Robot-assisted Gait Rehabilitation Research Articles

Human-Gait-Based Tracking Control for Lower Limb Exoskeleton Robot

Research shows that it is practical for the normal human movement mechanism to assist the patients with stroke in robot-assisted gait rehabilitation. In passive training, the effect of rehabilitation training for patients can be improved by imitating normal human walking. To make the lower limb exoskeleton robot (LLER) move like a normal human, a tracking control scheme based on human gait data is proposed in this paper. The real human gait data is obtained from healthy subjects using a three-dimensional motion capture platform (3DMCP). Furthermore, the normal human motion characteristics are adopted to enhance the scientificity and effectiveness of assistant rehabilitation training using LLER. An adaptive radial basis function network (ARBFN) controller based on feed-forward control is presented to improve the trajectory tracking accuracy and tracking performance of the control system, where the ARBFN controller is deployed to predict the uncertain model parameters. The feed-forward controller based on the tracking errors is used to compensate for the input torque of LLER. The effectiveness of the presented control scheme is confirmed by simulation results based on experimental data.

Real-time motion onset recognition for robot-assisted gait rehabilitation

BackgroundMany patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users’ movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8 classes in total).MethodsTo predict the onsets of the gait-related tasks, three Inertial Measurement Units (IMUs) were attached to the sternum and middle of outer thighs of 19 controls without neurological movement disorders and two individuals with incomplete Spinal Cord Injury (iSCI). The data of IMUs obtained from different gait tasks was sent synchronously to a real-time data acquisition system through a custom-made Bluetooth-EtherCAT gateway. In the first step, data was applied offline for training five different classifiers. The best classifier was chosen based on F1-score results of a Leave-One-Participant-Out Cross-Validation (LOPOCV), which is an unbiased way of testing. In a final step, the chosen classifier was tested in real time with an additional control participant to demonstrate feasibility for real-time classification.ResultsTesting five different classifiers, the best performance was obtained in a single-layer neural network with 25 neurons. The F1-score of 86.83% pm 6.2% and 92.01% are achieved on testing using LOPOCV and test data (30%, participants = 20), respectively. Furthermore, the results from the implemented real-time classifier were compared with the offline classifier and revealed nearly identical performance (difference = 0.08 %).ConclusionsA neural network classifier was trained for identifying the onset of gait-related tasks in real time. Test data showed convincing performance for offline and real-time classification. This demonstrates the feasibility and potential for implementing real-time onset recognition in rehabilitation devices in future.

Efficacy of robotic exoskeleton for gait rehabilitation in patients with subacute stroke : a systematic review.

Stroke is the most common cause of disability in Western Countries. It can lead to loss of mobility, capability to walk and ultimately loss of independence in activities of daily living (ADL). Several rehabilitative approaches have been proposed in these years. Robot-assisted gait rehabilitation (RAGT) plays a crucial role to perform a repetitive, intensive, and task-oriented treatment in stroke survivors. However, there are still few data on its role in subacute stroke patients. The aim of the present study was to assess the efficacy of RAGT for gait recovery in subacute stroke survivors. Systematic review with meta-analysis. The setting of the study included Units of Rehabilitation. The analyzed population was represented by subacute stroke patients. PubMed, Scopus, Web of Science, CENTRAL, and PEDro were systematically searched until January 18, 2021, to identify randomized controlled trials (RCTs) presenting: stroke survivors in subacute phase (≤6 months) as participants; exoskeleton robots devices as intervention; conventional rehabilitation as a comparator; gait assessment, through qualitative scales, quantitative gait scales or quantitative parameters, as outcome measures. We also performed a meta-analysis of the mean difference in the functional ambulation category (FAC) via the random effect method. Out of 3188 records, 14 RCTs were analyzed in this systematic review. The 14 studies have been published in the last 14 years (from 2006 to 2021) and included 576 stroke survivors, of which 306 received RAGT, and 270 underwent conventional rehabilitation. Lokomat robotic system was the most investigated robotic exoskeleton by the RCTs included (N.=9), albeit the meta-analysis demonstrated a non-significant difference of -0.09 in FAC (95% CI: -0.22.0.03) between Lokomat and conventional therapy. According to the PEDro scale, 11 (78.5%) were classified as good-quality studies, two as fair-quality studies (14.3%), and one as poor-quality study (7.1%). Taken together, these findings showed that RAGT might have a potential role in gait recovery in subacute stroke survivors. However, further RCTs comparing the efficacy of RAGT with conventional physical therapy are still warranted in the neurorehabilitation field. This systematic review provides information on the efficacy of RAGT in allowing subacute stroke patients to perform high-intensity gait training with a lower physical burden on PRM professionals.

