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Rehabilitation robots may help the patient improve their recovery by supporting them to perform repetitive, systematic training sessions. Safety and comfortable feeling of the patients who training with robots is an important issue in not only the steady state but also the transient process. In this research, the trajectory tracking control problems of a two-degrees of freedom (2-DOF) robotic orthosis is discussed. The robotic orthosis is powered by pneumatic artificial muscles (PAMs) in an antagonistic configuration. based on a mathematical model, a modified computed torque control scheme is employed to enhance the tracking performance. The effectiveness of the proposed control strategy is verified by the experiments with the participation of different subjects.

Mobility options for persons suffering from paraplegia or paraparesis are limited to mainly wheeled devices. There are significant health, psychological, and social consequences related to being confined to a wheelchair. We present the Mina, a robotic orthosis for assisting mobility, which offers a legged mobility option for these persons. Mina is an overground robotic device that is worn on the back and around the legs to provide mobility assistance for people suffering from paraplegia or paraparesis. Mina uses compliant actuation to power the hip and knee joints. For paralyzed users, balance is provided with the assistance of forearm crutches. This paper presents the evaluation of Mina with two paraplegics (SCI ASIA-A). We confirmed that with a few hours of training and practice, Mina is currently able to provide paraplegics walking mobility at speeds of up to 0.20 m/s. We further confirmed that using Mina is not physically taxing and requires little cognitive effort, allowing the user to converse and maintain eye contact while walking., (© 2011 IEEE)

Body-weight supported treadmill training following neurological disorders such as stroke and spinal cord injuries (SCI) has become an integral part of rehabilitation for treating gait disorders. Unfortunately techniques for selecting important training parameters, such as walking speed and body-weight support, have not been established. Here we present a 3-D inverse-dynamics based approach for evaluating an individual's ability to ambulate, in terms of evaluating the magnitude and timing of joint moments at the ankle, knee and hip. This technique, which utilizes an instrumented gait robot, allows clinicians and researchers the ability to determine the training parameters in which subjects generate joint moments at the proper phases of the gait cycle which when combined with electromyographic recordings, can help establish and then progress training parameters for individuals on a subject-by-subject basis. We believe that training subjects at their preferred walking speed and body-weight support will lead to higher functional outcomes.

Robot-assisted rehabilitation and training systems are utilized to improve the functional recovery of individuals with mobility limitations. These systems offer structured rehabilitation through precise human-robot interaction, outperforming traditional physical therapy by delivering advantages such as targeted muscle recovery, optimization of walking patterns, and automated training routines tailored to the user's objectives and musculoskeletal attributes. In our research, we propose the development of a walking simulator that considers user-specific musculoskeletal information to replicate natural walking dynamics, accounting for factors like joint angles, muscular forces, internal user-specific constraints, and external environmental factors. The integration of these factors into robot-assisted training can provide a more realistic rehabilitation environment and serve as a foundation for achieving natural bipedal locomotion. Our research team has developed a robot-assisted training platform (RATP) that generates gait training sets based on user-specific internal and external constraints by incorporating a genetic algorithm (GA). We utilize the Lagrangian multipliers to accommodate requirements from the rehabilitation field to instantly reshape the gait patterns while maintaining their overall characteristics, without an additional gait pattern search process. Depending on the patient's rehabilitation progress, there are times when it is necessary to reorganize the training session by changing training conditions such as terrain conditions, walking speed, and joint range of motion. The proposed method allows gait rehabilitation to be performed while stably satisfying ground contact constraints, even after modifying the training parameters., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests., (© 2024. International Federation for Medical and Biological Engineering.)

