A Review of Robot-Assisted Lower-Limb Stroke Therapy: Unexplored Paths and Future Directions in Gait Rehabilitation

Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot

The use of bodyweight unloading force control on a treadmill with therapist manual assistance for gait training imposes constraints on natural walking. It influences the patient’s training effect for a full range of natural walks. This study presents a prototype and a safety controller for a mobile rehabilitation robot (MRR). The prototype integrates an autonomous mobile bodyweight support system (AMBSS) with a lower-limb exoskeleton system (LES) to simultaneously achieve natural over-ground gait training and motion relearning. Human-centered rehabilitation robots must guarantee the safety of patients in the presence of significant tracking errors. It is difficult for traditional stiff controllers to ensure safety and excellent tracking accuracy concurrently, because they cannot explicitly guarantee smooth, safe, and overdamped motions without overshoot. This paper integrated a linear extended state observer (LESO) into proxy-based sliding mode control (ILESO-PSMC) to overcome this problem. The LESO was used to observe the system’s unknown states and total disturbance simultaneously, ensuring that the “proxy” tracks the reference target accurately and avoids the unsafe control of the MRR. Based on the Lyapunov theorem to prove the closed-loop system stability, the proposed safety control strategy has three advantages: (1) it provides an accurate and safe control without worsening tracking performance during regular operation, (2) it guarantees safe recoveries and overdamped properties after abnormal events, and (3) it need not identify the system model and measure unknown system states as well as external disturbance, which is quite difficult for human–robot interaction (HRI) systems. The results demonstrate the feasibility of the proposed ILESO-PSMC for MRR. The experimental comparison also indicates better safety performance for the ILESO-PSMC than for the conventional proportional–integral–derivative (PID) control.

Is robot-assisted gait training intensity a determinant of functional recovery early after stroke? A pragmatic observational study of clinical care

Gait rehabilitation is a critical factor in functional recovery after a stroke. The aim of this pragmatic observational study was to identify the optimal dose and timing of robot-assisted gait training (RAGT) that can lead to a favourable outcome in a sample of subacute stroke survivors. Subacute patients with stroke who underwent a RAGT within a multidisciplinary rehabilitation program were enrolled. A set of clinical (i.e. age, type of stroke and time since stroke) and rehabilitation stay outcomes (length of stay and RAGT number of sessions) were recorded to evaluate their impact on functional outcome measures by functional independence measure (FIM) or functional ambulation category (FAC). We included 236 patients (62.73 ± 11.82 year old); 38.44% were females, and 59.32% were ischaemic stroke patients. Patients that received at least 14 RAGT sessions, had 15.83% more chance to be responders compared to those that receive less sessions (P = 0.006). Similarly, younger patients (≤60 years) were more prone to be responders (+15.1%). Lastly, an early rehabilitation (<6 weeks) was found to be more efficient (+21.09%) in determining responsiveness (P < 0.001). Becoming newly independent for gait, that refers to a FAC score ≥4, was related with age and RAGT sessions (P = 0.001). In conclusion, a younger age (≤60 years), an early rehabilitation (<6 weeks since stroke) and a higher RAGT dose (at least 14 sessions) were related to a favourable outcome in patients with subacute stroke.

The relationship between upper limb function and activities of daily living without the effects of lower limb function: A cross-sectional study

Introduction

Upper limb motor function and activities of daily living (ADL) are related in chronic stroke patients. This study investigated this relationship after removal of the influence of motor function of the affected lower limb, which until now has remained unclear.

Methods

This retrospective cross-sectional study included 53 patients with chronic stroke. Upper and lower limb motor function and ADL were assessed using the Fugl-Meyer assessment of the upper (FMA-UL) and lower limbs (FMA-LL) and functional independence measure motor score (FIM-M). To clarify the relationship between FMA-UL and total FIM-M before and after removal of the influence of FMA-LL, Spearman's rank correlation coefficient and partial correlation analysis were used. The relationship between FMA-UL and each item of FIM-M after removal of the influence of FMA-LL was assessed using partial correlation analysis.