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

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. 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. 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. 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.

Effects of Assisted Dorsiflexion Timing on Voluntary Efforts and Compensatory Movements: A Feasibility Study in Healthy Participants.

In previous research, we found that modulating the assistance timing of dorsiflexion may affect a user's voluntary efforts. This could constitute a focus area based on assistive strategies that could be developed to foster patients' voluntary efforts. In this present study, we conducted an experiment to verify the effects of ankle dorsiflexion assistance under different timings using a high-dorsiflexion assistive system. Nine healthy and young participants wore a dorsiflexion-restrictive device that enabled them to use circumduction or steppage gaits. On the basis of the transition from the stance to the swing phase of the gait, the assistance timings of the high-dorsiflexion assistive system were set to have delays, which ranged from 0 to 300 ms. The index results from eight out of nine participants evaluated compensatory movements and revealed positive strong/moderate correlations with assistance delay times (r = 0.627-0.965, p <.001), whereas the other participants also performed compensatory movement when dorsiflexion assistance timing was late. Meanwhile, the results from tibialis anterior surface electromyography from six out of nine participants showed positive strong/moderate correlations with dorsiflexion assistance delay times (r = 0.598-0.922, p <.001), indicating that tuning the assistance timing did foster these participants' voluntary dorsiflexion movements. This result indicates that there should be a trade-off between ensuring voluntary dorsiflexion movements and preventing incorrect gait patterns at different assistance timings. The findings of this feasibility study indicate the potential of developing an adaptive control method to ensure voluntary efforts during robot-assisted gait rehabilitation based on assistance timing modification. A new assistance mechanism should also be required to stimulate and motivate a patient's voluntary efforts and should reinforce the effects of active gait rehabilitation.

Current State and Future Prospects of EEG and fNIRS in Robot-Assisted Gait Rehabilitation: A Brief Review.

Gait and balance impairments are frequently considered as the most significant concerns among individuals suffering from neurological diseases. Robot-assisted gait training (RAGT) has shown to be a promising neurorehabilitation intervention to improve gait recovery in patients following stroke or brain injury by potentially initiating neuroplastic changes. However, the neurophysiological processes underlying gait recovery through RAGT remain poorly understood. As non-invasive, portable neuroimaging techniques, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide new insights regarding the neurophysiological processes occurring during RAGT by measuring different perspectives of brain activity. Due to spatial information about changes in cortical activation patterns and the rapid temporal resolution of bioelectrical changes, more features correlated with brain activation and connectivity can be identified when using fused EEG-fNIRS, thus leading to a detailed understanding of neurophysiological mechanisms underlying motor behavior and impairments due to neurological diseases. Therefore, multi-modal integrations of EEG-fNIRS appear promising for the characterization of neurovascular coupling in brain network dynamics induced by RAGT. In this brief review, we surveyed neuroimaging studies focusing specifically on robotic gait rehabilitation. While previous studies have examined either EEG or fNIRS with respect to RAGT, a multi-modal integration of both approaches is lacking. Based on comparable studies using fused EEG-fNIRS integrations either for guiding non-invasive brain stimulation or as part of brain-machine interface paradigms, the potential of this methodologically combined approach in RAGT is discussed. Future research directions and perspectives for targeted, individualized gait recovery that optimize the outcome and efficiency of RAGT in neurorehabilitation were further derived.