Background and Objectives: While body weight support (BWS) intonation is vital during conventional gait training of neurologically challenged subjects, it is important to evaluate its effect during robot assisted gait training. In the present research we have studied the effect of BWS intonation on muscle activities during robotic gait training using dynamic simulations., Methods: Two dimensional (2-D) musculoskeletal model of human gait was developed conjointly with another 2-D model of a robotic orthosis capable of actuating hip, knee and ankle joints simultaneously. The musculoskeletal model consists of eight major muscle groups namely; soleus (SOL), gastrocnemius (GAS), tibialis anterior (TA), hamstrings (HAM), vasti (VAS), gluteus maximus (GLU), uniarticular hip flexors (iliopsoas, IP), and Rectus Femoris (RF). BWS was provided at levels of 0, 20, 40 and 60% during the simulations. In order to obtain a feasible set of muscle activities during subsequent gait cycles, an inverse dynamics algorithm along with a quadratic minimization algorithm was implemented., Results: The dynamic parameters of the robot assisted human gait such as joint angle trajectories, ground contact force (GCF), human limb joint torques and robot induced torques at different levels of BWS were derived. The patterns of muscle activities at variable BWS were derived and analysed. For most part of the gait cycle (GC) the muscle activation patterns are quite similar for all levels of BWS as is apparent from the mean of muscle activities for the complete GC., Conclusions: Effect of BWS variation during robot assisted gait on muscle activities was studied by developing dynamic simulation. It is expected that the proposed dynamic simulation approach will provide important inferences and information about the muscle function variations consequent upon a change in BWS during robot assisted gait. This information shall be quite important while investigating the influence of BWS intonation on neuromuscular parameters of interest during robotic gait training.

Introduction/Background Gait training is one of the main objectives in rehabilitation of motor incomplete spinal cord injury (SCI). There are different modalities of locomotion training, being the robotic orthosis among them, and offering until now, positive outcomes. However there is still a lack of evidence of the optimal training characteristics. Objective To determine the effect of two different training programs with robotic gait orthosis for patients with chronic motor incomplete SCI. Material and method Study design: randomized, blinded to the observer, clinical trial. Patients from the National Institute of Rehabilitation (INR) with motor incomplete SCI, with at least 6 months of evolution and who were able to walk, were eligible. Patients were randomly assigned to either one of the two different training groups: 30 minutes or 60 minutes training group. Both groups received a training period of six weeks, five days a week. Assessments of gait pattern analysis with the GaitRite instrument, functional assessment with Spinal Cord Independence measure (SCIM), walking index for spinal cord injury (WISCI) and quality of life with life satisfaction questionnaire (Lisat-9); repeating such evaluations in a 6 and 12 months follow-up. Results In total, 12 participants were studied. After 30 training sessions with the robotic orthosis, both groups had significant improvements in gait as well as in SCIM, LISAT-9 and WISCI. No significant differences between groups were found. Conclusion Robotic orthosis gait training has a positive effect improving gait pattern in incomplete SCI. Until now, there are no significant differences between a 30 minutes and a 60 minutes-training program. [ABSTRACT FROM AUTHOR]

Study Design: Bibliometric analysis. Objective: The study aims to comprehensively assess the literature related to gait rehabilitation for individuals with spinal cord injury (SCI) to identify significant contributors, and to explore the collaborations and emerging themes in the field. Methods: Original and review articles in English using relevant keywords were searched in the Clarivate Web of Science database. The data from the selected articles were imported into R software. Bibliometric indicators were assessed to determine author contributions, country affiliations, journal sources, and thematic trends. Results: A total of 1313 relevant articles were identified. The USA, followed by Canada and Switzerland were the most prolific countries contributing to gait rehabilitation research in SCI. The most relevant journals were Spinal Cord, Archives of Physical Medicine and Rehabilitation, Journal of Spinal Cord Medicine, Journal of NeuroEngineering, and Journal of Neurotrauma. The highest contributions came from Northwestern University, the University of Miami, and the University of Alberta. The analysis revealed an increase in research interest in gait rehabilitation after 2000, with a focus on interdisciplinary approaches and emerging technologies like robotics, exoskeletons, and neuromodulation. Conclusion: The analysis demonstrates the importance of collaborative and interdisciplinary research in gait rehabilitation. The results indicate a shift in research focus from traditional methods to the integration of technology. The impact of publications from the USA and Europe is a notable finding. The study highlights the growth of articles related to technology-driven approaches and understanding neuroplasticity in gait rehabilitation. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) rehabilitation emphasizes locomotion. Robotic-assisted gait training (RAGT) is widely used in clinical settings because of its benefits; however, its efficacy remains controversial. We conducted a systematic review and meta-analysis to investigate the efficacy of RAGT in patients with SCI. We searched international and domestic databases for articles published until April 18, 2024. The meta-analysis employed a random effects model to determine the effect size as either mean difference (MD) or standardized MD (SMD). Evidence quality was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Twenty-three studies with a total of 690 participants were included in the final analysis. The overall pooled effect size for improvement in activities of daily living was 0.24, with SMD (95% confidence interval [95% CI], 0.04–0.43; GRADE: high) favoring RAGT over conventional rehabilitation. Muscular strength (MD, 0.23; 95% CI, 0.02–0.44; GRADE: high), walking index for SCI (MD, 0.31; 95% CI, 0.07–0.55; GRADE: moderate) and 6 min walk test distance (MD, 0.38; 95% CI, 0.14–0.63; GRADE: moderate) showed significant improvement in the robot group. Subgroup analysis revealed that subacute patients and intervention periods >2 months were more effective. This meta-analysis revealed that RAGT significantly improved activities of daily living, muscular strength, and walking abilities. Additional studies are needed to identify the optimal treatment protocol and specific patient groups for which the protocol is most effective. [ABSTRACT FROM AUTHOR]

