Gait Training System Research Articles

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking

Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have been developed to address these issues and increase walking performance. While these devices have shown promise in their ability to increase musculature engagement of the lower limbs when used on a treadmill, most have not been developed or validated for overground walking and community use. Overground walking may limit the effectiveness of robotic devices due to differences in gait characteristics between walking terrains and reduced user engagement. The purpose of this study was to validate our multimodal robotic gait training system for overground walking in individuals with neuromuscular gait impairments. This untethered wearable robotic device can provide an ankle resistive torque proportional to the users’ biological ankle torque. The device can also provide audio biofeedback based on users’ plantar pressure intending to increase ankle power and muscle activity of the plantar flexors. Seven individuals with cerebral palsy participated. Participants walked overground and on a treadmill with our robotic gait training system in a single testing session. Results showed all seven participants to increase peak plantar flexor muscle activity, 10.3% on average, when walking with the gait trainer overground compared to treadmill. When compared to typical baseline overground walking, overground gait trainer use caused individuals to have slightly less knee joint excursion (3°) and moderately more ankle joint excursion (7°). This work supports our vision of using the wearable robotic device as a gait aid and rehabilitation tool in the home and community settings.

Trajectory Tracking Control of Pneumatic Cylinder-Actuated Lower Limb Robot for a Gait Training System

Trajectory Tracking Control of Pneumatic Cylinder-Actuated Lower Limb Robot for a Gait Training System

The Development of a Wearable Biofeedback System to Elicit Temporal Gait Asymmetry using Rhythmic Auditory Stimulation and an Assessment of Immediate Effects.

Temporal gait asymmetry (TGA) is commonly observed in individuals facing mobility challenges. Rhythmic auditory stimulation (RAS) can improve temporal gait parameters by promoting synchronization with external cues. While biofeedback for gait training, providing real-time feedback based on specific gait parameters measured, has been proven to successfully elicit changes in gait patterns, RAS-based biofeedback as a treatment for TGA has not been explored. In this study, a wearable RAS-based biofeedback gait training system was developed to measure temporal gait symmetry in real time and deliver RAS accordingly. Three different RAS-based biofeedback strategies were compared: open- and closed-loop RAS at constant and variable target levels. The main objective was to assess the ability of the system to induce TGA with able-bodied (AB) participants and evaluate and compare each strategy. With all three strategies, temporal symmetry was significantly altered compared to the baseline, with the closed-loop strategy yielding the most significant changes when comparing at different target levels. Speed and cadence remained largely unchanged during RAS-based biofeedback gait training. Setting the metronome to a target beyond the intended target may potentially bring the individual closer to their symmetry target. These findings hold promise for developing personalized and effective gait training interventions to address TGA in patient populations with mobility limitations using RAS.

An App-Assisted Frontend of Robot Gait Training System for Lower Limb Rehabilitation

An App-Assisted Frontend of Robot Gait Training System for Lower Limb Rehabilitation

Go Across Immersive Technology: A Preliminary Study of the Design and Development of a System for Gait Training Using Virtual Reality.

Virtual reality (VR) allows visuotactile interaction in a virtual environment. VR has several potential applications such as surgical training, phobia treatments, and gait rehabilitation. However, further interface development is required. Therefore, the objective of this study was to develop a noninvasive wearable device control to a VR gait training program. It consists of custom-made insoles with vibratory actuators, and plantar pressure sensor-based wireless interface with a VR game. System usability testing involved a habituation period and three gaming sessions. Significant gait improvement was associated with game scores (P < 0.05). This VR gait training system allowed real-time virtual immersive interaction with anticipatory stimulus and feedback during gait.

Neural network based patient recovery estimation of a PAM-based rehabilitation robot

Rehabilitation robots have shown a promise in aiding patient recovery by supporting them in repetitive, systematic training sessions. A critical factor in the success of such training is the patient’s recovery progress, which can guide suitable treatment plans and reduce recovery time. In this study, a neural network-based approach is proposed to estimate the patient’s recovery, which can aid in the development of an assist-as-needed training strategy for the gait training system. Experimental results show that the proposed method can accurately estimate the external torques generated by the patient to determine their recovery. The estimated patient recovery is used for an impedance control of a 2-DOF robotic orthosis powered by pneumatic artificial muscles, which improves the robot joint compliance coefficients and makes the patient more comfortable and confident during rehabilitation exercises.

Effect of wearable exoskeleton on post-stroke gait: A systematic review and meta-analysis.

