivability, high controllable bandwidth, and low mechanical impedance. The mechanical design was developed from the kinematic solution, including an adjustable thumb cuff to accommodate different hand sizes. Fit and function of the device were tested on multiple subjects, including survivors of stroke. A proportional-derivative force controller with gravity and friction compensation was implemented to reduce resistance to motion during subject testing.

Neurorehabilitation robotics: how much control should therapists have?

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.

Feasibility of an Intelligent Algorithm Based on an Assist-as-Needed Controller for a Robot-Aided Gait Trainer (Lokomat) in Neurological Disorders: A Longitudinal Pilot Study

Most robotic gait assisted devices are designed to provide constant assistance during the training without taking into account each patient’s functional ability. The Lokomat offers an assist-as-needed control via the integrated exercise “Adaptive Gait Support” (AGS), which adapts the robotic support based on the patient’s abilities. The aims of this study were to examine the feasibility and characteristics of the AGS during long-term application. Ten patients suffering from neurological diseases underwent an 8-week Lokomat training with the AGS. They additionally performed conventional walking tests and a robotic force measurement. The difference between robotic support during adaptive and conventional training and the relationship between the robotic assessment and the conventional walking and force tests were examined. The results show that AGS is feasible during long-term application in a heterogeneous population. The support during AGS training in most of the gait phases was significantly lower than during conventional Lokomat training. A relationship between the robotic support level determined by the AGS and conventional walking tests was revealed. Moreover, combining the isometric force data and AGS data could divide patients into clusters, based on their ability to generate high forces and their level of motor control. AGS shows a high potential in assessing patients’ walking ability, as well as in providing challenging training, e.g., by automatically adjusting the robotic support throughout the whole gait cycle and enabling training at lower robotic support.

Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness

BACKGROUND

In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.

METHODS

Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.

RESULTS

(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.

CONCLUSIONS

Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.

Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability

BACKGROUND

Significant clinician training is required to mitigate the subjective nature and achieve useful reliability between measurement occasions and therapists. Previous research supports that robotic instruments can improve quantitative biomechanical assessments of the upper limb, offering reliable and more sensitive measures. Furthermore, combining kinematic and kinetic measurements with electrophysiological measurements offers new insights to unlock targeted impairment-specific therapy. This review presents common methods for analyzing biomechanical and neuromuscular data by describing their validity and reporting their reliability measures.

METHODS

This paper reviews literature (2000-2021) on sensor-based measures and metrics for upper-limb biomechanical and electrophysiological (neurological) assessment, which have been shown to correlate with clinical test outcomes for motor assessment. The search terms targeted robotic and passive devices developed for movement therapy. Journal and conference papers on stroke assessment metrics were selected using PRISMA guidelines. Intra-class correlation values of some of the metrics are recorded, along with model, type of agreement, and confidence intervals, when reported.

RESULTS

A total of 60 articles are identified. The sensor-based metrics assess various aspects of movement performance, such as smoothness, spasticity, efficiency, planning, efficacy, accuracy, coordination, range of motion, and strength. Additional metrics assess abnormal activation patterns of cortical activity and interconnections between brain regions and muscle groups; aiming to characterize differences between the population who had a stroke and the healthy population.

CONCLUSION

Range of motion, mean speed, mean distance, normal path length, spectral arc length, number of peaks, and task time metrics have all demonstrated good to excellent reliability, as well as provide a finer resolution compared to discrete clinical assessment tests. EEG power features for multiple frequency bands of interest, specifically the bands relating to slow and fast frequencies comparing affected and non-affected hemispheres, demonstrate good to excellent reliability for populations at various stages of stroke recovery. Further investigation is needed to evaluate the metrics missing reliability information. In the few studies combining biomechanical measures with neuroelectric signals, the multi-domain approaches demonstrated agreement with clinical assessments and provide further information during the relearning phase. Combining the reliable sensor-based metrics in the clinical assessment process will provide a more objective approach, relying less on therapist expertise. This paper suggests future work on analyzing the reliability of metrics to prevent biasedness and selecting the appropriate analysis.

Recent advancements in digital health management using multi-modal signal monitoring

Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.