Gait and balance have a direct impact on patients' independence and quality of life. Due to a higher life expectancy, the number of patients suffering neurological disorders has increased exponentially, with gait and balance impairments being the main side effects. In this context, the use of rehabilitation robotic devices arises as an effective and complementary tool to recover gait and balance functions. Among rehabilitation devices, end effectors present some advantages and have shown encouraging outcomes. The objective of this study is twofold: to propose a general classification of devices for gait and balance rehabilitation and to provide a review of the existing end effectors for such purposes. We classified the devices into five groups: treadmills, exoskeletons, patient-guided systems, perturbation platforms, and end effectors. Overall, 55 end effectors were identified in the literature, of which 16 were commercialized. We found a disproportionate number of end effectors capable of providing both types of rehabilitation (2/55) and those focused on either balance (21/55) or gait (32/55). The analysis of their features from a mechanical standpoint (degrees of freedom, topology, and training mode) allowed us to identify the potential of parallel manipulators as driving mechanisms of end effector devices and to suggest several future research directions. [ABSTRACT FROM AUTHOR]

Cadence or stride frequency is an important parameter being controlled in gait training of neurologically impaired subjects. The aim of this study was to examine the effects of cadence variation on muscle activation patterns during robot assisted unimpaired gait using dynamic simulations. A twodimensional (2-D) musculoskeletal model of human gait was developed considering eight major muscle groups along with existing ground contact force (GCF) model. A 2-D model of a robotic orthosis was also developed which provides actuation to the hip, knee and ankle joints in the sagittal plane to guide subjects limbs on reference trajectories. A custom inverse dynamics algorithm was used along with a quadratic minimization algorithm to obtain a feasible set of muscle activation patterns. Predicted patterns of muscle activations during slow, natural and fast cadence were compared and the mean muscle activations were found to be increasing with an increase in cadence. The proposed dynamic simulation provide important insight into the muscle activation variations with change in cadence during robot assisted gait and provide the basis for investigating the influence of cadence regulation on neuromuscular parameters of interest during robot assisted gait.

Due to the labor-intensiveness and the shortage of therapists in the application of most forms of manually assisted gait training in neuro-rehabilitation, robotics rehabilitation gait systems have been developed to contribute in such fields of neuro-rehabilitation. This paper presents an overground gait rehabilitation robot, which consists of a pair of robotic orthosis (RO) connected to pelvic arm (PA) and mounted on a mobile platform (MP). As the robotic orthosis (RO) is connected to pelvic arm (PA), the compensation between the two modules should be considered. Possible problems that may surface during overground walking include: 1. Ground impact problem. 2. Poor coordination to mobile platform (MP) which would cause foot drag. To reduce, if not to avoid, the ground impact and dragging of the footpad occurring during the course of overground walking with help of the device, a gait motion generation is developed and specified to adjust the gait patterns for an impactless and smooth footpad contact. The initial tests demonstrate the effectiveness and smoothness of the overground walking on the system., (© 2011 IEEE)

