Comparative Effects of Different Assistance Force During Robot-Assisted Gait Training on Locomotor Functions in Patients With Subacute Stroke: An Assessor-Blind, Randomized Controlled Trial

Effect of Robotic-Assisted Gait at Different Levels of Guidance and Body Weight Support on Lower Limb Joint Kinematics and Coordination

The Lokomat provides task-oriented therapy for patients with gait disorders. This robotic technology drives the lower limbs in the sagittal plane. However, normative gait also involves motions in the coronal and transverse planes. This study aimed to compare the Lokomat with Treadmill gait through three-dimensional (3D)-joint kinematics and inter-joint coordination. Lower limb kinematics was recorded in 18 healthy participants who walked at 3 km/h on a Treadmill or in a Lokomat with nine combinations of Guidance (30%, 50%, 70%) and bodyweight support (30%, 50%, 70%). Compared to the Treadmill, the Lokomat altered pelvic rotation, decreased pelvis obliquity and hip adduction, and increased ankle rotation. Moreover, the Lokomat resulted in significantly slower velocity at the hip, knee, and ankle flexion compared to the treadmill condition. Moderate to strong correlations were observed between the Treadmill and Lokomat conditions in terms of inter-joint coordination between hip-knee (r = 0.67-0.91), hip-ankle (r = 0.66-0.85), and knee-ankle (r = 0.90-0.95). This study showed that some gait determinants, such as pelvis obliquity, rotation, and hip adduction, are altered when walking with Lokomat in comparison to a Treadmill. Kinematic deviations induced by the Lokomat were most prominent at high levels of bodyweight support. Interestingly, different levels of Guidance did not affect gait kinematics. The present results can help therapists to adequately select settings during Lokomat therapy.

Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury

BACKGROUND

Walking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limitations. Robot-assisted locomotor training is becoming an established clinical practice. Besides training, these devices could be used for assessing walking ability in a controlled environment. Here, we propose an adaptive assist-as-needed (AAN) control for a treadmill-based robotic exoskeleton, the Lokomat, that reduces the support of the device (body weight support and impedance of the robotic joints) based on the ability of the patient to follow a gait pattern displayed on screen. We hypothesize that the converged values of robotic support provide valid and reliable information about individuals' walking ability.

METHODS

Fifteen participants with spinal cord injury and twelve controls used the AAN software in the Lokomat twice within a week and were assessed using clinical scores (10MWT, TUG). We used a regression method to identify the robotic measure that could provide the most relevant information about walking ability and determined the test-retest reliability. We also checked whether this result could be extrapolated to non-ambulatory and to unimpaired subjects.

RESULTS

The AAN controller could be used in patients with different injury severity levels. A linear model based on one variable (robotic knee stiffness at terminal swing) could explain 74% of the variance in the 10MWT and 61% in the TUG in ambulatory patients and showed good relative reliability but poor absolute reliability. Adding the variable 'maximum hip flexor torque' to the model increased the explained variance above 85%. This did not extend to non-ambulatory nor to able-bodied individuals, where variables related to stance phase and to push-off phase seem more relevant.

CONCLUSIONS

The novel AAN software for the Lokomat can be used to quantify the support required by a patient while performing robotic gait training. The adaptive software might enable more challenging training conditions tuned to the ability of the individuals. While the current implementation is not ready for assessment in clinical practice, we could demonstrate that this approach is safe, and it could be integrated as assist-as-needed training, rather than as assessment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02425332.

Bolstering Cognitive and Locomotor Function in Post-Stroke Dementia Using Human-Robotic Interactive Gait Training