Wearable exoskeletons are a recently developed technology. The present systematic review aimed to investigate the effect of a wearable exoskeleton on post-stroke walking by considering its use in a gait training system and simply as an orthosis assisting walking. We systematically searched for randomised and quasi-randomised controlled trials in PubMed, Scopus, CINAHL and Embase databases from their earliest publication record to July 2021. We chose reports of trials investigating the effects of exoskeleton-assisted training or the effects of wearing an exoskeleton to assist walking. A meta-analysis was conducted to explore the benefits of the wearable exoskeleton on mobility capacity, walking speed, motor function, balance, endurance and activities of daily living. We included 13 studies (492 participants) comparing exoskeleton-assisted training with dose-matched conventional gait training. Studies addressing the effect of wearing a wearable exoskeleton were unavailable. As compared with conventional gait training at the end of the intervention, exoskeleton-assisted training was superior for walking speed (mean difference [MD] 0.13 m/s, 95% CI 0.05; 0.21) and balance (standardized MD [SMD] 0.3, 95% CI 0.07; 0.54). The subgroup with chronic stroke (i.e., > 6 months) presented the outcome favouring exoskeleton-assisted training regarding overall mobility capacity (SMD 0.37, 95% CI 0.04; 0.69). At the end of follow-up, exoskeleton-assisted training was superior to conventional gait training in overall mobility (SMD 0.45, 95% CI 0.07; 0.84) and endurance (MD 46.23 m, 95% CI 9.90; 82.56). Exoskeleton-assisted training was superior to dose-matched conventional gait training in several gait-related outcomes at the end of the intervention and follow-up in this systematic review and meta-analysis, which may support the use of exoskeleton-assisted training in the rehabilitation setting. Whether wearing versus not wearing a wearable exoskeleton is beneficial during walking remains unknown.

Taping-induced cutaneous stimulation to the ankle tendons reduces minimum toe clearance variability

Large variability of minimum toe clearance (MTC) leads to a higher risk of tripping. Visual feedback-based gait training systems have been used to regulate MTC distribution, but these systems are expensive and bulky. Furthermore, the effect of such training lasts only for a short period of time. Considering the efficacy of elastic adhesive tape-induced cutaneous stimulation to the ankle tendons in improving proprioception and movement detection, we hypothesize that application of tapes to the ankle tendons as a practical method for modifying MTC distribution. To test this hypothesis, we recruited 13 young and healthy adults and instructed them to walk on a treadmill under four conditions: no taping, taping the tibialis anterior tendon, taping the Achilles tendon, and taping both tendons. We measured MTC distribution, lower limb joint angles and muscle activations of the tibialis anterior and gastrocnemius medialis, and compared these outcomes under the four conditions. The application of elastic adhesive tape to the ankle tendons had no significant effect on the average MTC height, but tapes applied to the Achilles tendon and both tendons significantly reduced MTC variability. Taping decreased the variability of some lower limb joint angles, but taping did not induce significant changes in the activation levels of the shank muscles. These results demonstrate that elastic adhesive tape applied to the shank can reduce MTC variability with minimal resistance, inertia and cumbersomeness.

A Mobile Gait Training System Providing an Active Interaction

In this paper, we propose an interactive mobile gait training system that allows trainees to interact actively with its content (the gait training content). The proposed system is a new type of gait training one combining a mobile robot with virtual reality (contents). It is a mobile system that projects virtual contents (for example, virtual footprints) for gait training on the actual ground (or floor). The performance and effectiveness of the proposed system were examined through a trainee’s foot recognition test and usability evaluation. The test results confirmed that the proposed system showed an average recognition ratio of more than 97%, meaning that the system could accurately recognize the trainee’s foot. In addition, as a result of usability evaluation, the overall satisfaction was 86%, confirming that the proposed system is effective.

Medical University Of Sofia

Ultimately, contemporary Information and Communication Technologies (ICT) were introduced in the rehabilitation practice. We use Exoskeletons, virtual reality, robotic rehabilitation with Locomat (Hocoma system for balance and gait training) and ThyroMotion system (for grasp training). Our purpose was to emphasize the impact of robotic rehabilitation in the process of recovery of patients with neurological complications of COVID-19, as follows: cerebral vascular accidents, spinal ischemic stroke, relapses of multiple sclerosis, Guillain-Barre syndrome, development of rare diseases (as cerebellar ataxia or amyotrophic lateral sclerosis /motor neuron disease), etc. We present typical and rare clinical cases (consequences and complications of COVID-19), treated by robotic neurorehabilitation. Our results demonstrated positive effects of ICT-application on the neuroplasticity, functional recovery and quality of life of neurological patients. We consider introduction of robotics as necessary tool in the process of neurorehabilitation.