Objective: The goal of this study was to compare the muscle activation patterns in various major leg muscles during treadmill ambulation with those exhibited during robotic-assisted walking., Background: Robotic devices are now being integrated into neurorehabilitation programs with promising results. The influence of these devices on altering naturally occurring muscle activation patterns utilized during walking have not been quantified., Methods: Muscle activity measured during 60 s of walking was broken up into individual stride cycles, averaged, and normalized. The stride cycle was then broken up into seven distinct phases and the integrated muscle activity during each phase was compared between treadmill and robotic-assisted walking using a multi-factor ANOVA., Results: Significant differences in the spatial and temporal muscle activation patterns were observed across various portions of the gait cycle between treadmill and robotic-assisted walking. Activity in the quadriceps and hamstrings was significantly higher during the swing phase of Lokomat walking than treadmill walking, while activity in the ankle flexor and extensor muscles was reduced throughout most of the gait cycle in the Lokomat., Conclusions: Walking within a robotic orthosis that limits the degrees of freedom of leg and pelvis movement leads to changes in naturally occurring muscle activation patterns., Relevance: An understanding of how robotic-assisted walking alters muscle activation patterns is necessary clinically in order to establish baseline patterns against which subject's with neurological disorders can be compared. Furthermore, this information will guide further developments in robotic devices targeting gait training.

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. [ABSTRACT FROM AUTHOR]

Objective: To measure muscle activity before and after robot-assisted gait training (RAGT) in patients with stroke and examine the differences in muscle activity changes compared with conventional gait training (CGT). Methods: Thirty patients with stroke (RAGT group, n=17; CGT group, n=13) participated in the study. All patients underwent RAGT using a footpad locomotion interface or CGT for 20 minutes for a total of 20 sessions. Outcome measures were lower-limb muscle activity and gait speed. Measurements were performed before the start of the intervention and after the end of the 4-week intervention. Results: The RAGT group showed increased muscle activity in the gastrocnemius, whereas the CGT group showed high muscle activity in the rectus femoris. In the terminal stance of the gait cycle, the gastrocnemius, the increase in muscle activity was significantly higher in the RAGT group than in the CGT group. Conclusion: The results suggest that RAGT with end-effector type is more effective than CGT to increase the gastrocnemius muscle activity. [ABSTRACT FROM AUTHOR]

Background: Wearable devices for robot-assisted gait training (RAGT) provide overground gait training for the rehabilitation of neurological injuries. We aimed to evaluate the effectiveness and safety of RAGT in patients with a neurologic deficit. Methods: Twenty-eight patients receiving more than ten sessions of overground RAGT using a joint-torque-assisting wearable exoskeletal robot were retrospectively analyzed in this study. Nineteen patients with brain injury, seven patients with spinal cord injury and two patients with peripheral nerve injury were included. Clinical outcomes, such as the Medical Research Council scale for muscle strength, Berg balance scale, functional ambulation category, trunk control tests, and Fugl–Meyer motor assessment of the lower extremities, were recorded before and after RAGT. Parameters for RAGT and adverse events were also recorded. Results: The Medical Research Council scale scores for muscle strength (36.6 to 37.8), Berg balance scale (24.9 to 32.2), and functional ambulation category (1.8 to 2.7) significantly improved after overground RAGT (p < 0.05). The familiarization process was completed within six sessions of RAGT. Only two mild adverse events were reported. Conclusions: Overground RAGT using wearable devices can improve muscle strength, balance, and gait function. It is safe in patients with neurologic injury. [ABSTRACT FROM AUTHOR]

Copyright of Al-Nahrain Journal for Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern rehabilitation processes for neurological patients have been widely assisted by robotic structures, with continuous research and improvements. The use of robotic assistance in rehabilitation is a consolidated technique for upper limb training sessions. However, human gait robotic rehabilitation still needs further research and development. Based on that, this paper deals with the development of a novel active body weight support (BWS) system integrated with a serious game for poststroke patients. This paper starts with a brief review of the state of the art of applied technologies for gait rehabilitation. Next, it presents the obtained mathematical model followed by multibody synthesis techniques and meta-heuristic optimization to the proposed device. The control of the structure is designed using proportional integral derivative (PID) controllers tuned with meta-heuristic optimization and associated with a suppression function to perform assist-as-needed actions. Then, the prototype is integrated with a serious game designed specifically for this application. Finally, a pilot study is conducted with the structure and healthy volunteers. The results obtained show that the mobility of the novel BWS is as expected and the proposed system potentially offers a novel tool for gait training. [ABSTRACT FROM AUTHOR]