THE DEVELOPMENT OF AN EMG CONTROLLER-BASED ROBOTIC GAIT TRAINING SYSTEM AND ITS CLINICAL FEASIBILITY FOR SUBACUTE STROKE PATIENTS IN IMPROVING LOCOMOTIVE FUNCTION

One-third of stroke survivors fail to regain independent ambulation and strokes have been identified as a significant source of long-term disability and a tremendous health burden. Robot-assisted gait rehabilitation is gaining traction and advocators for its inclusion as part of the routine post-stroke rehabilitation program are on the increase. However, despite the recent technological advances in the development and design of better robotics, the research evidence on the best model of robotic training remains sparse and unclear. It is therefore the aim of the current study to comparatively investigate the clinical feasibility and efficacy of a recently developed HIWIN Robotic Gait Training System (MRG-P100) combined with the use of a lab-developed MBS-E100 EMG system as a controller on facilitating the development of an appropriate gait pattern for motor impaired subacute stroke patients. The results indicated that due to the heterogeneity of stroke-induced changes in muscle characteristics, an “auto-fit” algorithm was required to allow constant monitoring and updating of the appropriate threshold based on EMG signals captured during previous gait cycle in order to determine the desired muscle activation threshold for the current gait cycle. Eighteen participants were tested using the new auto-fit algorithm and results demonstrated a significantly more fluent and physiologically appropriate gait pattern.

"Pathological Gait Abnormality Detection and Segmentation by Processing the Hip Joints Motion Data to Support Mobile Gait Rehabilitation"

An accurate detection of the gait sub-phases is fundamental in clinical gait analysis to interpret kinetic and kinematic data. In general, detecting the gait events that mark the transition from one gait sub-phase to another as well as the sequence of sub-phases is essential to evaluate gait abnormalities. However, finding a reliable segmentation for pathological gait has been a challenging task. This manuscript entails a generic approach for the gait segmentation into sub-phases in the CORBYS1 system. A number of distinctive features are extracted from the Hip joints motion data which are able to partition and segment the gait cycles in an efficient way. The degree of deviation (i.e. anomaly) in each sub-phase is then calculated with respect to an optimal gait reference which is used for robot-assisted gait rehabilitation. The proposed gait segmentation method is applicable to gait with many types of pathology since training on the pathology specific templates is not required. Performance of the proposed algorithm is evaluated by statistical analysis of results which produced 100% gait segmentation accuracy for healthy subjects and over 99% for pathological subjects.

Models for temporal-spatial parameters in walking with cadence ratio as the independent variable

Accurate models that describe temporal-spatial parameters are desirable in gait estimation and rehabilitation. This study aimed to explore simple but relatively accurate models to describe stride length (SL), speed (SP) and walk ratio (WR) at various cadences. Twenty-four able-bodied participants (16 in a test group and 8 in a validation group) walked at seven cadence ratios (CRs). The individual and group mean SL, SP and WR were studied. Suitable temporal-spatial model structures were proposed and used to approximate the individual SL, SP and WR at various CRs. After the temporal-spatial model structures were found to be feasible, the general temporal-spatial models were analysed using the test group mean SL, SP and WR. Accuracy was assessed using the validation group mean values. Individual approximation accuracies showed that the proposed model structure deduced from the linear SL model was suitable for WR approximation. The linear, deduced quadratic and power functions approximated the individual SL, SP and WR, respectively, with high accuracy. Based on the test group mean SL, SP and WR, the general temporal-spatial models were obtained and produced comparable approximation accuracies in the validation group. The general temporal-spatial models predicted well the individual gait parameters with similar individual errors for both groups. These temporal-spatial models clearly describe SL, SP and especially WR at various cadences. They provide accurate reference data for gait estimation and have potential to guide speed modulation in robot-assisted gait rehabilitation.Graphical abstractTwenty-four able-bodied participants (16 in test group and 8 in validation group) walked at seven cadence ratios (CRs), with the individual and group mean stride length (SL), speed (SP) and walk ratio (WR) studied. This work selected the cadence ratio as the independent variable and yielded general temporal-spatial models based on the test group data, which were a linear model for SL, a quadratic function for SP and a power function for WR. The general temporal-spatial model produced comparable approximation accuracies in the validation group. Clearly describing SL, SP and especially WR at various cadences, these temporal-spatial models provide accurate references for gait estimation and have the potential to guide speed modulation in robot-assisted gait rehabilitation. Approximation of the group mean temporal-spatial parameters at seven cadences. Solid lines in parts (a, b): the general linear SL model. Solid lines in (c, d): the general quadratic SP model. Solid lines in (e, f): the general WR model. Dots and stars in (a, c, e): the individual and group mean values for the test group. Dots and stars in (b, d, f): the individual and group mean values for the validation group.