Feasibility of a Novel Therapist-Assisted Feedback System for Gait Training in Parkinson's Disease.

We tested the feasibility of one session of treadmill training using a novel physical therapist assisted system (Mobility Rehab) using wearable sensors on the upper and lower limbs of 10 people with Parkinson's disease (PD). Participants performed a 2-min walk overground before and after 15 min of treadmill training with Mobility Rehab, which included an electronic tablet (to visualize gait metrics) and five Opal sensors placed on both the wrists and feet and on the sternum area to measure gait and provide feedback on six gait metrics (foot-strike angle, trunk coronal range-of-motion (ROM), arm swing ROM, double-support duration, gait-cycle duration, and step asymmetry). The physical therapist used Mobility Rehab to select one or two gait metrics (from the six) to focus on during the treadmill training. Foot-strike angle (effect size (ES) = 0.56, 95% Confidence Interval (CI) = 0.14 to 0.97), trunk coronal RoM (ES = 1.39, 95% CI = 0.73 to 2.06), and arm swing RoM (ES = 1.64, 95% CI = 0.71 to 2.58) during overground walking showed significant and moderate-to-large ES following treadmill training with Mobility Rehab. Participants perceived moderate (60%) and excellent (30%) effects of Mobility Rehab on their gait. No adverse events were reported. One session of treadmill training with Mobility Rehab is feasible for people with mild-to-moderate PD.

Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking.

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.

Effects of Robotic Interactive Gait Training Combined with Virtual Reality and Augmented Reality on Balance, Gross Motor Function, Gait Kinetic, and Kinematic Characteristics in Angelman Syndrome: A Case Report

Angelman syndrome (AS) is a genetic neurological disorder resulting in cognitive and neuromuscular impairments, such as lack of safety awareness and attention, as well as lack of balance and locomotor control. The robotic interactive gait training (RIGT) system is designed to provide accurate proprioceptive, kinematic, and kinetic feedback, and facilitate virtual reality and augmented reality (VR-AR) interactive exercises during gait training. In the present case report, we examined the effect of an innovative hip-knee-ankle interlimb-coordinated RIGT system. We utilized this therapeutic modality in a participant with Angelman syndrome (AS). Gross motor function measures, risk of fall, and gait-related kinetic (force), and kinematic (joint angle) biomechanical characteristics were assessed before and after 20 sessions of RIGT with VR-AR. We found RIGT with VR-AT improved gait ability, as shown by Performance-Oriented Mobility Assessment score, gross motor function by Gross Motor Function Measure score, balance by Pediatric Balance Scale score, knee and hip joint kinetics, and kinematics during gait. Our clinical and biomechanical evidence provide important clinical insights to improve the effectiveness of current neurorehabilitation approaches for treating patients with AS in balance and locomotor control and reduce the risk of falling.

Effect of Cluster Needling of Scalp Acupuncture Combined with Walking Training on Walking Ability of Stroke Patients under Different Task

