In this case report, we investigated the effects of robot-assisted gait therapy in a chronic stroke patient using motor assessment and gait analysis. A patient who suffered from the right hemiparesis following the left corona radiata and basal ganglia infarction received 30 minutes of robot-assisted gait therapy, 3 times a week for 4 weeks. Outcome was measured using Motoricity index(MI), Fugl-Meyer assessment(FMA), modified motor assessment scale(MMAS), isometric torque, body tissue composition, 10-meter gait speed and gait analysis. After robot-assisted gait therapy, the patient showed improvement in motor functions measured by MI, FMA, MMAS, isometric torque, skeletal muscle mass, 10-meter gait speed. In gait analysis, cadence, single support time, double support time, step length, walking speed improvement in after robot-assisted gait therapy. The results of this study showed that robot-assisted gait therapy is considered to facilitate locomotor recovery of the chronic hemiparetic stroke patient.
Purpose : The goal of this study is to examine the effect of robot assisted gait training (RAGT) on the kinematic factors (temporospatial gait parameters, gait cycle ratio, and gait line length) of gait in stroke patients. Methods : The subjects of this study were 24 stroke patients selected by inclusion criteria. Participants were randomly allocated to two groups: robot assisted gait training (n=11) and general neurological physical therapy group (n=11). In the robot-assisted gait training group, robot-assisted gait training was mediated for 30 minutes a day in addition to general neurological physical therapy. The general neurological physical therapy group was mediated by general neurological physical therapy for 30 minutes a day in addition to general neurological physical therapy. The number of interventions was 5 times a week for 5 weeks. In order to compare the kinematic factors of walking between the two groups, gait analysis was performed before and after 5 weeks of training using the Zebris gait analysis system. Results : As a result of the gait analysis of the two groups, there were significant differences in temporospatial gait variables (step length, stride length, step width, step time, stride time), gait cycle ratio (swing phase, stance phase) and gait line length. However, there was no significant difference in the cadence (temporospatial gait parameters) in the robot assisted gait training group compared to general neurological physical therapy group. Conclusion : It is considered to be a useful treatment for stroke patients to promote the recovery of gait function in stroke patients. Based on the results of this study, continuous robot assisted gait training treatment is considered to have a positive effect on gait ability, the goal of stroke rehabilitation. In the future, additional studies should be conducted on many subjects of stroke patients, the kinematic factors of the legs according to the severity of stroke and treatment period, and the effect of gait training.
Robot-assisted rehabilitation therapy has been used to increase physical function in post-stroke patients. The aim of this meta-analysis was to identify whether robot-assisted gait training can improve patients' functional abilities. A comprehensive search was performed of PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Physiotherapy Evidence Database (PEDro), Academic Search Premier (ASP), ScienceDirect, Korean Studies Information Service System (KISS), Research Information Sharing Service (RISS), Korea National Library, and the Korean Medical Database up to April, 2014. Fifteen eligible studies researched the effects of robot-assisted gait training to a control group. All outcome measures were classified by International Classification of Functioning, Disability, and Health (ICF) domains (body function and structures, activity, and participation) and were pooled for calculating the effect size. The overall effect size of the robot-assisted gait training was .356 [95% confidence interval (CI): .186~.526]. When the effect was compared by the type of electromechanical robot, Gait Trainer (GT) (.471, 95% CI: .320~.621) showed more effective than Lokomat (.169, 95% CI: .063~.275). In addition, acute stroke patients showed more improvement than others. Although robot-assisted gait training may improve function, but there is no scientific evidence about the appropriate treatment time for one session or the appropriate duration of treatment. Additional researchers are needed to include more well-designed trials in order to resolve these uncertainties.