The role of feedback in the robotic-assisted upper limb rehabilitation in people with multiple sclerosis: a systematic review

INTRODUCTION

Robotic-assisted upper limb rehabilitation might improve upper limb recovery in people with multiple sclerosis (PwMS) with moderate-to-severe disability. In the few existing studies, the training effects have been related to the type of intervention, if intensive, repetitive, or task-oriented training might promote neuroplasticity and recovery. Notably, most of these devices operate within a serious game context providing different feedback. Since feedback is a key component of motor control and thus involved in motor and cognitive rehabilitation, clinicians cannot desist from considering the potential contribution of feedback in the upper limb robot-assisted rehabilitation effects.

AREA COVERED

In this systematic review, we reported the rehabilitation protocols used in the robot-assisted upper limb training in PwMS to provide state-of-the-art on the role of feedback in robotic-assisted Upper Limb rehabilitation. PubMed, the Cochrane Library, and the Physiotherapy Evidence Database databases were systematically searched from inception to March 2022. After a literature search, the classification systems for feedback and the serious game were applied.

EXPERT OPINION

There is a need for sharing standard definitions and components of feedback and serious game in technologically assisted upper limb rehabilitation. Indeed, improving these aspects might further improve the effectiveness of such training in the management of PwMS.

How virtual and mechanical coupling impact bimanual tracking

Bilateral training systems look to promote the paretic hand's use in individuals with hemiplegia. Although this is normally achieved using mechanical coupling (i.e., a physical connection between the hands), a virtual reality system relying on virtual coupling (i.e., through a shared virtual object) would be simpler to use and prevent slacking. However, it is not clear whether different coupling modes differently impact task performance and effort distribution between the hands. We explored how 18 healthy right-handed participants changed their motor behaviors in response to the uninstructed addition of mechanical coupling, and virtual coupling using a shared cursor mapped to the average hands' position. In a second experiment, we then studied the impact of connection stiffness on performance, perception, and effort imbalance. The results indicated that both coupling types can induce the hands to actively contribute to the task. However, the task asymmetry introduced by using a cursor mapped to either the left or right hand only modulated the hands' contribution when not mechanically coupled. The tracking performance was similar for all coupling types, independent of the connection stiffness, although the mechanical coupling was preferred and induced the hands to move with greater correlation. These findings suggest that virtual coupling can induce the hands to actively contribute to a task in healthy participants without hindering their performance. Further investigation on the coupling types' impact on the performance and hands' effort distribution in patients with hemiplegia could allow for the design of simpler training systems that promote the affected hand's use.NEW & NOTEWORTHY We showed that the uninstructed addition of a virtual and/or a mechanical coupling can induce both hands to actively contribute in a continuous redundant bimanual tracking task without impacting performance. In addition, we showed that the task asymmetry can only alter the effort distribution when the hands are not connected, independent of the connection stiffness. Our findings suggest that virtual coupling could be used in the development of simpler VR-based training devices.

Effects of stance control via hidden Markov model-based gait phase detection on healthy users of an active hip-knee exoskeleton

Introduction: In the past years, robotic lower-limb exoskeletons have become a powerful tool to help clinicians improve the rehabilitation process of patients who have suffered from neurological disorders, such as stroke, by applying intensive and repetitive training. However, active subject participation is considered to be an important feature to promote neuroplasticity during gait training. To this end, the present study presents the performance assessment of the AGoRA exoskeleton, a stance-controlled wearable device designed to assist overground walking by unilaterally actuating the knee and hip joints. Methods: The exoskeleton's control approach relies on an admittance controller, that varies the system impedance according to the gait phase detected through an adaptive method based on a hidden Markov model. This strategy seeks to comply with the assistance-as-needed rationale, i.e., an assistive device should only intervene when the patient is in need by applying Human-Robot interaction (HRI). As a proof of concept of such a control strategy, a pilot study comparing three experimental conditions (i.e., unassisted, transparent mode, and stance control mode) was carried out to evaluate the exoskeleton's short-term effects on the overground gait pattern of healthy subjects. Gait spatiotemporal parameters and lower-limb kinematics were captured using a 3D-motion analysis system Vicon during the walking trials. Results and Discussion: By having found only significant differences between the actuated conditions and the unassisted condition in terms of gait velocity (ρ = 0.048) and knee flexion (ρ ≤ 0.001), the performance of the AGoRA exoskeleton seems to be comparable to those identified in previous studies found in the literature. This outcome also suggests that future efforts should focus on the improvement of the fastening system in pursuit of kinematic compatibility and enhanced compliance.