Strategy of Robot-assisted Gait Rehabilitation Treatment

Strategy of Robot-assisted Gait Rehabilitation Treatment

ROBO-K, robot-assisted gait rehabilitation: A user focused collaborative project

Robots-assisted gait rehabilitation are used in association with conventional rehabilitation but it's necessary to improve their functionality. This paper presents a psychosocial intervention framework to support the development of a mobile robot for gait training: ROBO-K. The aim was to facilitate the integration of the robot in the walking rehabilitative process and thus, ensure its diffusion. The representation of the robot's use and its evolutions (i.e., the usual trajectory) has been first studied. Then two multicenter studies were performed in 2013 and 2015. In the first one, patients ( n = 34) and practitioners ( n = 74) were interviewed to define acceptable functional specifications (i.e., social acceptability). After this first study, a prototype was developed in collaboration with an industrial partner. In the second one, patients ( n = 36) and physiotherapists ( n = 4) tested the co designed prototype in ecological situation during 1 to 6 months (i.e., acceptance and appropriation). The results showed that the robot is perceived positively by the patients and the physiotherapists because it's perceived as useful to improve balance and gait and provide safety and motivation. This perception persisted along the experimental procedure. For the physiotherapists, the robot helped to better manage their inner physical effort. In the other hand, the mental load was high. The results seemed also very encouraging in terms of clinical effectiveness. For example, the FAC (Functional Ambulation Classifications) increases by 40% on average. Finally, these psychosocial approach with industrial, clinical researchers collaboration is an excellent pathway for innovation conception.

A Poincare map based analysis of stroke patients’ walking after a rehabilitation by a robot

Since the past decade, rehabilitation robots have become common technologies for recovering gait ability after a stroke. Nevertheless, it is believed that these robots can be further enhanced. Hence, several researches are making progress in optimizing gait rehabilitation robots. However, most of these researches have only assessed the robots and their controllers in improving spatiotemporal and kinetic features of walking. There are not many researchers have focused on the robots’ controllers’ effects on the central nervous or neuromuscular systems. On the other hand, recently computational methods have been utilized to investigate the rehabilitations of neural disorders, through developing neuromechanical models. However, these methods have neither studied the robot-assisted gait rehabilitation, nor have they theoretically proved why rehabilitation exercises enhance patients’ walking ability. Therefore, this paper merged a theoretical approach into a computational method to investigate the effects of gait rehabilitation robots on post-stroke neuromuscular system. To this end, a neuromechanical model of gait has been developed and thereby, the Poincare maps of intact and stroke people have been obtained. Comparison of these maps revealed why a stroke reduces the stability of walking. Then, the effect of an impedance controller, which is used in a rehabilitative robot, is scrutinized in stabilizing a walking motion. Obtaining the Poincare map of this close-loop system, proved that this controller improves motion stability. Finally, the effect of this controller is investigated by simulations and experiments. The experimental tests are performed by Arman rehabilitative robot. Clinical Reference Number: IR.TMU.REC.1394.254.