Background: A vital objective to treat people with cerebral palsy (CP) is to increase gait velocity and improve gross motor function. This study aimed to evaluate the relative effectiveness of gait training interventions for persons with CP. Methods: Studies published up to October 26, 2022 were searched from four electronic databases [includingMedline (via PubMed), Web of Science, Embase and Cochrane]. Studies with randomized controlled trials (RCTs), people with CP, comparisons of different gait training interventions and outcomes of gait velocity and gross motor function measures (GMFM) were included in this study. The quality of the literature was evaluated using the risk of bias tool in the Cochrane Handbook, the extracted data were analyzed through network meta-analysis (NMA) using Stata16.0 and RevMan5.4 software. Results: Twenty RCTs with a total of 516 individuals with CP were included in accordance with the criteria of this study. The results of the NMA analysis indicated that both external cues treadmill training (ECTT) [mean difference (MD) = 0.10, 95% confidence interval CI (0.04, 0.17), P < 0.05] and partial body weight supported treadmill training (BWSTT) [MD = 0.12, 95% CI (0.01, 0.23), P < 0.05] had better gait velocity than over ground gait training (OGT), BWSTT [MD = 0.09, 95%CI(0.01,0.18), P < 0.05] had a better gait velocity than robot-assisted gait training (RAGT), BWSTT [MD = 0.09, 95% CI (0.06, 0.13) P < 0.05] had a better gait velocity than treadmill training (TT), and BWSTT [MD = 0.14, 95% CI (0.07, 0.21), P < 0.05] had a better gait velocity than conventional physical therapy (CON). The SUCRA ranking indicated that BWSTT optimally improved the gait velocity, and the other followed an order of BWSTT (91.7%) > ECTT (80.9%) > RAGT (46.2%) > TT (44%) > OGT (21.6%) > CON (11.1%). In terms of GMFM, for dimension D (GMFM-D), there was no statistical difference between each comparison; for dimension E (GMFM-E), RAGT [MD = 10.45, 95% CI (2.51, 18.40), P < 0.05] was significantly more effective than CON. Both SUCRA ranking results showed that RAGT improved GMFM-D/E optimally, with rankings of RAGT (69.7%) > TT (69.3%) > BWSTT (67.7%) > OGT (24%) > CON (20.3%), and RAGT (86.1%) > BWSTT (68.2%) > TT (58%) > CON (20.1%) > OGT (17.6%) respectively. Conclusion: This study suggested that BWSTT was optimal in increasing the gait velocity and RAGT was optimal in optimizing GMFM in persons with CP. Impacted by the limitations of the number and quality of studies, randomized controlled trials with larger sample sizes, multiple centers, and high quality should be conducted to validate the above conclusion. Further studies will be required to focus on the total duration of the intervention, duration and frequency of sessions, and intensity that are optimal for the promotion of gait ability in this population. [ABSTRACT FROM AUTHOR]

Fatigue is a common phenomenon in sports and affects sports performance. The production of fatigue during running increases the risk of sports-related injury. People with high physical demands, such as construction workers, soldiers and athletes, are often in a state of muscle fatigue, which may have an adverse effect on health and safety. It is necessary to take effective preventive measures when muscle fatigue occurs. In this paper, a wearable system for monitoring hip dynamics during human walking is proposed, and a machine learning method is used to evaluate fatigue level. The fatigue level of each subject was determined by monitoring the percentage of maximum oxygen uptake. Different percentages of oxygen uptake correspond to different exercise levels. The hip joint angle sensor used herein can sense real-time changes in the angle of the human hip joint, and the data can be used to objectively evaluate the fatigue level of the human body to reduce the risk of running-related overuse injuries. This system can be applied to a human exoskeleton device without increasing the burden on the wearer. [ABSTRACT FROM AUTHOR]