Postural control exercise without using the upper limbs improves activities of daily living in patients with stroke

Introduction

Stroke is one of the most common neurological disorders worldwide. Stroke survivors have restricted activities of daily living (ADL) and lower functional independence measures (FIM) after disease onset. Recovery of postural control abilities in patients with stroke is one of the most important therapeutic goals. In this study, we examined the differences in the FIM motor items between groups that performed postural control exercises with the upper limb and those that performed postural control exercises without the upper limb.

Methods

The medical records of patients with stroke admitted and discharged from the Recovery Rehabilitation Unit at Azumino Red Cross Hospital between 2016 and 2018 were reviewed. We retrospectively investigated the relationships between postural control exercises with or without upper limbs, FIM motor items at admission and discharge, and percentage of gait acquisition at discharge.

Results and Discussion

Among the thirteen FIM motor items, nine (bathing, dressing the upper body, dressing the lower body, toileting, transfers [bed, chair, and wheelchair], transfers [toilet], transfers [tub or shower], locomotion, and climbing of stairs) were significantly different between the two groups (those who performed postural control exercises with the upper limb and those who performed postural control exercises without the upper limb). Patients with stroke who performed postural control exercises without the upper limbs showed a higher percentage of gait acquisition. Touch contact during quiet standing reduces body sway and the associated fluctuations. However, continual practice of postural control with a small degree of body sway for a long period after a stroke would result in decreased pressure on the sole. This may hinder postural control relearning. Touch contact also reduces anticipatory postural adjustment, which may limit the improvement in balance ability during physical exercise. Postural control exercises without the upper limbs improve postural control ability and may be beneficial from a long-term perspective.

Robot-assisted therapy for upper limb paresis after stroke: Use of robotic algorithms in advanced practice

BACKGROUND

Rehabilitation of stroke-related upper limb paresis is a major public health issue.

OBJECTIVE

Robotic systems have been developed to facilitate neurorehabilitation by providing key elements required to stimulate brain plasticity and motor recovery, namely repetitive, intensive, adaptative training with feedback. Although the positive effect of robot-assisted therapy on motor impairments has been well demonstrated, the effect on functional capacity is less certain.

METHOD

This narrative review outlines the principles of robot-assisted therapy for the rehabilitation of post-stroke upper limb paresis.

RESULTS

A paradigm is proposed to promote not only recovery of impairment but also function.

CONCLUSION

Further studies that would integrate some principles of the paradigm described in this paper are needed.

Orientation control strategies and adaptation to a visuomotor perturbation in rotational hand movements

Computational approaches to biological motor control are used to discover the building blocks of human motor behaviour. Models explaining features of human hand movements have been studied thoroughly, yet only a few studies attempted to explain the control of the orientation of the hand; instead, they mainly focus on the control of hand translation, predominantly in a single plane. In this study, we present a new methodology to study the way humans control the orientation of their hands in three dimensions and demonstrate it in two sequential experiments. We developed a quaternion-based score that quantifies the geodicity of rotational hand movements and evaluated it experimentally. In the first experiment, participants performed a simple orientation-matching task with a robotic manipulator. We found that rotations are generally performed by following a geodesic in the quaternion hypersphere, which suggests that, similarly to translation, the orientation of the hand is centrally controlled, possibly by optimizing geometrical properties of the hand’s rotation. This result established a baseline for the study of human response to perturbed visual feedback of the orientation of the hand. In the second experiment, we developed a novel visuomotor rotation task in which the rotation is applied on the hand’s rotation, and studied the adaptation of participants to this rotation, and the transfer of the adaptation to a different initial orientation. We observed partial adaptation to the rotation. The patterns of the transfer of the adaptation to a different initial orientation were consistent with the representation of the orientation in extrinsic coordinates. The methodology that we developed allows for studying the control of a rigid body without reducing the dimensionality of the task. The results of the two experiments open questions for future studies regarding the mechanisms underlying the central control of hand orientation. These results can be of benefit for many applications that involve fine manipulation of rigid bodies, such as teleoperation and neurorehabilitation.