Effect of robotic-assisted gait rehabilitation on dynamic equilibrium control in the gait of children with cerebral palsy

Due to the intensity and repetition of movement, roboticassisted gait training therapy could have a beneficial effect on the recovery and improvement of postural and locomotor functions of the patient. This study sought to highlight the effects of robotic-assisted gait rehabilitation in gait of children with Cerebral Palsy (CP). We analyzed the different strategies before and after this rehabilitation which was used in order to generate forward motion while maintaining balance. Data were collected by a motion analysis system (Vicon® - Oxford Metrics, Oxford, UK). The children were divided into two groups in such a way as to obtain a randomized controlled population: i) a group of fourteen children (Treated Group) underwent 20 sessions of roboticassisted gait training therapy using the driven gait orthosis Lokomat®Pediatric (Hocoma AG, Volketswil, Switzerland) compared to ii) a group of sixteen children without sessions of Lokomat®Pediatric (Control Group). Significant differences are observed for the TG between the preand post-test values of the locomotor parameters and of the kinetic data of the propulsive forces of the Center of Mass (COM) and of the Center of Pressure (COP) dynamic trajectory. This first study, although performed on a limited number of patients, shows the usefulness of this robotic gait rehabilitation mainly in the balance control in gait. Indeed after this rehabilitation, these children improve their gait that is especially characterized by a more appropriate time lag between the time instant of COM-COP trajectory divergence and the time instant when the forward propulsive forces became apparent.

Assist-as-Needed Control of an Intrinsically Compliant Robotic Gait Training Orthosis

It is a common hypothesis in the field of robot-assisted gait rehabilitation that the active involvement and voluntary participation of neurologically impaired subjects in the robotic gait training process may enhance the outcomes of such therapy. An adaptive seamless assist-as-needed (AAN) control scheme was developed for the robotic gait training. The AAN control scheme learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. The overall AAN control architecture works on the basis of a robust adaptive control approach. The performance of seamless AAN control scheme was evaluated during treadmill training with a compliant robotic orthosis having six degrees of freedom. Two experiments, namely, trajectory following experiment and the AAN experiment were carried out to evaluate the performance of seamless adaptive AAN control scheme. It was found that the robotic orthosis is capable of guiding the subjects’ limbs on reference trajectories during the trajectory following experiment. Also, a variation in robotic assistance was recorded during the AAN experiment based on the voluntary participation of human subjects. This work is an advance on the current state of the art in the compliant actuation of robotic gait rehabilitation orthoses in the context of seamless AAN gait training.

Unilateral changes in walking surface compliance evoke dorsiflexion in paretic leg of impaired walkers.

IntroductionGait impairments due to stroke impact millions of individuals throughout the world. Despite the growing interest in automating gait therapy with robotic devices, there is no clear evidence that robot-assisted gait therapy is superior to traditional treadmill-based therapy.MethodsThis work investigates the effect of perturbations to the compliance of the walking surface on the paretic leg of impaired walkers. Using a novel robotic device, the variable stiffness treadmill, we apply perturbations to the compliance of the walking surface underneath the non-paretic leg of two hemi-paretic walkers and analyze the kinematic and neuromuscular response of the contralateral (paretic) leg with motion capture and surface electromyography systems.ResultsWe present results of evoked muscle activity (predominately tibialis anterior) and increased dorsiflexion in the paretic leg during the swing phase of gait at stiffness values of 60 kN/m and less for all subjects.ConclusionsThis work provides evidence for the first time of reducing the drop-foot effect in the impaired leg of hemiparetic walkers in response to unilateral perturbations to the compliance of the treadmill platform, thus providing direction for targeted robot-assisted gait rehabilitation.