Results

Before the influence of FMA-LL was removed, FMA-UL was moderately to well correlated with total FIM-M. This became weak after the influence was removed. Regarding each item of FIM-M, FMA-UL was correlated with dressing (upper body), toileting, and walking or wheelchair after removal of the influence.

Conclusion

The relationship between upper limb motor function and ADL is strongly influenced by lower limb motor function.

A Robot-Assisted Therapy to Increase Muscle Strength in Hemiplegic Gait Rehabilitation

This study examines the feasibility of using a robot-assisted therapy methodology based on the Bobath concept to perform exercises applied in conventional therapy for gait rehabilitation in stroke patients. The aim of the therapy is to improve postural control and movement through exercises based on repetitive active-assisted joint mobilization, which is expected to produce strength changes in the lower limbs. As therapy progresses, robotic assistance is gradually reduced and the patient's burden increases with the goal of achieving a certain degree of independence. The relationship between force and range of motion led to the analysis of both parameters of interest. The study included 23 volunteers who performed 24 sessions, 2 sessions per week for 12 weeks, each lasting about 1 h. The results showed a significant increase in hip abduction and knee flexion strength on both sides, although there was a general trend of increased strength in all joints. However, the range of motion at the hip and ankle joints was reduced. The usefulness of this platform for transferring exercises from conventional to robot-assisted therapies was demonstrated, as well as the benefits that can be obtained in muscle strength training. However, it is suggested to complement the applied therapy with exercises for the maintenance and improvement of the range of motion.

An Inverse Dynamics-Based Control Approach for Compliant Control of Pneumatic Artificial Muscles

Rehabilitation is an area of robotics in which human–robot collaboration occurs, requiring adaptation and compliance. Pneumatic artificial muscles (PAM) are soft actuators that have built-in compliance making them usable for rehabilitation robots. Conversely, compliance arises from nonlinear characteristics and generates obstructions in modeling and controlling actions. It is a critical issue limiting the use of PAM. In this work, multi-input single-output (MISO) inverse modeling and inverse dynamics model learning approaches are combined to obtain a novel nonlinear adaptive control scheme for single PAM-actuated 1-DoF rehabilitation devices, for instance, continuous passive motion (CPM) devices. The objective of the proposed system is to bring an alternative solution to the compliant operation of PAM while performing exercise trajectories, to satisfy requirements such as larger range of motion (ROM) and adaptability to external load impedance variations. The control system combines the operation of a nonlinear autoregressive network with exogenous inputs (NARX)-based inverse dynamics estimator used as a global range controller and cascade PIDs for local position and pressure loops. Implementation results demonstrated the efficacy of the introduced method in terms of compliant operation for dynamic external load variations as well as a stable operation in case of impulsive disturbances. To summarize, a simple but efficient method is illustrated to facilitate the common use of PAM.

Development of a 3D Relative Motion Method for Human-Robot Interaction Assessment

Exoskeletons have been assessed by qualitative and quantitative features known as performance indicators. Within these, the ergonomic indicators have been isolated, creating a lack of methodologies to analyze and assess physical interfaces. In this sense, this work presents a three-dimensional relative motion assessment method. This method quantifies the difference of orientation between the user’s limb and the exoskeleton link, providing a deeper understanding of the Human–Robot interaction. To this end, the AGoRA exoskeleton was configured in a resistive mode and assessed using an optoelectronic system. The interaction quantified a difference of orientation considerably at a maximum value of 41.1 degrees along the sagittal plane. It extended the understanding of the Human–Robot Interaction throughout the three principal human planes. Furthermore, the proposed method establishes a performance indicator of the physical interfaces of an exoskeleton.

[Research on gait recognition and prediction based on optimized machine learning algorithm]

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.

Brain Network Organization Following Post-Stroke Neurorehabilitation

Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.

The Application of Technological Intervention for Stroke Rehabilitation in Southeast Asia: A Scoping Review With Stakeholders' Consultation

BACKGROUND

The technological intervention is considered as an adjunct to the conventional therapies applied in the rehabilitation session. In most high-income countries, technology has been widely used in assisting stroke survivors to undergo their treatments. However, technology use is still lacking in Southeast Asia, especially in middle- and low-income countries. This scoping review identifies and summarizes the technologies and related gaps available in Southeast Asia pertaining to stroke rehabilitation.