Design and torque control base on neural network PID of a variable stiffness joint for rehabilitation robot

Variable stiffness joints have been gradually applied in rehabilitation robots because of their intrinsic compliance and greater ability to adjust mechanical stiffness. This paper designs a variable stiffness joint for upper limb rehabilitation training. The joint adopts the variable stiffness principle based special curved surface. The trapezoidal lead screw in the variable stiffness module has a self-locking function, and the stiffness can be maintained without the continuous output torque of the motor. In the aspect of control, back propagation (BP) neural network PID control strategy is used to control the torque of variable stiffness joint. Experiments show that this control method can effectively improve the torque control performance of variable stiffness joints in the case of low stiffness, and the isotonic centripetal resistance training can be realized by using the joints and control methods designed in this paper.

Automated patient-robot assignment for a robotic rehabilitation gym: a simplified simulation model

Background

A robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome.

Methods

As a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session.

Results

Analyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice.

Conclusions

Though it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.

A Semi-Supervised Progressive Learning Algorithm for Brain-Computer Interface

Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semi-supervised learning framework for EEG classification and EEG-EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement). The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.

Motor Control: A Conceptual Framework for Rehabilitation

There is a lack of conceptual and theoretical clarity among clinicians and researchers regarding the control of motor actions based on the use of the term "motor control." It is important to differentiate control processes from observations of motor output to improve communication and to make progress in understanding motor disorders and their remediation. This article clarifies terminology related to theoretical concepts underlying the control of motor actions, emphasizing how the term "motor control" is applied in neurorehabilitation. Two major opposing theoretical frameworks are described (i.e., direct and indirect), and their strengths and pitfalls are discussed. Then, based on the proposition that sensorimotor rehabilitation should be predicated on one comprehensive theory instead of an eclectic mix of theories and models, several solutions are offered about how to address controversies in motor learning, optimality, and adaptability of movement.

Impact of Gravity Compensation on Upper Extremity Movements in Harmony Exoskeleton

Robots have been used to offset the limb weight through gravity compensation in upper body rehabilitation to delineate the effects of loss of strength and loss of dexterity, which are two common forms of post-stroke impairments. In this paper, we explored the impact of this anti-gravity support on the quality of movement during reaching and coordinated arm movements in a pilot study with two participants with chronic stroke. The subjects donned the Harmony exoskeleton which supported proper shoulder coordination in addition to providing gravity compensation. Participants had previously taken part in seven one-hour sessions with the Harmony exoskeleton, performing six sets of passive-stretching and active exercises. Pre- and post-training sessions included assessments of two separate tasks, planar reaching and a set of six coordinated arm movements, in two conditions, outside of and supported by the exoskeleton. The movements were recorded using an optical motion capture system and analyzed using spectral arc length (SPARC) and straight line deviation to quantify movement smoothness and quality. We observed that gravity compensation resulted in an increased smoothness for the subject with high level of impairment whereas compensation resulted in a reduction in smoothness for the subject with low level of impairment in the reaching task. Both participants demonstrated better coordination of the shoulder-arm joint with gravity compensation. This result motivates further studies into the role of gravity compensation during coordinated movement training and rehabilitation interventions.

Hand Rehabilitation Devices: A Comprehensive Systematic Review

A cerebrovascular accident, or a stroke, can cause significant neurological damage, inflicting the patient with loss of motor function in their hands. Standard rehabilitation therapy for the hand increases demands on clinics, creating an avenue for powered hand rehabilitation devices. Hand rehabilitation devices (HRDs) are devices designed to provide the hand with passive, active, and active-assisted rehabilitation therapy; however, HRDs do not have any standards in terms of development or design. Although the categorization of an injury’s severity can guide a patient into seeking proper assistance, rehabilitation devices do not have a set standard to provide a solution from the beginning to the end stages of recovery. In this paper, HRDs are defined and compared by their mechanical designs, actuation mechanisms, control systems, and therapeutic strategies. Furthermore, devices with conducted clinical trials are used to determine the future development of HRDs. After evaluating the abilities of 35 devices, it is inferred that standard characteristics for HRDs should include an exoskeleton design, the incorporation of challenge-based and coaching therapeutic strategies, and the implementation of surface electromyogram signals (sEMG) based control.