Studies have reported inconclusive results regarding the effectiveness and clinical indications of the exclusive use of human-robotic interactive gait training (HIT) in patients with post-stroke dementia (PSD). This study aimed to compare the effects of human-robotic interactive gait training (HIT) and conventional physiotherapy (CPT) on cognitive and sensorimotor functions, trunk balance and coordination, dynamic and static balance, and activities related to daily living performance in patients with PSD. Forty-eight patients with PSD who received 60-minute therapy sessions three times per week for 6 weeks were assigned to either the CPT (n = 25) or HIT (n = 23) group. The clinical outcomes included the scores of the mini-mental state examination (MMSE), Fugl-Meyer assessment (FMA), trunk impairment scale (TIS), Berg balance scale (BBS), and modified Barthel index (MBI). Friedman tests were conducted at p < 0.05. The Friedman tests showed that HIT had superior effects to CPT in relation to MMSE, FMA, and TIS (p < 0.05), but not in relation to BBS and MBI (p > 0.05). Our results provide promising clinical evidence that HIT significantly improves cognitive and sensorimotor recovery functions, as well as trunk balance and coordination, in patients with PSD who cannot concurrently perform dual cognitive-locomotor tasks.

Adapted Assistance and Resistance Training With a Knee Exoskeleton After Stroke

Studies on robotic interventions for gait rehabilitation after stroke require: (i) rigorous performance evidence; (ii) systematic procedures to tune the control parameters; and (iii) combination of control modes. In this study, we investigated how stroke individuals responded to training for two weeks with a knee exoskeleton (ABLE-KS) using both Assistance and Resistance training modes together with auditory feedback to train peak knee flexion angle. During the training, the torque provided by the ABLE-KS and the biofeedback were systematically adapted based on the subject's performance and perceived exertion level. We carried out a comprehensive experimental analysis that evaluated a wide range of biomechanical metrics, together with usability and users' perception metrics. We found significant improvements in peak knee flexion ( p = 0.0016 ), minimum knee angle during stance ( p = 0.0053 ), paretic single support time ( p = 0.0087 ) and gait endurance ( p = 0.022 ) when walking without the exoskeleton after the two weeks of training. Participants significantly ( ) improved the knee angle during the stance and swing phases when walking with the exoskeleton powered in the high Assistance mode in comparison to the No Exo and the Unpowered conditions. No clinically relevant differences were found between Assistance and Resistance training sessions. Participants improved their performance with the exoskeleton (24-55 %) for the peak knee flexion angle throughout the training sessions. Moreover, participants showed a high level of acceptability of the ABLE-KS (QUEST 2.0 score: 4.5 ± 0.3 out of 5). Our preliminary findings suggest that the proposed training approach can produce similar or larger improvements in post-stroke individuals than other studies with knee exoskeletons that used higher training intensities.

Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study

Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user's performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS). Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957). Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70-0.80; MAE = 0.48-0.58 Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7 m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00-0.19; MAE = 1.15-1.29 Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter. Discussion: Our preliminary results indicate that the stride-by-stride estimations of shank-worn IMUs show potential to design automatic and adaptive exoskeleton control strategies for people with moderate impairments in gait function due to stroke.

Evidence-based improvement of gait in post-stroke patients following robot-assisted training: A systematic review

BACKGROUND

The recovery of walking after stroke is a priority goal for recovering autonomy. In the last years robotic systems employed for Robotic Assisted Gait Training (RAGT) were developed. However, literature and clinical practice did not offer standardized RAGT protocol or pattern of evaluation scales.

OBJECTIVE

This systematic review aimed to summarize the available evidence on the use of RAGT in post-stroke, following the CICERONE Consensus indications.

METHODS

The literature search was conducted on PubMed, Cochrane Library and PEDro, including studies with the following criteria: 1) adult post-stroke survivors with gait disability in acute/subacute/chronic phase; 2) RAGT as intervention; 3) any comparators; 4) outcome regarding impairment, activity, and participation; 5) both primary studies and reviews.

RESULTS

Sixty-one articles were selected. Data about characteristics of patients, level of disability, robotic devices used, RAGT protocols, outcome measures, and level of evidence were extracted.

CONCLUSION

It is possible to identify robotic devices that are more suitable for specific phase disease and level of disability, but we identified significant variability in dose and protocols. RAGT as an add-on treatment seemed to be prevalent. Further studies are needed to investigate the outcomes achieved as a function of RAGT doses delivered.