Objective: Robot assisted gait training is implemented as part of therapy for the recovery of gait patterns in recent clinical fields, and the scope of implications are continuously increasing. However clear therapy protocols of robot assisted gait training are insufficent. The purpose of this study was to investigate the effects of robot-assisted gait training applied with guidance force on balance and gait performance in persons with hemiparetic stroke. Design: Two group pre-test post-test design. Methods: Nineteen persons were diagnosed with hemiparesis following stroke participated in this study. The participants were randomly assigned to the unilateral guidance group or bilateral guidance group to conduct robot-assisted gait training. All participants underwent robot-assisted gait training for twelve sessions (30 min/d, 3 d/wk for 4 weeks). They were assessed with gait parameters (gait velocity, cadence, step length, stance phase, and swing phase) using Optogait. This study also measured the dynamic gait index (DGI), the Berg balance scale (BBS) score, and timed up and go (TUG). Results: After training, BBS scores were was significantly increased in the bilateral training group than in the unilateral guidance group (p<0.05). Spatiotemporal parameters were significantly changed in the bilateral training group (gait speed, swing phase ratio, and stance phase ratio) compared to the unilateral training group (p<0.05). Conclusions: The results of this study suggest that robot-assisted gait training show feasibility in facilitating improvements in balance and gait performance for subacute hemiparetic stroke patients.
Purpose: This study was conducted to assess the effects of robot-assisted gait training with visual feedback on gait, balance, and balance confidence in patients with chronic stroke. Methods: Thirty subjects with chronic stroke were randomly assigned to two groups: the experimental group (n=15) and the control group (n=15). The experimental group performed robot-assisted gait training for 30 minutes and the control group performed gait training with assisted devices training for 30 minutes after both groups performed conventional physical therapy for 30 minutes. Both groups performed the therapeutic interventions for 5 days per week, for a period of 4 weeks. For assessment of the 10 m walking test (10 MWT), Figure of 8 on the walk test (F8WT), Timed-Up and Go test (TUG), and Berg Balance Scale (BBS) were used to test the gait and balance, and the Korean version of the Activities-specific Balance Confidence Scale was used to test the balance confidence. Results: The experimental group showed significant improvement in the 10 MWT and the K-ABC (p<0.05), and the control group showed significant improvement in the BBS and the TUG (p<0.05). In four measurements, there were significant differences between the two groups (p<0.05), and the control group showed significant improvement in the F8WT at pre and post intervention (p<0.05). Conclusion: This study demonstrated that Robot-assisted gait training with visual feedback is an effective intervention for improving straight gait abilities and balance confidence, while the control group showed some improvement in curve gait and balance. Thus, we suggest both Robot-assisted gait training with visual feedback and gait training with assisted devices training exercise as a therapeutic intervention in chronic stroke rehabilitation.
PURPOSE: This study evaluated the effects of robot-assisted gait training combined with virtual reality training on balance and gait ability in stroke patients. METHODS: Thirty-one stroke patients were allocated randomly into one of two groups: robot-assisted gait training combined virtual reality training group (RGVR group; n = 16) and control group (n = 15). The RGVR group received 30 minutes of robot-assisted gait training combined with virtual reality training. Robot-assisted gait training was conducted in parallel using a virtual reality device. In the Control group, neurodevelopmental therapy was performed according to the function of chronic stroke patients. Both groups underwent training for 30 minutes, three times per week for eight weeks. The balance assessment system (BioRescue, Marseille, France), BBS, and TUG were used to evaluate the balance ability. The OptoGait (Microgate Srl, Bolzano, Italy) and 10 mWT were measured to evaluate the gait ability. The measurements were performed before and after the eight-week intervention period. RESULTS: Both groups showed significant improvement in their balance and gait ability during the intervention. RGVR showed significant differences in balance and gait ability compared to the control group groups (p < .05). These results showed that RGVR was more effective on balance and gait ability in patients with chronic stroke. CONCLUSION: RGVR can improve balance and gait ability, highlighting the benefits of RGVR. This study provides intervention data for recovering the balance and gait ability of chronic stroke patients.
Background: Previous robot-mediated gait training has been proven several limitations such as pointless repeated motion training, decreased presence, etc. In this research, adult stroke patients were participated in robot-mediated gait training accompanied with or without virtual reality program. Objectives: Exploring whether the results indicated virtual reality system has contribution to muscle strength and balance ability. Design: A case series research, cross-over trial. Methods: Eleven participants (male 4, female 7) with adults diagnosed as stroke from medical doctor ware engaged. The participants received 2 treatment sessions of identical duration, robot-assisted gait training with virtual reality and robot-assisted gait training with screen-off randomly crossed over include 1-day for each person of wash-out period. The parameter was muscle activity, the researchers assessed sEMG (surface electromyography). Results: The result showed less muscle activities during training in robot-assisted gait training with virtual reality circumstances, and these indicated muscles were gluteus medius muscle, vastus medialis muscle, vastus intermedius and vastus lateralis muscle, semimembranosus muscle, gastrocnemius-lateral head, and soleus muscle (P<.05). Conclusion: In this study, we analyzed the outcome of muscle activity for clinical inference of robot-assisted gait training with virtual reality (VR). Less muscle activity was measured in the treatment accompanied by VR, therefore, a more systematic, in-depth and well-founded level of follow-up research is needed.