Effects of control strategies on gait in robot-assisted post-stroke lower limb rehabilitation: a systematic review

BACKGROUND

Stroke related motor function deficits affect patients' likelihood of returning to professional activities, limit their participation in society and functionality in daily living. Hence, robot-aided gait rehabilitation needs to be fruitful and effective from a motor learning perspective. For this reason, optimal human-robot interaction strategies are necessary to foster neuroplastic shaping during therapy. Therefore, we performed a systematic search on the effects of different control algorithms on quantitative objective gait parameters of post-acute stroke patients.

METHODS

We conducted a systematic search on four electronic databases using the Population Intervention Comparison and Outcome format. The heterogeneity of performance assessment, study designs and patients' numerosity prevented the possibility to conduct a rigorous meta-analysis, thus, the results were presented through narrative synthesis.

RESULTS

A total of 31 studies (out of 1036) met the inclusion criteria, without applying any temporal constraints. No controller preference with respect to gait parameters improvements was found. However, preferred solutions were encountered in the implementation of force control strategies mostly on rigid devices in therapeutic scenarios. Conversely, soft devices, which were all position-controlled, were found to be more commonly used in assistive scenarios. The effect of different controllers on gait could not be evaluated since conspicuous heterogeneity was found for both performance metrics and study designs.

CONCLUSIONS

Overall, due to the impossibility of performing a meta-analysis, this systematic review calls for an outcome standardisation in the evaluation of robot-aided gait rehabilitation. This could allow for the comparison of adaptive and human-dependent controllers with conventional ones, identifying the most suitable control strategies for specific pathologic gait patterns. This latter aspect could bolster individualized and personalized choices of control strategies during the therapeutic or assistive path.

Artificial Partners to Understand Joint Action: Representing Others to Develop Effective Coordination

In the last years, artificial partners have been proposed as tools to study joint action, as they would allow to address joint behaviors in more controlled experimental conditions. Here we present an artificial partner architecture which is capable of integrating all the available information about its human counterpart and to develop efficient and natural forms of coordination. The model uses an extended state observer which combines prior information, motor commands and sensory observations to infer the partner's ongoing actions (partner model). Over trials, these estimates are gradually incorporated into action selection. Using a joint planar task in which the partners are required to perform reaching movements while mechanically coupled, we demonstrate that the artificial partner develops an internal representation of its human counterpart, whose accuracy depends on the degree of mechanical coupling and on the reliability of the sensory information. We also show that human-artificial dyads develop coordination strategies which closely resemble those observed in human-human dyads and can be interpreted as Nash equilibria. The proposed approach may provide insights for the understanding of the mechanisms underlying human-human interaction. Further, it may inform the development of novel neuro-rehabilitative solutions and more efficient human-machine interfaces.

Robotic Systems for the Physiotherapy Treatment of Children with Cerebral Palsy: A Systematic Review

Cerebral palsy is a neurological condition that is associated with multiple motor alterations and dysfunctions in children. Robotic systems are new devices that are becoming increasingly popular as a part of the treatment for cerebral palsy. A systematic review of the Pubmed, Web of Science, MEDLINE, Cochrane, Dialnet, CINAHL, Scopus, Lilacs and PEDro databases from November 2021 to February 2022 was conducted to prove the effectiveness of these devices for the treatment of motor dysfunctions in children who were diagnosed with cerebral palsy. Randomized clinical trials in Spanish and English were included. In total, 653 potential manuscripts were selected but only 7 of them met the inclusion criteria. Motor dysfunctions in the lower limbs and those that are specifically related to gait are the main parameters that are affected by cerebral palsy and the robotic systems Lokomat, Innowalk, Robogait and Waltbox-K are the most commonly used. There is no consensus about the effectiveness of these devices. However, it seems clear that they have presented a good complement to conventional physical therapies, although not a therapy as themselves. Unfortunately, the low quality of some of the randomized clinical trials that were reviewed made it difficult to establish conclusive results. More studies are needed to prove and test the extent to which these devices aid in the treatment of children with cerebral palsy.