<bold>Objective</bold> To observe the effect of cluster needling of scalp acupuncture combined with walking training on walking ability of stroke patients under single-task walking (STW) and dual-task walking (DTW) paradigm. <bold>Methods</bold> A total of 78 inpatients with non-acute stroke who were treated in the the Third Affiliated Hospital of Zhejiang Chinese Medicine University from September 2019 to April 2021, which were divided into the control group and the observation group according to the random number table method, with 39 cases in each group. Both groups received conventional treatment for cerebrovascular disease, the control group received walking training on the basis of conventional treatment, 40 minutes a time, once a day, five days a week, continuous treatment for four weeks, including antigravity muscle training, lower limb weight-bearing support training, lower limb step training of affect side and standing center of gravity transfer training, etc. The observation group received cluster needling of scalp acupuncture on the basis of the control group, once a day, five days a week, continuous treatment for four weeks. The parietal area, anterior parietal area and frontal area of the head were selected for cluster needling of scalp acupuncture, the needles were kept for six hours, routine walking training was performed during needle retention. Before and after treatment, the gait parameters (stride, step length, step speed, stride frequency, stride time, support time, swing time) under STW and DTW paradigms were evaluated by the gait detection evaluation and training system; activities of daily living (ADL) were used to assess patients' activities of daily living, including basic activities of daily living (BADL) and instrumental activities of daily living (IADL). <bold>Results</bold> ① Gait parameters: compared with the STW paradigm, stride, step length, stride speed, stride frequency and swing time of the stroke patients were significantly decreased under the DTW paradigm, while stride time and support time were significantly increased, the differences were statistically significant (<italic>P</italic>&lt;0.05). Under the STW and DTW paradigm, compared with before treatment, stride,step length, step speed, stride frequency and swing time of the two groups after treatment were significantly increased (<italic>P</italic>&lt;0.05), while stride time and support time were significantly decreased (<italic>P</italic>&lt;0.05). Under the STW paradigm, compared with the control group, the stride,step length, step speed, stride frequency and support time of the observation group were significantly increased (<italic>P</italic>&lt;0.05), and the stride time was significantly decreased (<italic>P</italic>&lt;0.05). Under the DTW paradigm, compared with the control group, stride,step length, step speed, stride frequency of the observation group were significantly increased, while the stride time was significantly decreased (<italic>P</italic>&lt;0.05). ② ADL scores: compared with before treatment, the ADL total score and BADL score of the two groups after treatment were significantly decreased, and the IADL score in the observation group after treatment was significantly decreased (<italic>P</italic>&lt;0.05). Compared with the control group, the ADL total score and IADL score in the observation group were significantly decreased (<italic>P</italic>&lt;0.05). <bold>Conclusion</bold> The walking ability of stroke patients with dual-task walking is lower than that of single-task walking; cluster needling of scalp acupuncture combined with walking training can effectively improve the walking ability of dual-task and activities of daily living.

Artificial intelligence applications and robotic systems in Parkinson's disease (Review).

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder following Alzheimer's disease. Advanced stages of PD, 4 or 5 of the Hoehn and Yahr Scale, are characterized by severe motor complications, limited mobility without assistance, risk of falling, and non-motor complications. The aim of this review was to provide a practical overview on specific artificial intelligence (AI) systems for the management of advanced stages of PD, as well as relevant technological limitations. The authors conducted a systematic search on PubMed and EMBASE with predefined keywords searching for studies published until December 2020. Full articles that satisfied the inclusion criteria were included in the systematic review. To minimize results bias, the reference list was manually searched for pertinent articles to identify any additional relevant missed publications. Exclusion criteria included the following: Other stages of PD than 4 and 5 of the Hoehn and Yahr Scale, case reports, reviews, practice guidelines, commentaries, opinions, letters, editorials, short surveys, articles in press, conference abstracts, conference papers, and abstracts published without a full article. The search identified 21 studies analyzing AI-based applications and robotic systems used for the management of advanced stages of PD, out of which 6 articles analyzed AI-based applications for autonomous management of pharmacologic therapy, 5 articles analyzed home-based telemedicine systems and 10 articles analysed robot-assisted gait training systems. The authors identified significant evidence demonstrating that current AI-based technologies are feasible for automatic management of patients with advanced stages of PD. Improving the quality of care and reducing the cost for patients and healthcare systems are the most important advantages.

The effect of pelvic movements of a gait training system for stroke patients: a single blind, randomized, parallel study