Purpose: This study aimed to investigate the effect of robot-assisted gait training on the active ranges of motion, gait abilities, and biomechanical characteristics of gait in patients who underwent lower extremity surgery, and to verify the effectiveness and clinical usefulness of robot-assisted gait training. Methods: This study was conducted on 14 subjects who underwent lower extremity surgery. The subjects participated in robot-assisted gait training for 2 weeks. The active ranges of motion of the lower extremities were evaluated, and gait abilities were assessed using 10-m and 2-min walk tests. An STT Systems Inertial Measurement Unit was used to collect data on biomechanical characteristics during gait. Spatiotemporal parameters were used to measure cadence, step length, and velocity, and kinematic parameters were used to measure hip and knee joint movement during gait. Results: Significant improvements in the active ranges of motion of the hip and knee joints (flexion, extension, abduction, and adduction) and in the 10-m and 2-min walk test results were observed after robot-assisted gait training (p < 0.05). In addition, biomechanical characteristics of gait, spatiotemporal factors (cadence, step length, and velocity), and kinematic factors (gait hip flexion-extension, internal rotation-external rotation angle, and knee joint flexion-extension) were also significantly improved (p < 0.05). Conclusion: The results of this study are of clinical importance as they demonstrate that robot-assisted gait training can be used as an effective intervention method for patients who have undergone lower extremity surgery. Furthermore, the findings of this study are clinically meaningful as they expand the scope of robot-assisted gait training, which is currently mainly applied to patients with central nervous system conditions.
Purpose: The purpose of this study was to determine the effect of early Robot-assisted training on gait ability, function and ADL in patients with stroke. Methods: 26 patients with stroke were recruited for this study. The subjects were randomly assigned to either the experimental group (EG) or the control group (CG), with 13 patients in each group. All subjects received a routine physical therapy. The robot-assisted training was for 30 min in the case of the EG subjects. The assessment tools of this study involved the gait ability, balance ability, function and ADL. The measurements were recorded before the intervention and after the intervention. Results: EG subjects and CG subjects, the variables measured after the intervention significantly differed from gait ability, balance ability, function and ADL without the FMA (p<0.05). The FMA was only effective experimental group after intervention. Also, there were significant differences in gait ability, balance ability, function and ADL without the FMA at post-test between the 2 groups (p<0.05). Conclusion: The findings indicate that early robot-assisted training exerts a positive effect on gait ability, balance ability, function and ADL in patients with stroke. This result indicates the possibility of application of the early Robot-assisted training to the management for stroke patients. Further studies are required to generalize the result for this study.
Purpose: This systematic review aims to determine whether robot-assisted training is more effective in gait training for persons with subacute hemiparetic stroke. Methods: This study adopted a systematic review study design focused on subacute hemiparetic stroke, and four core academic databases were searched until June 11, 2021, for relevant studies, including PubMed, Embase, the Cochrane Library, and ProQuest Central. The review included randomized controlled trials (RCTs) evaluating the effects of robotic-assisted training on gait performance in persons with a diagnosis of subacute hemiparetic stroke. The selected RCT studies were qualitatively synthesized based on the population, intervention, comparison, outcome, settings, and study design (PICOS-SD). Results: The study selected five RCTs involving 253 subacute hemiparetic stroke patients and performing robotic-assisted gait training using the following devices: the Lokomat, Morning Walk, Walkbot, ProStep Plus, or Gait Trainer II. Five RCTs were eligible for the meta-analysis after quantitative synthesis, and the results showed that the robot-assisted gait training group had a greater gait performance than the control group based on the 10-meter walk test, Berg balance scale, Rivermed mobility index, functional ambulation category, and modified Barthel index. Conclusion: The results of this study showed that the gait performance of subacute hemiparetic stroke patients changes throughout robot-assisted gait training, but there were no indications that any of the clinically relevant effects of robot-assisted training are greater than those of conventional gait training. Further, the small sample size and different therapeutic intensities indicate that definitive conclusions could not be made.
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