Preclinical upper limb neurorobotic platform to assess, rehabilitate, and develop therapies

Numerous neurorehabilitative, neuroprosthetic, and repair interventions aim to address the consequences of upper limb impairments after neurological disorders. Although these therapies target widely different mechanisms, they share the common need for a preclinical platform that supports the development, assessment, and understanding of the therapy. Here, we introduce a neurorobotic platform for rats that meets these requirements. A four-degree-of-freedom end effector is interfaced with the rat's wrist, enabling unassisted to fully assisted execution of natural reaching and retrieval movements covering the entire body workspace. Multimodal recording capabilities permit precise quantification of upper limb movement recovery after spinal cord injury (SCI), which allowed us to uncover adaptations in corticospinal tract neuron dynamics underlying this recovery. Personalized movement assistance supported early neurorehabilitation that improved recovery after SCI. Last, the platform provided a well-controlled and practical environment to develop an implantable spinal cord neuroprosthesis that improved upper limb function after SCI.

Ergodic Shared Control: Closing the Loop on pHRI Based on Information Encoded in Motion

Advances in exoskeletons and robot arms have given us increasing opportunities for providing physical support and meaningful feedback in training and rehabilitation settings. However, the chosen control strategies must support motor learning and provide mathematical task definitions that are actionable for the actuation. Typical robot control architectures rely on measuring error from a reference trajectory. In physical human-robot interaction, this leads to low engagement, invariant practice, and few errors, which are not conducive to motor learning. A reliance on reference trajectories means that the task definition is both over-specified—requiring specific timings not critical to task success—and lacking information about normal variability. In this article, we examine a way to define tasks and close the loop using an ergodic measure that quantifies how much information about a task is encoded in the human-robot motion. This measure can capture the natural variability that exists in typical human motion—enabling therapy based on scientific principles of motor learning. We implement an ergodic hybrid shared controller(HSC) on a robotic arm as well as an error-based controller—virtual fixtures—in a timed drawing task. In a study of 24 participants, we compare ergodic HSC with virtual fixtures and find that ergodic HSC leads to improved training outcomes.

Comparative effects of passive and active mode robot-assisted gait training on brain and muscular activities in sub-acute and chronic stroke

BACKGROUND

Robot-assisted gait training (RAGT) was initially developed based on the passive controlled (PC) mode, where the target or ideal locomotor kinematic trajectory is predefined and a patient basically 'rides' the robot instead of actively participating in the actual locomotor relearning process. A new insightful contemporary neuroscience and mechatronic evidence suggest that robotic-based locomotor relearning can be best achieved through active interactive (AI) mode rather than PC mode.

OBJECTIVE

The purpose of this study was to compare the pattern of gait-related cortical activity, specifically gait event-related spectral perturbations (ERSPs), and muscle activity from the tibialis anterior (TA) and clinical functional tests in subacute and chronic stroke patients during robot-assisted gait training (RAGT) in passive controlled (PC) and active interactive (AI) modes.

METHODS

The present study involves a two-group pretest-posttest design in which two groups (i.e., PC-RAGT group and AI-RAGT group) of 14 stroke subjects were measured to assess changes in ERSPs, the muscle activation of TA, and the clinical functional tests, following 15- 18 sessions of intervention according to the protocol of each group.

RESULTS

Our preliminary results demonstrated that the power in the μ band (8- 12 Hz) was increased in the leg area of sensorimotor cortex (SMC) and supplementary motor area (SMA) at post-intervention as compared to pre-intervention in both groups. Such cortical neuroplasticity change was associated with TA muscle activity during gait and functional independence in functional ambulation category (FAC) and motor coordination in Fugl- Meyer Assessment for lower extremity (FMA-LE) test as well as spasticity in the modified Ashworth scale (MAS) measures.

CONCLUSIONS

We have first developed a novel neuroimaging experimental paradigm which distinguished gait event related cortical involvement between pre- and post-intervention with PC-RAGT and AI-RAGT in individuals with subacute and chronic hemiparetic stroke.

Real-Time Control of a Multi-Degree-of-Freedom Mirror Myoelectric Interface During Functional Task Training

Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional tasks and the effectiveness of the rehabilitation therapy. The objective of this study was to develop a myoelectric interface that would allow multi-degree-of-freedom control of an exoskeleton involving arm, wrist and hand joints, with an eye toward rehabilitation. We tested the effectiveness of a myoelectric decoder trained with data from one upper limb and mirrored to control a multi-degree-of-freedom exoskeleton with the opposite upper limb (i.e., mirror myoelectric interface) in 10 healthy participants. We demonstrated successful simultaneous control of multiple upper-limb joints by all participants. We showed evidence that subjects learned the mirror myoelectric model within the span of a five-session experiment, as reflected by a significant decrease in the time to execute trials and in the number of failed trials. These results are the necessary precursor to evaluating if a decoder trained with EMG from the healthy limb could foster learning of natural EMG patterns and lead to motor rehabilitation in stroke patients.