METHODS

The JBI manual for evidence synthesis was used to conduct a scoping study. Until September 2021, an electronic search was performed using four databases (Medline, CINAHL, Scopus, ASEAN Citation Index). Only the studies that were carried out in Southeast Asia were chosen.

RESULTS

Forty-one articles were chosen in the final review from 6,873 articles found during the initial search. Most of the studies reported the implementation of technological intervention combined with conventional therapies in stroke rehabilitation. Advanced and simple technologies were found such as robotics, virtual reality, telerehabilitation, motion capture, assistive devices, and mobility training from Singapore, Thailand, Malaysia, and Indonesia. The majority of the studies show that technological interventions can enhance the recovery period of stroke survivors. The consultation session suggested that the technological interventions should facilitate the needs of the survivors, caregivers, and practitioners during the rehabilitation.

CONCLUSIONS

The integration of technology into conventional therapies has shown a positive outcome and show significant improvement during stroke recovery. Future studies are recommended to investigate the potential of home-based technological intervention and lower extremities.

Information Security in Medical Robotics: A Survey on the Level of Training, Awareness and Use of the Physiotherapist

Cybersecurity is becoming an increasingly important aspect to investigate for the adoption and use of care robots, in term of both patients' safety, and the availability, integrity and privacy of their data. This study focuses on opinions about cybersecurity relevance and related skills for physiotherapists involved in rehabilitation and assistance thanks to the aid of robotics. The goal was to investigate the awareness among insiders about some facets of cybersecurity concerning human-robot interactions. We designed an electronic questionnaire and submitted it to a relevant sample of physiotherapists. The questionnaire allowed us to collect data related to: (i) use of robots and its relationship with cybersecurity in the context of physiotherapy; (ii) training in cybersecurity and robotics for the insiders; (iii) insiders' self-assessment on cybersecurity and robotics in some usage scenarios, and (iv) their experiences of cyber-attacks in this area and proposals for improvement. Besides contributing some specific statistics, the study highlights the importance of both acculturation processes in this field and monitoring initiatives based on surveys. The study exposes direct suggestions for continuation of these types of investigations in the context of scientific societies operating in the rehabilitation and assistance robotics. The study also shows the need to stimulate similar initiatives in other sectors of medical robotics (robotic surgery, care and socially assistive robots, rehabilitation systems, training for health and care workers) involving insiders.

[Implementation of robotic mechanotherapy for movement recovery in patients after stroke]

Lower extremity dysfunction after a stroke can vary from mild to extremely severe and significantly reduce the functional independence of patients. The restoration of walking is one of the key components of rehabilitation, it requires a balanced approach and the participation of a multidisciplinary team. In the last decade, new rehabilitation methods have appeared that meet high safety standards and have a minimum set of contraindications. One of the promising methods is robotic mechanotherapy. The article presents an overview of modern technologies of robotic mechanotherapy, its types and recommendations for use in medical rehabilitation.

Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism

To date, several alterations in the gait pattern can be treated through rehabilitative approaches and robot assisted therapy (RAT). Gait data and gait trajectories are essential in specific exoskeleton control strategies. Nevertheless, the scarcity of human gait data due to the high cost of data collection or privacy concerns can hinder the performance of controllers or models. This paper thus first creates a GANs-based (Generative Adversarial Networks) data augmentation method to generate synthetic human gait data while still retaining the dynamics of the real gait data. Then, both the real collected and the synthesized gait data are fed to our constructed two-stage attention model for gait trajectories prediction. The real human gait data are collected with the five healthy subjects recruited from an optical motion capture platform. Experimental results indicate that the created GANs-based data augmentation model can synthesize realistic-looking multi-dimensional human gait data. Also, the two-stage attention model performs better compared with the LSTM model; the attention mechanism shows a higher capacity of learning dependencies between the historical gait data to accurately predict the current values of the hip joint angles and knee joint angles in the gait trajectory. The predicted gait trajectories depending on the historical gait data can be further used for gait trajectory tracking strategies.

A model-free deep reinforcement learning approach for control of exoskeleton gait patterns

Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user’s interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals’ baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training.

A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton

Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.