Design, Analysis, and Optimization of an Acute Stroke Gait Rehabilitation Device

Stroke is one of the leading causes of adult physical disability, and rehabilitation and hospitalization costs for stroke are among the highest for all injuries. Current rehabilitation techniques are labor intensive and time consuming for therapists and difficult to perform effectively. Research suggests that starting rehabilitation during the acute or subacute stage of recovery results in better outcomes than therapy delivered in the chronic stage. To improve the gait rehabilitation process, robot-assisted gait rehabilitation has gained much interest over the past years. However, many robot-assisted rehabilitation devices have limitations; one of which is being bulky and complex to handle. Large and expensive devices that require special training to operate are less attractive to clinics and therapists, and ultimately less likely to be available to patients especially at the early stage of stroke. To address these limitations, this research proposes a new gait rehabilitation device called the linkage design gait trainer (LGT). The device is based on a walking frame design with a simple four-bar linkage “end-effector” mechanism to generate normal gait trajectories during general walking and exercise. The design of the four-bar linkage mechanism was optimized for a particular gait pattern. A prototype of the device was developed and tested. The kinematics of the device itself and gait kinematics with and without assistance from the device were recorded and analyzed using an optical motion capture system. The results show the linkage mechanism is able to guide the leg of the user during over ground walking. There were some differences in the hip (20.5 deg RMS) and knee (14.8 deg RMS) trajectory between the person walking with and without the device assistance. The study demonstrated the concept and feasibility of this novel gait training device.

The motivation, expectations and ease-of-use of robot-assisted gait rehabilitation in stroke patients and their therapists

The motivation, expectations and ease-of-use of robot-assisted gait rehabilitation in stroke patients and their therapists

Psychological Effects of Robot-Assisted Gait Rehabilitation According to Degree of Paralysis in SCI

Psychological Effects of Robot-Assisted Gait Rehabilitation According to Degree of Paralysis in SCI

Update on rehabilitation in multiple sclerosis

Given that mobility impairment is a hallmark of multiple sclerosis, people with this disease are likely to benefit from rehabilitation therapy throughout the course of their illness. The review provides an update on rehabilitation focused on balance and walking impairment. Classical rehabilitation focusing on muscle rehabilitation, neurotherapeutic facilitation is effective and recommended. Other techniques did not prove their superiority: transcutaneal neurostimulation, repetitive magnetic stimulation, electromagnetic therapy, whole body vibration and robot-assisted gait rehabilitation and need more studies to conclude. Cooling therapy, hydrotherapy, orthoses and textured insoles could represent a complementary service to other techniques in specific conditions. Multidisciplinary rehabilitation program provides positive effects and high satisfaction for patients with multiple sclerosis but needs more evaluation. New technologies using serious game and telerehabilitation seem to be an interesting technique to promote physical activity, self-management and quality of life. Rehabilitation like other therapy needs regular clinical evaluation to adapt the program and propose appropriate techniques. Moreover, the objective of rehabilitation needs to be decided with the patient with realistic expectation.

Body weight support during robot-assisted walking: influence on the trunk and pelvis kinematics.

Efficacy studies concerning robot assisted gait rehabilitation showed limited clinical benefits. A changed kinematic pattern might be responsible for this. Little is known about the kinematics of the trunk and pelvis during robot assisted treadmill walking (RATW). The aim of this study was to assess the trunk and pelvis kinematics of healthy subjects during RATW, with different amounts of body weight support (BWS) compared to regular treadmill walking (TW). Eighteen healthy participants walked on a treadmill, while kinematics were registered by an electromagnetic tracking device. Hereafter, the kinematics of pelvis and trunk were registered during RATW (guidance force 30%) with 0%, 30% and 50% BWS. Compared to TW, RATW showed a decrease in the following trunk movements: axial rotation, anteroposterior flexion, lateral and anteroposterior translation. Besides, a decrease in lateral tilting and all translation of the pelvis was found when comparing RATW with TW. Furthermore, the anteroposterior tilting of the pelvis increased during RATW. In general, there was a decrease in trunk and pelvis movement amplitude during RATW compared with regular TW. Though, it is not known if these changes are responsible for the limited efficacy of robot assisted gait rehabilitation. Further research is indicated.