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The concept of synergy has drawn attention and been applied to lower limb assistive devices such as exoskeletons and prostheses for improving human–machine interaction. A better understanding of the influence of gait kinematics on synergies and a better synergy-modeling method are important for device design and improvement. To this end, gait data from healthy, amputee, and stroke subjects were collected. First, continuous relative phase (CRP) was used to quantify their synergies and explore the influence of kinematics. Second, long short-term memory (LSTM) and principal component analysis (PCA) were adopted to model interlimb synergy and intralimb synergy, respectively. The results indicate that the limited hip and knee range of motions (RoMs) in stroke patients and amputees significantly influence their synergies in different ways. In interlimb synergy modeling, LSTM (RMSE: 0.798° (hip) and 1.963° (knee)) has lower errors than PCA (RMSE: 5.050° (hip) and 10.353° (knee)), which is frequently used in the literature. Further, in intralimb synergy modeling, LSTM (RMSE: 3.894°) enables better synergy modeling than PCA (RMSE: 10.312°). In conclusion, stroke patients and amputees perform different compensatory mechanisms to adapt to new interlimb and intralimb synergies different from healthy people. LSTM has better synergy modeling and shows a promise for generating trajectories in line with the wearer's motion for lower limb assistive devices. [ABSTRACT FROM AUTHOR]

Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis—PAFO). Several studies have forecasted healthy gait trajectories, but, to the best of our knowledge, none have forecasted gait trajectories of children with pathological gait yet. These exhibit higher inter- and intra-subject variability compared to typically developing gait of healthy subjects. Pathological trajectories represent the typical gait patterns that rehabilitative exoskeletons and actuated orthoses would target. In this study, we implemented two deep learning models, a Long-Term Short Memory (LSTM) and a Convolutional Neural Network (CNN), to forecast hip, knee, and ankle trajectories in terms of corresponding Euler angles in the pitch, roll, and yaw form for children with neurological disorders, up to 200 ms in the future. The deep learning models implemented in our study are trained on data (available online) from children with neurological disorders collected by Gillette Children's Speciality Healthcare over the years 1994–2017. The children's ages range from 4 to 19 years old and the majority of them had cerebral palsy (73%), while the rest were a combination of neurological, developmental, orthopaedic, and genetic disorders (27%). Data were recorded with a motion capture system (VICON) with a sampling frequency of 120 Hz while walking for 15 m. We investigated a total of 35 combinations of input and output time-frames, with window sizes for input vectors ranging from 50–1000 ms, and output vectors from 8.33–200 ms. Results show that LSTMs outperform CNNs, and the gap in performance becomes greater the larger the input and output window sizes are. The maximum difference between the Mean Absolute Errors (MAEs) of the CNN and LSTM networks was 0.91 degrees. Results also show that the input size has no significant influence on mean prediction errors when the output window is 50 ms or smaller. For output window sizes greater than 50 ms, the larger the input window, the lower the error. Overall, we obtained MAEs ranging from 0.095–2.531 degrees for the LSTM network, and from 0.129–2.840 degrees for the CNN. This study establishes the feasibility of forecasting pathological gait trajectories of children which could be integrated with exoskeleton control systems and experimentally explores the characteristics of such intelligent systems under varying input and output window time-frames. [ABSTRACT FROM AUTHOR]

Featured Application: This paper investigates the effectiveness of using an automatic NDT trainer for the clinical rehabilitation of stroke patients. This paper presents a clinical rehabilitation protocol for stroke patients using a movable trainer, which can automatically execute a neurodevelopmental treatment (NDT) intervention based on key gait events. The trainer consists of gait detection and motor control systems. The gait detection system applied recurrent neural networks (RNNs) to recognize important gait events in real time to trigger the motor control system to repeat the NDT intervention. This paper proposes a modified intervention method that simultaneously improves the user's gait symmetry and pelvic rotation. We recruited ten healthy subjects and had them wear a rehabilitation gaiter on one knee joint to mimic stroke gaits for verification of the effectiveness of the trainer. We used the RNN model and a modified intervention method to increase the trainer's effectiveness in improving gait symmetry and pelvic rotation. We then invited ten stroke patients to participate in the experiments, and we found improvement in gait symmetry in 80% and 90% of the patients during and after the training, respectively. Similarly, pelvic rotation improved in 80% of the patients during and after the training. These findings confirmed that the movable NDT trainer could improve gait performance for the rehabilitation of stroke patients. [ABSTRACT FROM AUTHOR]

