Effects of robot (SUBAR)-assisted gait training in patients with chronic stroke: Randomized controlled trial

A Comprehensive Review: Robot-Assisted Treatments for Gait Rehabilitation in Stroke Patients

Robot-assisted gait training (RAGT) is at the cutting edge of stroke rehabilitation, offering a groundbreaking method to improve motor recovery and enhance the quality of life for stroke survivors. This review investigates the effectiveness and application of various RAGT systems, including both end-effector and exoskeleton robots, in facilitating gait enhancements. The selection process for this comprehensive analysis involved a meticulous review of the literature from databases such as PubMed, the Cochrane Library, and EMBASE, focusing on studies published between 2018 and 2023. Ultimately, 27 studies met the criteria and were included in the final analysis. The focus of these studies was on the various RAGT systems and their role in promoting gait and balance improvements. The results of these studies conclusively show that patients experience significant positive effects from RAGT, and when combined with other physiotherapy methods, the outcomes are notably superior in enhancing functional ambulation and motor skills. This review emphasizes RAGT's capability to deliver a more customized and effective rehabilitation experience, highlighting the importance of tailoring interventions to meet the specific needs of each patient.

Effect of exoskeleton robot-assisted training on gait function in chronic stroke survivors: a systematic review of randomised controlled trials

OBJECTIVES

Numbers of research have reported the usage of robot-assisted gait training for walking restoration post-stroke. However, no consistent conclusion has been reached yet about the efficacy of exoskeleton robot-assisted training (ERAT) on gait function of stroke survivors, especially during the chronic period. We conducted a systematic review to investigate the efficacy of ERAT on gait function for chronic stroke survivors.

DESIGN

This review followed the Participant, Intervention, Comparison and Outcome principle.

DATA SOURCES

PubMed, Cochrane Library, Web of Science, Embase and Cumulative Index to Nursing and Allied Health Literature databases were systematically searched until December 2022.

ELIGIBILITY CRITERIA

Only randomised controlled trials (RCTs) were included and these RCTs took patients who had a chronic stroke as participants, exoskeleton robot-assisted gait training as intervention, regular rehabilitation therapy as comparison and gait-related functional assessments as outcomes.

DATA EXTRACTION AND SYNTHESIS

Data extraction and synthesis used the reporting checklist for systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The risk of bias and methodological quality of included studies were evaluated by two independent investigators under the guidance of Cochrane risk of bias.

RESULTS

Out of 278 studies, a total of 10 studies (n=323, mean age 57.6 years, 63.2% males) were identified in this systematic review. According to the Cochrane risk of bias, the quality of these studies was assessed as low risk. Six studies reported favourable effects of ERAT on gait function involving gait performance, balance function and physical endurance, and the ERAT group was significantly superior when compared with the control group. In contrast, the other four trials showed equal or negative effects of ERAT considering different study designs. All the included studies did not claim any serious adverse events.

CONCLUSION

ERAT could be an efficient intervention to improve gait function for individuals who had a chronic stroke. However, more rigorously designed trials are required to draw more solid evidence.

PROSPERO REGISTRATION NUMBER

CRD42023410796.

Exoskeletal wearable robot on ambulatory function in patients with stroke: a protocol for an international, multicentre, randomised controlled study

INTRODUCTION

The purpose of this study is to determine the effect of overground gait training using an exoskeletal wearable robot (exoskeleton) on the recovery of ambulatory function in patients with subacute stroke. We also investigate the assistive effects of an exoskeleton on ambulatory function in patients with subacute stroke.

METHODS AND ANALYSIS

This study is an international, multicentre, randomised controlled study at five institutions with a total of 150 patients with subacute stroke. Participants will be randomised into two groups (75 patients in the robot-assisted gait training (RAGT) group and 75 patients in the control group). The gait training will be performed with a total of 20 sessions (60 min/session); 5 sessions a week for 4 weeks. The RAGT group will receive 30 min of gait training using an exoskeleton (ANGEL LEGS M20, Angel Robotics) and 30 min of conventional gait training, while the control group will receive 60 min conventional gait training. In all the patients, the functional assessments such as ambulation, motor and balance will be evaluated before and after the intervention. Follow-up monitoring will be performed to verify whether the patient can walk without physical assistance for 3 months. The primary outcome is the improvement of the Functional Ambulatory Category after the gait training. The functional assessments will also be evaluated immediately after the last training session in the RAGT group to assess the assistive effects of an exoskeletal wearable robot. This trial will provide evidence on the effects of an exoskeleton to improve and assist ambulatory function in patients with subacute stroke.

ETHICS AND DISSEMINATION

This protocol has been approved by the Institutional Review Board of each hospital and conforms to the Declaration of Helsinki. The results will be disseminated through publication.

TRIAL REGISTRATION NUMBER

Protocol was registered at ClinicalTrials.gov (NCT05157347) on 15 December 2021 and CRIS (KCT0006815) on 19 November 2021.