Impact of the robotic-assistance level on upper extremity function in stroke patients receiving adjunct robotic rehabilitation: sub-analysis of a randomized clinical trial

Background

Robotic therapy has been demonstrated to be effective in treating upper extremity (UE) paresis in stroke survivors. However, it remains unclear whether the level of assistance provided by robotics in UE training could affect the improvement in UE function in stroke survivors. We aimed to exploratorily investigate the impact of robotic assistance level and modes of adjustment on functional improvement in a stroke-affected UE.

Methods

We analyzed the data of 30 subacute stroke survivors with mild-to-severe UE hemiplegia who were randomly assigned to the robotic therapy (using ReoGo System) group in our previous randomized clinical trial. A cluster analysis based on the training results (the percentage of each stroke patient’s five assistance modes of robotics used during the training) was performed. The patients were divided into two groups: high and low robotic assistance groups. Additionally, the two groups were sub-categorized into the following classes based on the severity of UE functional impairment: moderate-to-mild [Fugl-Meyer Assessment (FMA) score ≥ 30] and severe-to-moderate class (FMA < 30). The outcomes were assessed using FMA, FMA-proximal, performance-time in the Wolf motor function test (WMFT), and functional assessment scale (FAS) in WMFT. The outcomes of each class in the two groups were analyzed. A two-way analysis of variance (ANOVA) was conducted with robot assistance level and severity of UE function as explanatory factors and the change in each outcome pre- and post-intervention as the objective factor.

Results

Overall, significant differences of the group × severity interaction were found in most of the outcomes, including FMA-proximal (p = 0.038, η² = 0.13), WMFT-PT (p = 0.021, η² = 0.17), and WMFT-FAS (p = 0.045, η² = 0.14). However, only the FMA score appeared not to be significantly different in each group (p = 0.103, η² = 0.09).

Conclusion

An optimal amount of robotic assistance is a key to maximize improvement in post-stroke UE paralysis. Furthermore, severity of UE paralysis is an important consideration when deciding the amount of assistance in robotic therapy.

Trial registration Trial enrollment was done at UMIN (UMIN 000001619, registration date was January 1, 2009)

Cooperative ankle-exoskeleton control can reduce effort to recover balance after unexpected disturbances during walking

Background

In the last two decades, lower-limb exoskeletons have been developed to assist human standing and locomotion. One of the ongoing challenges is the cooperation between the exoskeleton balance support and the wearer control. Here we present a cooperative ankle-exoskeleton control strategy to assist in balance recovery after unexpected disturbances during walking, which is inspired on human balance responses.

Methods

We evaluated the novel controller in ten able-bodied participants wearing the ankle modules of the Symbitron exoskeleton. During walking, participants received unexpected forward pushes with different timing and magnitude at the pelvis level, while being supported (Exo-Assistance) or not (Exo-NoAssistance) by the robotic assistance provided by the controller. The effectiveness of the assistive strategy was assessed in terms of (1) controller performance (Detection Delay, Joint Angles, and Exerted Ankle Torques), (2) analysis of effort (integral of normalized Muscle Activity after perturbation onset); and (3) Analysis of center of mass COM kinematics (relative maximum COM Motion, Recovery Time and Margin of Stability) and spatio-temporal parameters (Step Length and Swing Time).

Results

In general, the results show that when the controller was active, it was able to reduce participants’ effort while keeping similar ability to counteract and withstand the balance disturbances. Significant reductions were found for soleus and gastrocnemius medialis activity of the stance leg when comparing Exo-Assistance and Exo-NoAssistance walking conditions.

Conclusions

The proposed controller was able to cooperate with the able-bodied participants in counteracting perturbations, contributing to the state-of-the-art of bio-inspired cooperative ankle exoskeleton controllers for supporting dynamic balance. In the future, this control strategy may be used in exoskeletons to support and improve balance control in users with motor disabilities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01000-y.