Mechanical Design and Control System Development of a Rehabilitation Robotic System for Walking With Arm Swing

Background: Interlimb neural coupling implies that arm swing should be included during gait training to improve rehabilitation outcomes. We previously developed several systems for production of walking with arm swing, but the reaction forces on the foot sole during usage of the systems were not satisfactory and there was potential to improve control system performance. This work aimed to design and technically evaluate a novel system for producing walking with synchronised arm and leg movement and with dynamic force loading on the foot soles.

Methods: The robotic system included a passive curved treadmill and a trunk frame, upon which the rigs for the upper and lower limbs were mounted. Ten actuators and servocontrollers with EtherCAT communication protocol controlled the bilateral shoulder, elbow, hip, knee and ankle joints. Impedance control algorithms were developed and ran in an industrial PC. Flexible pressure sensors recorded the plantar forces on the foot soles. The criteria of implementation and responsiveness were used to formally evaluate the technical feasibility of the system.

Results: Using impedance algorithms, the system produced synchronous walking with arm swing on the curved treadmill, with mean RMS angular tracking error <2° in the 10 joint profiles. The foot trajectories relative to the hip presented similar shapes to those during normal gait, with mean RMS displacement error <1.5 cm. A force pattern that started at the heel and finished at the forefoot was observed during walking using the system, which was similar to the pattern from overground walking.

Conclusion: The robotic system produced walking-like kinematics in the 10 joints and in the foot trajectories. Integrated with the curved treadmill, the system also produced walking-like force patterns on the foot soles. The system is considered feasible as far as implementation and responsiveness are concerned. Future work will focus on improvement of the mechanical system for future clinical application.

The Efficacy of Interlimb-Coordinated Intervention on Gait and Motor Function Recovery in Patients with Acute Stroke: A Multi-Center Randomized Controlled Trial Study Protocol

BACKGROUND

The efficacy of interlimb-coordinated training on gait and upper limb functional improvement remains unclear. The latest published randomized controlled trials have supported the potential benefits of interlimb-coordinated training to enhance gait function. Upper limb functional recovery may also benefit from interlimb-coordinated training since most everyday activities require the coordinated use of both hands to complete a task. This study investigates the efficacy of interlimb-coordinated training on gait and upper limb functional recovery over a short-medium term period.

METHODS

A total of 226 acute stroke patients will be recruited from four centres over four years. Patients will be randomly allocated to either conventional therapy or conventional therapy plus interlimb-coordinated training. Outcomes will be recorded at baseline, after 2 weeks of intervention, and at 3- and 6-months post-intervention. Gait speed is the primary outcome measure. Secondary outcome measures include Fugl-Meyer Assessment of Motor Recovery, Berg Balance Scale, Timed Up and Go test, Action Research Arm Test, electroencephalography, and magnetic resonance imaging.

CONCLUSION

The results of this trial will provide an in-depth understanding of the efficacy of early interlimb-coordinated intervention on gait and upper functional rehabilitation and how it may relate to the neural plasticity process.

Measurement Method of Human Lower Limb Joint Range of Motion Through Human-Machine Interaction Based on Machine Vision

To provide stroke patients with good rehabilitation training, the rehabilitation robot should ensure that each joint of the limb of the patient does not exceed its joint range of motion. Based on the machine vision combined with an RGB-Depth (RGB-D) camera, a convenient and quick human-machine interaction method to measure the lower limb joint range of motion of the stroke patient is proposed. By analyzing the principle of the RGB-D camera, the transformation relationship between the camera coordinate system and the pixel coordinate system in the image is established. Through the markers on the human body and chair on the rehabilitation robot, an RGB-D camera is used to obtain their image data with relative position. The threshold segmentation method is used to process the image. Through the analysis of the image data with the least square method and the vector product method, the range of motion of the hip joint, knee joint in the sagittal plane, and hip joint in the coronal plane could be obtained. Finally, to verify the effectiveness of the proposed method for measuring the lower limb joint range of motion of human, the mechanical leg joint range of motion from a lower limb rehabilitation robot, which will be measured by the angular transducers and the RGB-D camera, was used as the control group and experiment group for comparison. The angle difference in the sagittal plane measured by the proposed detection method and angle sensor is relatively conservative, and the maximum measurement error is not more than 2.2 degrees. The angle difference in the coronal plane between the angle at the peak obtained by the designed detection system and the angle sensor is not more than 2.65 degrees. This paper provides an important and valuable reference for the future rehabilitation robot to set each joint range of motion limited in the safe workspace of the patient.