Hybrid robot-assisted gait training for motor function in subacute stroke: a single-blind randomized controlled trial

Background

Robot-assisted gait training (RAGT) is a practical treatment that can complement conventional rehabilitation by providing high-intensity repetitive training for patients with stroke. RAGT systems are usually either of the end-effector or exoskeleton types. We developed a novel hybrid RAGT system that leverages the advantages of both types.

Objective

This single-blind randomized controlled trial evaluated the beneficial effects of the novel RAGT system both immediately after the intervention and at the 3-month follow-up in nonambulatory patients with subacute stroke.

Methods

We recruited 40 patients with subacute stroke who were equally randomized to receive conventional rehabilitation either alone or with the addition of 15 RAGT sessions. We assessed lower-extremity motor function, balance, and gait performance by using the following tools: active range of motion (AROM), manual muscle test (MMT), the Fugl–Meyer Assessment (FMA) lower-extremity subscale (FMA-LE) and total (FMA-total), Postural Assessment Scale for Stroke (PASS), Berg Balance Scale (BBS), Tinetti Performance-Oriented Mobility Assessment (POMA) balance and gait subscores, and the 3-m and 6-m walking speed and Timed Up and Go (TUG) tests. These measurements were performed before and after the intervention and at the 3-month follow-up.

Results

Both groups demonstrated significant within-group changes in the AROM, MMT, FMA-LE, FMA-total, PASS, BBS, POMA, TUG, and 3-m and 6-m walking speed tests before and after intervention and at the 3-month follow-up (p < 0.05). The RAGT group significantly outperformed the control group only in the FMA-LE (p = 0.014) and total (p = 0.002) assessments.

Conclusion

Although the novel hybrid RAGT is effective, strong evidence supporting its clinical effectiveness relative to controls in those with substantial leg dysfunction after stroke remains elusive.

Trial registration The study was registered with an International Standard Randomized Controlled Trial Number, ISRCTN, ISRCTN15088682. Registered retrospectively on September 16, 2016, at https://www.isrctn.com/ISRCTN15088682

Optimal Intervention Timing for Robotic-Assisted Gait Training in Hemiplegic Stroke

This study was designed to determine the best intervention time (acute, subacute, and chronic stages) for Walkbot robot-assisted gait training (RAGT) rehabilitation to improve clinical outcomes, including sensorimotor function, balance, cognition, and activities of daily living, in hemiparetic stroke patients. Thirty-six stroke survivors (acute stage group (ASG), n = 11; subacute stage group (SSG), n = 15; chronic stage group (CSG), n = 10) consistently received Walkbot RAGT for 30 min/session, thrice a week, for 4 weeks. Six clinical outcome variables, including the Fugl–Meyer Assessment (FMA), Berg Balance Scale (BBS), Trunk Impairment Scale (TIS), Modified Barthel Index (MBI), Modified Ashworth Scale (MAS), and Mini-Mental State Examination, were examined before and after the intervention. Significant differences in the FMA, BBS, TIS, and MBI were observed between the ASG and the SSG or CSG. A significant time effect was observed for all variables, except for the MAS, in the ASG and SSG, whereas significant time effects were noted for the FMA, BBS, and TIS in the CSG. Overall, Walkbot RAGT was more favorable for acute stroke patients than for those with subacute or chronic stroke. This provides the first clinical evidence for the optimal intervention timing for RAGT in stroke.

Settings matter: a scoping review on parameters in robot-assisted gait therapy identifies the importance of reporting standards

BACKGROUND

Lokomat therapy for gait rehabilitation has become increasingly popular. Most evidence suggests that Lokomat therapy is equally effective as but not superior to standard therapy approaches. One reason might be that the Lokomat parameters to personalize therapy, such as gait speed, body weight support and Guidance Force, are not optimally used. However, there is little evidence available about the influence of Lokomat parameters on the effectiveness of the therapy. Nevertheless, an appropriate reporting of the applied therapy parameters is key to the successful clinical transfer of study results. The aim of this scoping review was therefore to evaluate how the currently available clinical studies report Lokomat parameter settings and map the current literature on Lokomat therapy parameters.