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Introduction: Disturbances of balance control are common after stroke, affecting the quality of gait and increasing the risk of falls. Because balance and gait disorders may persist also in the chronic stage, reducing individual independence and participation, they represent primary goals of neurorehabilitation programs. For this purpose, in recent years, numerous technological devices have been developed, among which one of the most widespread is the Lokomat®, an actuated exoskeleton that guide the patient's limbs, simulating a symmetrical bilateral gait. Preliminary evidence suggests that beyond gait parameters, robotic assisted gait training may also improve balance. Therefore, the aim of this systematic review was to summarize evidence about the effectiveness of Lokomat® in improving balance in stroke patients. Methods: Randomized controlled trials published between January 1989 and August 2020, comparing Lokomat® training to conventional therapy for stroke patients, were retrieved from seven electronic databases. Balance, assessed by means of validated clinical scales, was considered as outcome measure. The Physiotherapy Evidence Database (PEDro) scale was used to evaluate the methodological quality of the studies. The study protocol was registered on PROSPERO (no. CRD42020197531). Results: After the removal of the duplicates, according to the inclusion criteria, 13 studies were selected, involving 445 subacute or chronic stroke patients. Eleven papers contributed to three meta-analyses. Favorable results for recovery of balance in stroke survivors treated with Lokomat® were shown using Timed Up and Go (pooled mean difference = −3.40, 95% CI −4.35 to −2.44; p < 0.00001) and Rivermead Mobility Index as outcome measures (pooled mean difference = 0.40, 95% CI 0.26–0.55; p < 0.00001). Inconclusive results were found when balance was measured by means of the Berg Balance Scale (pooled mean difference = 0.17, 95% CI −0.26 to 0.60; p = 0.44). Conclusions: Overall, most studies have shown beneficial effects of Lokomat® on balance recovery for stroke survivors, at least comparable to conventional physical therapy. However, due to the limited number of studies and their high heterogeneity, further research is needed to draw more solid and definitive conclusions. [ABSTRACT FROM AUTHOR]

Background: Compared with traditional physical therapy for stroke patients, lower extremity exoskeletons can provide patients with greater endurance and more repeatable and controllable training, which can reduce the therapeutic burden of the therapist. However, most exoskeletons are expensive, heavy or require active power to be operated. Therefore, a lighter, easy to wear, easy to operate, low-cost technology for stroke rehabilitation would be a welcome opportunity for stroke survivors, caregivers and clinicians. One such device is the Kickstart Walk Assist system and the purpose of this study was to determine feasibility of using this unpowered exoskeleton device in a sample of stroke survivors.Methods: Thirty stroke survivors were enrolled in the study and experienced walking with the Kickstart exoskeleton device that provided spring-loaded assistance during gait. After 5 days of wearing the exoskeleton, participants were evaluated in the two states of wearing and not wearing the exoskeleton. Outcome measures included: (a) spatio-temporal gait measures, (b) balance measures and (c) exoskeleton-use feedback questionnaire.Results: In comparison to not wearing the device, when participants wore the Kickstart walking system, weight bearing asymmetry was reduced. The time spent on the 10-m walk test was also reduced, but there was no difference in the timed-up-and-go test (TUGT). Gait analysis data showed reduction in step time and double support time. Stroke survivors were positive about the Kickstart walking system's ability to improve their balance, speed and gait. In addition, their confidence level and willingness to use the device was also positive.Conclusions: These findings show the feasibility of using the Kickstart walking system for improving walking performance in stroke survivors. Our future goal is to perform a longer duration study with more comprehensive pre- and post-testing in a larger sample of stroke survivors. Trial registration Chinese Clinical Trial Registry, ChiCTR2000032665. Registered 5 May 2020-Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=53288. [ABSTRACT FROM AUTHOR]