Comparative efficacy of gait training for balance outcomes in patients with stroke: A systematic review and network meta-analysis

Background

Growing evidence suggests that gait training can improve stroke patients’ balance outcomes. However, it remains unclear which type of gait training is more effective in improving certain types of balance outcomes in patients with stroke. Thus, this network meta-analysis (NMA) included six types of gait training (treadmill, body-weight-supported treadmill, virtual reality gait training, robotic-assisted gait training, overground walking training, and conventional gait training) and four types of balance outcomes (static steady-state balance, dynamic steady-state balance, proactive balance, and balance test batteries), aiming to compare the efficacy of different gait training on specific types of balance outcomes in stroke patients and determine the most effective gait training.

Method

We searched PubMed, Embase, Medline, Web of Science, and Cochrane Library databases from inception until 25 April 2022. Randomized controlled trials (RCTs) of gait training for the treatment of balance outcomes after stroke were included. RoB2 was used to assess the risk of bias in the included studies. Frequentist random-effects network meta-analysis (NMA) was used to evaluate the effect of gait training on four categories of balance outcomes.

Result

A total of 61 RCTs from 2,551 citations, encompassing 2,328 stroke patients, were included in this study. Pooled results showed that body-weight-support treadmill (SMD = 0.30, 95% CI [0.01, 0.58]) and treadmill (SMD = 0.25, 95% CI [0.00, 0.49]) could improve the dynamic steady-state balance. Virtual reality gait training (SMD = 0.41, 95% CI [0.10, 0.71]) and body-weight-supported treadmill (SMD = 0.41, 95% CI [0.02, 0.80]) demonstrated better effects in improving balance test batteries. However, none of included gait training showed a significant effect on static steady-state balance and proactive balance.

Conclusion

Gait training is an effective treatment for improving stroke patients’ dynamic steady-state balance and balance test batteries. However, gait training had no significant effect on static steady-state balance and proactive balance. To achieve maximum efficacy, clinicians should consider this evidence when recommending rehabilitation training to stroke patients. Considering body-weight-supported treadmill is not common for chronic stroke patients in clinical practice, the treadmill is recommended for those who want to improve dynamic steady-state balance, and virtual reality gait training is recommended for those who want to improve balance test batteries.

Limitation

Missing evidence in relation to some types of gait training is supposed to be taken into consideration. Moreover, we fail to assess reactive balance in this NMA since few included trials reported this outcome.

Systematic Review Registration

PROSPERO, identifier CRD42022349965.

Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis

BACKGROUND

Balance impairment is a common disability in post-stroke survivors, leading to reduced mobility and increased fall risk. Robotic gait training (RAGT) is largely used, along with traditional training. There is, however, no strong evidence about RAGT superiority, especially on balance. This study aims to determine RAGT efficacy on balance of post-stroke survivors.

METHODS

PubMed, Cochrane Library, and PeDRO databases were investigated. Randomized clinical trials evaluating RAGT efficacy on post-stroke survivor balance with Berg Balance Scale (BBS) or Timed Up and Go test (TUG) were searched. Meta-regression analyses were performed, considering weekly sessions, single-session duration, and robotic device used.

RESULTS

A total of 18 trials have been included. BBS pre-post treatment mean difference is higher in RAGT-treated patients, with a pMD of 2.17 (95% CI 0.79; 3.55). TUG pre-post mean difference is in favor of RAGT, but not statistically, with a pMD of -0.62 (95%CI - 3.66; 2.43). Meta-regression analyses showed no relevant association, except for TUG and treatment duration (β = -1.019, 95% CI - 1.827; -0.210, p-value = 0.0135).

CONCLUSIONS

RAGT efficacy is equal to traditional therapy, while the combination of the two seems to lead to better outcomes than each individually performed. Robot-assisted balance training should be the focus of experimentation in the following years, given the great results in the first available trials. Given the massive heterogeneity of included patients, trials with more strict inclusion criteria (especially time from stroke) must be performed to finally define if and when RAGT is superior to traditional therapy.

Effects of a lower limb rehabilitation robot with various training modes in patients with stroke: A randomized controlled trial

BACKGROUND

The effect of robot-assisted gait training has been demonstrated to improve gait recovery in patients with stroke. The aim of this study was to determine effects of robot-assisted gait training with various training modes in patients post stroke.

METHODS

Forty-seven patients post stroke were randomly assigned to one of 4 groups: Healbot T with pelvic off mode (pelvic off group; n = 11); Healbot T with pelvic control mode (pelvic on group; n = 12); Healbot T with constraint-induced movement therapy (CIMT) mode (CIMT group; n = 10); and conventional physiotherapy (control group; n = 10). All patients received a 30-minute session 10 times for 4 weeks. The primary outcomes were the 10-meter walk test (10MWT) and Berg Balance Scale (BBS). The secondary outcomes were functional ambulation category, timed up and go (TUG), and motricity index of the lower extremities (MI-Lower).

RESULTS

The pelvic off group showed significant improvements in BBS, TUG, and MI-Lower (P < .05). The pelvic on and CIMT groups showed significant improvement in 10MWT, BBS, TUG, and MI-Lower (P < .05). Compared with control group, the pelvic on group showed greater improvement in the TUG and BBS scores; the CIMT group showed greater improvement in 10MWT and MI-Lower (P < .05).

CONCLUSION

This study suggested that Healbot T-assisted gait training benefited patients with stroke. The Healbot T with pelvic motion and CIMT modes were more helpful in improving balance and walking ability and lower limb strength, respectively, compared with conventional physiotherapy.

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.