METHODS AND RESULTS

A systematic literature search was performed in three databases: Pubmed, Scopus and Embase. All primary research articles performing therapy with the Lokomat in neurologic populations in English or German were included. The quality of reporting of all clinical studies was assessed with a framework developed for this particular purpose. We identified 208 studies investigating Lokomat therapy in patients with neurologic diseases. The reporting quality was generally poor. Less than a third of the studies indicate which parameter settings have been applied. The usability of the reporting for a clinical transfer of promising results is therefore limited.

CONCLUSION

Although the currently available evidence on Lokomat parameters suggests that therapy parameters might have an influence on the effectiveness, there is currently not enough evidence available to provide detailed recommendations. Nevertheless, clinicians should pay close attention to the reported therapy parameters when translating research findings to their own clinical practice. To this end, we propose that the quality of reporting should be improved and we provide a reporting framework for authors as a quality control before submitting a Lokomat-related article.

Stage 2: Who Are the Best Candidates for Robotic Gait Training Rehabilitation in Hemiparetic Stroke?

We aimed to compare the effects of robotic-assisted gait training (RAGT) in patients with FAC < 2 (low initial functional ambulation category [LFAC]) and FAC ≥ 2 (high initial functional ambulation category [HFAC]) on sensorimotor and spasticity, balance and trunk stability, the number of steps and walking distance in subacute hemiparetic stroke. Fifty-seven patients with subacute hemiparetic stroke (mean age, 63.86 ± 12.72 years; 23 women) were assigned to two groups. All patients received a 30-min Walkbot-assisted gait training session, 3 times/week, for 6 weeks. Clinical outcomes included scores obtained on the Fugl-Meyer Assessment (FMA) scale, Modified Ashworth Scale (MAS), Berg Balance Scale (BBS), trunk impairment scale (TIS), and the number of walking steps and walking distance. Analysis of covariance and analysis of variance were conducted at p < 0.05. Significant main effects of time in both groups on number of walking steps and distance (p < 0.05) were observed, but not in MAS (p> 0.05). Significant changes in FMA, BBS, and TIS scores between groups (p < 0.05) were observed. Significant main effects of time on BBS and TIS were demonstrated (p < 0.05). Our study shows that RAGT can maximize improvement in the functional score of FMA, BBS, TIS, steps, and distance during neurorehabilitation of subacute stroke patients regardless of their FAC level.

Abnormal synergistic gait mitigation in acute stroke using an innovative ankle-knee-hip interlimb humanoid robot: a preliminary randomized controlled trial

Abnormal spasticity and associated synergistic patterns are the most common neuromuscular impairments affecting ankle–knee–hip interlimb coordinated gait kinematics and kinetics in patients with hemiparetic stroke. Although patients with hemiparetic stroke undergo various treatments to improve gait and movement, it remains unknown how spasticity and associated synergistic patterns change after robot-assisted and conventional treatment. We developed an innovative ankle–knee–hip interlimb coordinated humanoid robot (ICT) to mitigate abnormal spasticity and synergistic patterns. The objective of the preliminary clinical trial was to compare the effects of ICT combined with conventional physical therapy (ICT-C) and conventional physical therapy and gait training (CPT-G) on abnormal spasticity and synergistic gait patterns in 20 patients with acute hemiparesis. We performed secondary analyses aimed at elucidating the biomechanical effects of Walkbot ICT on kinematic (spatiotemporal parameters and angles) and kinetic (active force, resistive force, and stiffness) gait parameters before and after ICT in the ICT-C group. The intervention for this group comprised 60-min conventional physical therapy plus 30-min robot-assisted training, 7 days/week, for 2 weeks. Significant biomechanical effects in knee joint kinematics; hip, knee, and ankle active forces; hip, knee, and ankle resistive forces; and hip, knee, and ankle stiffness were associated with ICT-C. Our novel findings provide promising evidence for conventional therapy supplemented by robot-assisted therapy for abnormal spasticity, synergistic, and altered biomechanical gait impairments in patients in the acute post-stroke recovery phase.

Trial Registration: Clinical Trials.gov identifier NCT03554642 (14/01/2020).