Simultaneous Control of Tonic Vibration Reflex and Kinesthetic Illusion for Elbow Joint Motion Toward Novel Robotic Rehabilitation

Robotic rehabilitation is one of the most promising applications of robotic technologies. It is known that patients' active participation in rehabilitation is important for their recovery. On the other hand, mechanical vibration stimulation to muscles induces tonic vibration reflex (TVR) and kinesthetic illusion (KI) in the joint motion. In this paper, the possibility of a novel robotic rehabilitation method, in which the TVR is applied to an agonist muscle to enhance the intended motion of patients and the KI is simultaneously applied to an antagonist muscle to enhance the kinesthetic movement sensation of the generating intended motion by changing the frequency of vibration stimulation, is investigated. As the first step toward novel robotic rehabilitation, the proposed method is evaluated in elbow joint motion. The experimental results show the possibility of the proposed novel rehabilitation method.Clinical Relevance- This study shows the possibility of novel robotic rehabilitation.

Locomotion Synchronization and Gait Performance While Walking With an Overground Body Weight Support System

Rehabilitation robotics offers new alternatives to patients and therapists to efficiently support walking training using Body Weight Support (BWS) systems. Automating the locomotion of overground BWS systems is one of the feasible approaches to free therapists from manual operation. However, the effect of locomotion control strategies of BWS system on participant's gait performance have not been studied sufficiently. For this reason, in this paper we introduced locomotion synchronization between a participant, a therapist, and a BWS system as control criteria, and investigated its effect on participant's gait performance during walking with an overground BWS system. In the experiment, four healthy participants walked with a BWS system under different BWS conditions, and with/without wearing orthosis which simulates asymmetric gait of actual patients. As the result, it was observed a significant relationship between locomotion synchronization and participants' gait performance, such as walking speed and step time.Clinical relevance - Controlling an overground BWS system's locomotion in synchronizing with the participant's gait has the potential to facilitate the effect of gait rehabilitation.

Gait Training after Stroke with a Wearable Robotic Device: A Case Report of Further Improvements in Walking Ability after a Recovery Plateau

Background:

Conventional rehabilitation is known to improve walking ability after stoke, but its effectiveness is often limited. Recent studies have shown that gait training combining conventional rehabilitation and robotic devices in stroke patients provides better results than conventional rehabilitation alone, suggesting that gait training with a robotic device may lead to further improvements in the walking ability recovered by conventional rehabilitation. Therefore, the aim of this report was to highlight the changes in kinematic and electromyographic data recorded during walking before and after gait training with the Honda Walking Assist Device^® (HWAT) in a male patient whose walking speed had reached a recovery plateau under conventional rehabilitation.

Case:

The patient was a 42-year-old man with severe hemiplegia caused by right putaminal hemorrhage. He underwent conventional rehabilitation for 20 weeks following the onset of stroke, after which his walking speed reached a recovery plateau. Subsequently, we added robotic rehabilitation using HWAT to his regular rehabilitation regimen, which resulted in improved step length symmetry and gait endurance. We also noted changes in muscle activity patterns during walking.

Discussion:

HWAT further improved the walking ability of a patient who had recovered with conventional rehabilitation; this improvement was accompanied by changes in muscle activity patterns during walking. The improvement in gait endurance exceeded the smallest meaningful change in stroke patients, suggesting that this improvement represented a noticeable enhancement in the quality of life in relation to mobility in the community. Further clinical trials are needed to confirm the results of the present case study.