BackgroundAging societies lead to higher demand for gait rehabilitation as age-related neurological disorders such as stroke and spinal cord injury increase. Since conventional methods for gait rehabilitation are physically and economically burdensome, robotic gait training systems have been studied and commercialized, many of which provided movements confined in the sagittal plane. For better outcomes of gait rehabilitation with more natural gait patterns, however, it is desirable to provide pelvic movements in the transverse plane. In this study, a robotic gait training system capable of pelvic motions in the transverse plane was used to evaluate the effect of the pelvic motions on stroke patients.MethodHealbot T, which is a robotic gait training system and capable of providing pelvic movements in the transverse plane as well as flexion/extension of the hip and knee joints and adduction/abduction of the hip joints, is introduced and used to evaluate the effect of the pelvic movement on gait training of stroke patients. Gait trainings in Healbot T with and without pelvic movements are carried out with stroke patients having hemiparesis.ExperimentTwenty-four stroke patients with hemiparesis were randomly assigned into two groups and 23 of them successfully completed the experiment except one subject who had dropped out due to personal reasons. Pelvis-on group was provided with pelvic motions whereas no pelvic movement was allowed for pelvis-off group during 10 sessions of gait trainings in Healbot T. Electromyography (EMG) signals and interaction forces as well as the joint angles of the robot were measured. Gait parameters such as stride length, cadence, and walking speed were measured while walking on the ground without assistance of Healbot T after gait training on 1st, 5th, and 10th day.ResultStride length significantly increased in both groups. Furthermore, cadence and walking speed of the pelvis-on group were increased by 10.6% and 11.8%. Although interaction forces of both groups except the thighs showed no differences, EMG signals from gluteus medius of the pelvis-on group increased by 88.6% during stance phase. In addition, EMG signals of biceps femoris, gastrocnemius medial, and gastrocnemius lateral of the pelvis-on group increased whereas EMG signals of the pelvis-off group except gastrocnemius lateral showed no difference after gait trainings.ConclusionGait training using a robotic gait training system with pelvic movements was conducted to investigate the effects of lateral and rotational pelvic movements in gait training of stroke patients. The pelvic movements affected to increase voluntary muscle activation during the stance phase as well as cadence and walking speed.Clinical trial registrationKCT0003762, 2018-1254, Registered 28 October 2018, https://cris.nih.go.kr/cris/search/search_result_st01_kren.jsp?seq=14310<ype=&rtype=

Development of Virtual Reality-based Gait Training System Simulating Personal Home Environment.

We developed a virtual reality (VR)-based gait training system, which could be used by inpatients to train their gait function in a simulated home environment, to reduce the risk of falling after discharge. The proposed system simulates the home environment on a head-mounted display, in which a user can walk around freely. The system provides visual feedback in the event of a collision with an indoor object such as a wall or furniture, prompting the user to modify his or her gait pattern. We first applied the system to healthy young adults and confirmed the usefulness of visual feedback in reducing the walking time and the number of collisions in the simulated room environment. Further, we applied the system to an inpatient with stroke and lower limb paralysis. The patient performed gait training based on a scenario of daily activity using the VR environment that mimicked his house. Five days of training significantly improved the gait and balance functions of the patient. These results suggest that the proposed system foster attention to the surrounding environment and improve gait function in both healthy participants and patients with stroke.Clinical Relevances-This study establishes the importance of visual feedback for VR-based gait training. Additionally, it provides a novel application of VR for gait and balance training in patients with stroke and lower limb paralysis.

Design, Manufacturing, and Control of a Pneumatic-Driven Passive Robotic Gait Training System for Muscle-Weakness in a Lower Limb.

We designed and manufactured a pneumatic-driven robotic passive gait training system (PRPGTS), providing the functions of body-weight support, postural support, and gait orthosis for patients who suffer from weakened lower limbs. The PRPGTS was designed as a soft-joint gait training rehabilitation system. The soft joints provide passive safety for patients. The PRPGTS features three subsystems: a pneumatic body weight support system, a pneumatic postural support system, and a pneumatic gait orthosis system. The dynamic behavior of these three subsystems are all involved in the PRPGTS, causing an extremely complicated dynamic behavior; therefore, this paper applies five individual interval type-2 fuzzy sliding controllers (IT2FSC) to compensate for the system uncertainties and disturbances in the PRGTS. The IT2FSCs can provide accurate and correct positional trajectories under passive safety protection. The feasibility of weight reduction and gait training with the PRPGTS using the IT2FSCs is demonstrated with a healthy person, and the experimental results show that the PRPGTS is stable and provides a high-trajectory tracking performance.

Design and Validation of a Smartphone-based Haptic Feedback System for Gait Training

This letter presents a novel vibrotactile haptic system controlled by a smartphone that can be used for gait training of older adults. Compared to previous works, the system's modularity as well as its smartphone-based controller and user interface can enhance its usability and promote regular gait training of users during their daily living. Given the prevalence of reduced stride length and speed in older adults, we developed a biomechanical data-driven approach to enable improving those outcomes via modifying their underlying surrogates. A subject study was performed by recruiting 12 young participants (19-39 years) to assess the efficacy of the haptic system and our approach based on biomechanical surrogates. It was found that the participants could significantly increase their thigh and shank extensions (i.e., the surrogates) via the feedback provided by our system, and those increases resulted in higher values of their stride length and walking speed. The results provide a clear proof-of-concept for the developed biomechanics-driven haptic system for gait training of older adults to potentially improve their mobility and living independence.