VALIDITY AND TEST–RETEST RELIABILITY OF AN INTELLIGENT ROBOTIC SHOULDER JOINT KINEMATICS SYSTEM FOR REHABILITATION

The presence of normal upper limb arm swing movement is considered an important gait movement that affects the selective coordinated locomotor control of the shoulder, elbow, and wrist joint movements. However, in patients with neurological disorders, flexor synergy is characterized by decreased selective neuromuscular control and reciprocal disinhibition of the wrist flexor muscles associated with spasticity or shortness. This research aimed to demonstrate the reliability, validity, and feasibility of the progressive exoskeletal robotic shoulder joint kinematics system. The robotic shoulder joint kinematics system comprises a gait function-retraining robot designed to provide arm swing. The changes in the shoulder joint angle between ImageJ motion analysis software and robotic shoulder joint kinematics system were compared in this research to investigate the reliability and validity of the latter. The linear regression analysis revealed good correlation between the measured angles and the shoulder angle data ([Formula: see text]). Furthermore, the test–retest reliability test demonstrated excellent reliability ([Formula: see text]). The robotic shoulder joint kinematics system generated successful arm swing during the locomotion and range of motion training of the shoulder.

Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems

Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of the level of motor recovery through the measurement of force and movement patterns. However, in actual robotic rehabilitation training, emergency stops occur frequently to prevent injury to patients. However, frequent stopping is a waste of time and resources of both therapists and patients. Therefore, early detection of emergency stops in real-time is essential to take appropriate actions. In this paper, we propose a novel deep-learning-based technique for detecting emergency stops as early as possible. First, a bidirectional long short-term memory prediction model was trained using only the normal joint data collected from a real robotic training system. Next, a real-time threshold-based algorithm was developed with cumulative error. The experimental results revealed a precision of 0.94, recall of 0.93, and F1 score of 0.93. Additionally, it was observed that the prediction model was robust for variations in measurement noise.

Electromechanical-assisted training for walking after stroke

BACKGROUND

Electromechanical- and robot-assisted gait-training devices are used in rehabilitation and might help to improve walking after stroke. This is an update of a Cochrane Review first published in 2007 and previously updated in 2017.

OBJECTIVES

Primary • To determine whether electromechanical- and robot-assisted gait training versus normal care improves walking after stroke Secondary • To determine whether electromechanical- and robot-assisted gait training versus normal care after stroke improves walking velocity, walking capacity, acceptability, and death from all causes until the end of the intervention phase SEARCH METHODS: We searched the Cochrane Stroke Group Trials Register (last searched 6 January 2020); the Cochrane Central Register of Controlled Trials (CENTRAL; 2020 Issue 1), in the Cochrane Library; MEDLINE in Ovid (1950 to 6 January 2020); Embase (1980 to 6 January 2020); the Cumulative Index to Nursing and Allied Health Literature (CINAHL; 1982 to 20 November 2019); the Allied and Complementary Medicine Database (AMED; 1985 to 6 January 2020); Web of Science (1899 to 7 January 2020); SPORTDiscus (1949 to 6 January 2020); the Physiotherapy Evidence Database (PEDro; searched 7 January 2020); and the engineering databases COMPENDEX (1972 to 16 January 2020) and Inspec (1969 to 6 January 2020). We handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trial authors in an effort to identify further published, unpublished, and ongoing trials.

SELECTION CRITERIA

We included all randomised controlled trials and randomised controlled cross-over trials in people over the age of 18 years diagnosed with stroke of any severity, at any stage, in any setting, evaluating electromechanical- and robot-assisted gait training versus normal care.

DATA COLLECTION AND ANALYSIS

Two review authors independently selected trials for inclusion, assessed methodological quality and risk of bias, and extracted data. We assessed the quality of evidence using the GRADE approach. The primary outcome was the proportion of participants walking independently at follow-up.