Changes in kinesiostabilogram parameters and movement speed of stroke patients while increasing their physical activity due to the use of biofeedback method

Balance disorders are complications of stroke survivors. Aim of this study was the establish effectiveness of the biofeedback approach. In this intervention study 245 patients with early diagnosis of acute disturbance of cerebral circulation (ADCC) were examined. Patients able to move independently were treated by standard conservative ADCC therapy on an outpatient approach, but they continued to have problems with coordination of movement in upright position. Then they were submitted to an increasing physical activity based on five sessions of biofeedback, i.e., a complex rehabilitation of patients with motor pathology "Trust-M" according to TU 9442-001-63704475-2010. Mobility rates were assessed using a web camera. Patients' quality of life was evaluated by SF-36 questionnaire and the Hospital Anxiety and Depression Scale (HADS). All parameters were recorded before and after 5 sessions of biofeedback. After treatment, the stability indicators improved and all patients showed a significant increase in motion rate and quality of life. At the same time, the severity of pain and of depression and anxiety decreased. Negative correlations of average strength between the quadrant and patient HADS scaling rates were obtained. In conclusion, our work shows effectiveness of the biofeedback technique for correcting coordination in stroke survivors.

Targeting Paretic Propulsion and Walking Speed With a Soft Robotic Exosuit: A Consideration-of-Concept Trial

Background: Soft robotic exosuits can facilitate immediate increases in short- and long-distance walking speeds in people with post-stroke hemiparesis. We sought to assess the feasibility and rehabilitative potential of applying propulsion-augmenting exosuits as part of an individualized and progressive training program to retrain faster walking and the underlying propulsive strategy. Methods: A 54-yr old male with chronic hemiparesis completed five daily sessions of Robotic Exosuit Augmented Locomotion (REAL) gait training. REAL training consists of high-intensity, task-specific, and progressively challenging walking practice augmented by a soft robotic exosuit and is designed to facilitate faster walking by way of increased paretic propulsion. Repeated baseline assessments of comfortable walking speed over a 2-year period provided a stable baseline from which the effects of REAL training could be elucidated. Additional outcomes included paretic propulsion, maximum walking speed, and 6-minute walk test distance. Results: Comfortable walking speed was stable at 0.96 m/s prior to training and increased by 0.30 m/s after training. Clinically meaningful increases in maximum walking speed (Δ: 0.30 m/s) and 6-minute walk test distance (Δ: 59 m) were similarly observed. Improvements in paretic peak propulsion (Δ: 2.80 %BW), propulsive power (Δ: 0.41 W/kg), and trailing limb angle (Δ: 6.2 degrees) were observed at comfortable walking speed (p's < 0.05). Likewise, improvements in paretic peak propulsion (Δ: 4.63 %BW) and trailing limb angle (Δ: 4.30 degrees) were observed at maximum walking speed (p's < 0.05). Conclusions: The REAL training program is feasible to implement after stroke and capable of facilitating rapid and meaningful improvements in paretic propulsion, walking speed, and walking distance.

Robotic-assisted gait rehabilitation following stroke: a systematic review of current guidelines and practical clinical recommendations

INTRODUCTION

Stroke is the third leading cause of adult disability worldwide, and lower extremity motor impairment is one of the major determinants of long-term disability. Although robotic therapy is becoming more and more utilized in research protocols for lower limb stroke rehabilitation, the gap between research evidence and its use in clinical practice is still significant. The aim of this study was to determine the scope, quality, and consistency of guidelines for robotic lower limb rehabilitation after stroke, in order to provide clinical recommendations.

EVIDENCE ACQUISITION

We systematically reviewed stroke rehabilitation guideline recommendations between January 1, 2010 and October 31, 2020. We explored electronic databases (N.=4), guideline repositories and professional rehabilitation networks (N.=12). Two independent reviewers used the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, and brief syntheses were used to evaluate and compare the different recommendations, considering only the most recent version.

EVIDENCE SYNTHESIS

From the 1219 papers screened, ten eligible guidelines were identified from seven different regions/countries. Four of the included guidelines focused on stroke management, the other six on stroke rehabilitation. Robotic rehabilitation is generally recommended to improve lower limb motor function, including gait and strength. Unfortunately, there is still no consensus about the timing, frequency, training session duration and the exact characteristics of subjects who could benefit from robotics.

CONCLUSIONS

Our systematic review shows that the introduction of robotic rehabilitation in standard treatment protocols seems to be the future of stroke rehabilitation. However, robot assisted gait training (RAGT) for stroke needs to be improved with new solutions and in clinical practice guidelines, especially in terms of applicability.