MAIN RESULTS

We included in this review update 62 trials involving 2440 participants. Electromechanical-assisted gait training in combination with physiotherapy increased the odds of participants becoming independent in walking (odds ratio (random effects) 2.01, 95% confidence interval (CI) 1.51 to 2.69; 38 studies, 1567 participants; P < 0.00001; I² = 0%; high-quality evidence) and increased mean walking velocity (mean difference (MD) 0.06 m/s, 95% CI 0.02 to 0.10; 42 studies, 1600 participants; P = 0.004; I² = 60%; low-quality evidence) but did not improve mean walking capacity (MD 10.9 metres walked in 6 minutes, 95% CI -5.7 to 27.4; 24 studies, 983 participants; P = 0.2; I² = 42%; moderate-quality evidence). Electromechanical-assisted gait training did not increase the risk of loss to the study during intervention nor the risk of death from all causes. Results must be interpreted with caution because (1) some trials investigated people who were independent in walking at the start of the study, (2) we found variation between trials with respect to devices used and duration and frequency of treatment, and (3) some trials included devices with functional electrical stimulation. Post hoc analysis showed that people who are non-ambulatory at the start of the intervention may benefit but ambulatory people may not benefit from this type of training. Post hoc analysis showed no differences between the types of devices used in studies regarding ability to walk but revealed differences between devices in terms of walking velocity and capacity.

AUTHORS' CONCLUSIONS

People who receive electromechanical-assisted gait training in combination with physiotherapy after stroke are more likely to achieve independent walking than people who receive gait training without these devices. We concluded that eight patients need to be treated to prevent one dependency in walking. Specifically, people in the first three months after stroke and those who are not able to walk seem to benefit most from this type of intervention. The role of the type of device is still not clear. Further research should consist of large definitive pragmatic phase 3 trials undertaken to address specific questions about the most effective frequency and duration of electromechanical-assisted gait training, as well as how long any benefit may last. Future trials should consider time post stroke in their trial design.

Improving abnormal gait patterns by using a gait exercise assist robot (GEAR) in chronic stroke subjects: A randomized, controlled, pilot trial

BACKGROUND

Although the Gait Exercise Assist Robot (GEAR) has been reported to effectively improve gait of hemiplegic patients, no study has investigated its use in chronic stroke patients. It is possible to facilitate gait reorganization by gait training with less compensation using the GEAR even in chronic stroke patients.

RESEARCH QUESTION

What are the effects of GEAR training on the abnormal gait patterns of chronic stroke subjects?

METHODS

Subjects were randomly assigned to either the GEAR group (n = 8) or the treadmill group (n = 11). Each group underwent 20 sessions (40 min/day, 5 days/week). The changes in the 10 types of abnormal gait patterns were evaluated using a three-dimensional motion analysis system and the Global Rating of Change (GRC) scale before and after the intervention, and at 1-month and 3-month follow-up assessment.

RESULTS

In the GEAR group, hip hiking at a 1-month follow-up assessment was markedly lesser than that before the intervention, and the excessive hip external rotation at 3-month follow-up assessment was notably lesser than that after the intervention, but the change in excessive hip external rotation was in the normal range. In the treadmill group, knee extensor thrust at a 1-month follow-up assessment was strikingly lesser than that before the intervention, but the difference was in the normal range. In the GEAR group, the GRC scale scores were considerably higher after the intervention, at a 1-month, and 3-month follow-up assessment than those before the intervention. But, in the treadmill group, only the GRC scale score at a 1-month follow-up assessment was visibly higher than that before the intervention.

SIGNIFICANCE

Gait training using the GEAR may be more effective than treadmill-training in improving the swing phase in chronic stroke subjects.

Effects of innovative hip-knee-ankle interlimb coordinated robot training on ambulation, cardiopulmonary function, depression, and fall confidence in acute hemiplegia

BACKGROUND

While Walkbot-assisted locomotor training (WLT) provided ample evidence on balance and gait improvements, the therapeutic effects on cardiopulmonary and psychological elements as well as fall confidence are unknown in stroke survivors.

OBJECTIVE

The present study aimed to compare the effects of Walkbot locomotor training (WLT) with conventional locomotor training (CLT) on balance and gait, cardiopulmonary and psychological functions and fall confidence in acute hemiparetic stroke.

METHODS

Fourteen patients with acute hemiparetic stroke were randomized into either the WLT (60 min physical therapy + 30 min Walkbot-assisted gait training) or CLT (60 min physical therapy + 30 min gait training) groups, 7 days/week over 2 weeks. Clinical outcomes included the Berg Balance Scale (BBS), Functional Ambulation Category (FAC), heart rate (HR), Borg Rating of Perceived Exertion (BRPE), Beck Depression Inventory-II (BDI-II), and the activities-specific balance confidence (ABC) scale. The analysis of covariance (ANCOVA) was conducted at P < 0.05.

RESULTS

ANCOVA showed that WLT showed superior effects, compared to CLT, on FAC, HR, BRPE, BDI-II, and ABC scale (P < 0.05), but not on BBS (P = 0.061).

CONCLUSIONS

Our results provide novel, promising clinical evidence that WLT improved balance and gait function as well as cardiopulmonary and psychological functions, and fall confidence in acute stroke survivors who were unable to ambulate independently.

Effects of gait exercise assist robot (GEAR) on subjects with chronic stroke: A randomized controlled pilot trial

OBJECTIVE

The aim of this study was to investigate whether gait training using the Gait Exercise Assist Robot (GEAR) is more effective for improving gait ability than treadmill gait training in chronic stroke subjects.

DESIGN

Subjects were randomly assigned to either the GEAR group (n = 8) or treadmill group (n = 11). Both groups received a training program of 20 sessions (5 days/week). The 10-m walk test, Timed Up and Go (TUG) test, 6-min walk test, the Medical Outcome Study 8-item Short Form Health Survey (SF-8), and Global Rating of Change (GRC) scales were administered at baseline (week 0), completion of training (week 4), 1-mo follow-up (week 8), and 3-mo follow-up (week 16).

RESULTS

Gait speed was significantly increased at completion of training and 1-mo follow-up compared with baseline in the GEAR group. Mean changes in TUG and 6-min walk were significantly greater in the GEAR group than in the treadmill group at completion of training compared to baseline. Furthermore, GRC scales were significantly increased at completion of training, 1-mo follow-up, and 3-mo follow-up compared with baseline in the GEAR group.

CONCLUSION

This study suggests that gait training using GEAR was more effective for improving gait ability than treadmill among subjects with chronic stroke.

REGISTRATION OF CLINICAL TRIALS

This study was registered with the University Hospital Medical Information Network (No. UMIN000028042).

van Asseldonk EH. Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders

BACKGROUND

In clinical practice, therapists choose the amount of assistance for robot-assisted training. This can result in outcomes that are influenced by subjective decisions and tuning of training parameters can be time-consuming. Therefore, various algorithms to automatically tune the assistance have been developed. However, the assistance applied by these algorithms has not been directly compared to manually-tuned assistance yet. In this study, we focused on subtask-based assistance and compared automatically-tuned (AT) robotic assistance with manually-tuned (MT) robotic assistance.

METHODS

Ten people with neurological disorders (six stroke, four spinal cord injury) walked in the LOPES II gait trainer with AT and MT assistance. In both cases, assistance was adjusted separately for various subtasks of walking (in this study defined as control of: weight shift, lateral foot placement, trailing and leading limb angle, prepositioning, stability during stance, foot clearance). For the MT approach, robotic assistance was tuned by an experienced therapist and for the AT approach an algorithm that adjusted the assistance based on performances for the different subtasks was used. Time needed to tune the assistance, assistance levels and deviations from reference trajectories were compared between both approaches. In addition, participants evaluated safety, comfort, effect and amount of assistance for the AT and MT approach.

RESULTS

For the AT algorithm, stable assistance levels were reached quicker than for the MT approach. Considerable differences in the assistance per subtask provided by the two approaches were found. The amount of assistance was more often higher for the MT approach than for the AT approach. Despite this, the largest deviations from the reference trajectories were found for the MT algorithm. Participants did not clearly prefer one approach over the other regarding safety, comfort, effect and amount of assistance.

CONCLUSION

Automatic tuning had the following advantages compared to manual tuning: quicker tuning of the assistance, lower assistance levels, separate tuning of each subtask and good performance for all subtasks. Future clinical trials need to show whether these apparent advantages result in better clinical outcomes.