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human-machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals-namely, family members and professional carers-to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach

Patients with stroke can experience a drastic change in their body representation (BR), beyond the physical and psychological consequences of stroke itself. Noteworthy, the misperception of BR could affect patients' motor performance even more. Our study aimed at evaluating the usefulness of a robot-aided gait training (RAGT) equipped with augmented visuomotor feedback, expected to target BR (RAGT + VR) in improving lower limb sensorimotor function, gait performance (using Fugl-Meyer Assessment scale for lower extremities, FMA-LE), and BR (using the Body Esteem Scale-BES- and the Body Uneasiness Test-BUT), as compared to RAGT - VR. We also assessed the neurophysiologic basis putatively subtending the BR-based motor function recovery, using EEG recording during RAGT. Forty-five patients with stroke were enrolled in this study and randomized with a 1:2 ratio into either the RAGT + VR (n = 30) or the RAGT - VR (n = 15) group. The former group carried out rehabilitation training with the Lokomat©Pro; whereas, the latter used the Lokomat©Nanos. The rehabilitation protocol consisted of 40 one-hour training sessions. At the end of the training, the RAGT + VR improved in FMA-LE (p < 0.001) and BR (as per BES, (p < 0.001), and BUT, (p < 0.001)) more than the RAGT- did (p < 0.001). These differences in clinical outcomes were paralleled by a greater strengthening of visuomotor connectivity and corticomotor excitability (as detected at the EEG analyses) in the RAGT + VR than in the RAGT - VR (all comparisons p < 0.001), corresponding to an improved motor programming and execution in the former group.We may argue that BR recovery was important concerning functional motor improvement by its integration with the motor control system. This likely occurred through the activation of the Mirror Neuron System secondary to the visuomotor feedback provision, resembling virtual reality. Last, our data further confirm the important role of visuomotor feedback in post-stroke rehabilitation, which can achieve better patient-tailored improvement in functional gait by means of RAGT + VR targeting BR.

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain-computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

A Survey of Robots in Healthcare

In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

[Robotic recovery of walking function in patients in the early recovery period of stroke]

OBJECTIVE

To compare the efficacy of walking function recovery in patients in the early recovery period of ischemic stroke (IS) using an exoskeleton for the lower extremities and an active-passive pedal exercise bike.

MATERIAL AND METHODS

An open randomized study of 47 patients in the early recovery period of IS was conducted. The rehabilitation course included exercises on an ExoAtlet exoskeleton in group 1 and exercises on a pedal simulator for active-passive training (5 days a week for 2 weeks) in group 2. Several tests were used to evaluate treatment results, including the Hauser walking index, the 10-meter walking test, the Berg balance scale, stabilometry, and biomechanics of walking. The complete training course was completed by 20 patients of group 1 and 21 of group 2.

RESULTS

There was a significant increase in strength in paretic muscles, postural stability, functional level and walking speed in patients of both groups, but in patients of group 1, the dynamics of recovery was more pronounced (p<0.05). In group 1, there was a significant decrease in the level of disability and an increase in daily activity, which was higher compared to group 2. An analysis of the main indicators of the statokinesiogram showed the more pronounced positive shifts in patients of group 1, but significant differences were found only in the dynamics of the length and area of the curve in the test with eyes open. When studying the biomechanics of walking, it was found that the function of walking was changed: there was a significant decrease in the speed of movement by 2.2 times, the length of a double step by 1.6 times, and the pace of walking by 1.3 times compared to normal indicators. After the end of exercises, a significant increase in the length of the double step, speed and pace of walking as well as a decrease in the period of the locomotor cycle were found in group 1.

CONCLUSION

The study revealed a positive impact of hardware rehabilitation on locomotion, both with the use of an exoskeleton and an active-passive pedal simulator. The use of an exoskeleton, have the advantages resulting in a significantly greater recovery of strength, stability, speed and symmetry of walking over the same period of training. A significant increase in postural stability in vertical position was revealed.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

Processing Surface EMG Signals for Exoskeleton Motion Control

The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.