Purpose: The Lokomat is a commercially available robotic gait trainer, applied for gait rehabilitation in post-stroke hemiparetic patients. Selective and well-dosed clinical use of the Lokomat training parameters, i.e. guidance, speed and bodyweight support, requires a good understanding of how these parameters affect the neuromuscular control of post-stroke hemiparetic gait. Materials and methods: Ten stroke patients (unilateral paresis, 7 females, 64.5 ± 6.4 years, >3months post-stroke, FAC scores 2–4)) walked in the Lokomat under varying parameter settings: 50% or 100% guidance, 0.28 or 0.56m/s, 0% or 50% bodyweight support. Electromyography was recorded bilaterally from Gluteus Medius, Biceps Femoris, Vastus Lateralis, Medial Gastrocnemius, and Tibialis Anterior. Pressure sensors placed under the feet were used to determine the level of temporal gait symmetry. Results: Varying guidance and bodyweight support had little effect on muscle activity, but increasing treadmill speed led to increased activity in both the affected (Biceps Femoris, Medial Gastrocnemius, Tibialis Anterior) and unaffected leg (all muscles). The level of temporal symmetry was unaffected by the parameter settings. Conclusions: The Lokomat training parameters are generally ineffective in shaping short term muscle activity and step symmetry patients with hemiparetic stroke, as speed is the only parameter that significantly affects muscular amplitude. Trial Registration: d.n.a. The Lokomat is a commercially available gait trainer that can be used for gait rehabilitation in post-stroke hemiparetic patients. This study shows that muscle amplitude is generally low during Lokomat guided walking, and that treadmill Speed is the main training parameter to influence muscular output in stroke patients during Lokomat walking. Varying Guidance and Bodyweight Support within a clinical relevant range barely affected muscle activity, and temporal step symmetry was unaffected by variation in any of the training parameters. Based on the findings it is advised to increase speed as early as possible during Lokomat therapy, or use other means (e.g. feedback or instructions) to stimulate active involvement of patients during training. [ABSTRACT FROM AUTHOR]

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Background: Asymmetric gait post-stroke is associated with decreased mobility, yet individuals with chronic stroke often self-select an asymmetric gait despite being capable of walking more symmetrically. The purpose of this study was to test whether self-selected asymmetry could be explained by energy cost minimization. We hypothesized that short-term deviations from self-selected asymmetry would result in increased metabolic energy consumption, despite being associated with long-term rehabilitation benefits. Other studies have found no difference in metabolic rate across different levels of enforced asymmetry among individuals with chronic stroke, but used methods that left some uncertainty to be resolved.Methods: In this study, ten individuals with chronic stroke walked on a treadmill at participant-specific speeds while voluntarily altering step length asymmetry. We included only participants with clinically relevant self-selected asymmetry who were able to significantly alter asymmetry using visual biofeedback. Conditions included targeting zero asymmetry, self-selected asymmetry, and double the self-selected asymmetry. Participants were trained with the biofeedback system in one session, and data were collected in three subsequent sessions with repeated measures. Self-selected asymmetry was consistent across sessions. A similar protocol was conducted among unimpaired participants.Results: Participants with chronic stroke substantially altered step length asymmetry using biofeedback, but this did not affect metabolic rate (ANOVA, p = 0.68). In unimpaired participants, self-selected step length asymmetry was close to zero and corresponded to the lowest metabolic energy cost (ANOVA, p = 6e-4). While the symmetry of unimpaired gait may be the result of energy cost minimization, self-selected step length asymmetry in individuals with chronic stroke cannot be explained by a similar least-effort drive.Conclusions: Interventions that encourage changes in step length asymmetry by manipulating metabolic energy consumption may be effective because these therapies would not have to overcome a metabolic penalty for altering asymmetry. [ABSTRACT FROM AUTHOR]

The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool. [ABSTRACT FROM AUTHOR]

Individuals with cerebral palsy can have weak and poorly coordinated ankle plantar flexor muscles that contribute to inefficient walking patterns. Previous studies attempting to improve plantar flexor function have had inconsistent effects on mobility, likely due to a lack of task-specificity. The goal of this study was to develop, validate, and test the feasibility and neuromuscular response of a novel wearable adaptive resistance platform to increase activity of the plantar flexors during the propulsive phase of gait. We recruited eight individuals with spastic cerebral palsy to walk with adaptive plantar flexor resistance provided from an untethered exoskeleton. The resistance system and protocol was safe and feasible for all of our participants. Controller validation demonstrated our ability to provide resistance that proportionally- and instantaneously-adapted to the biological ankle moment (R = 0.92 ± 0.04). Following acclimation to resistance (0.16 ± 0.02 Nm/kg), more-affected limbs exhibited a 45 ± 35% increase in plantar flexor activity (p = 0.02), a 26 ± 24% decrease in dorsiflexor activity (p < 0.05), and a 46 ± 25% decrease in co-contraction (tibialis anterior and soleus) (p = 0.02) during the stance phase. This adaptive resistance system warrants further investigation for use in a longitudinal intervention study. [ABSTRACT FROM AUTHOR]

Background: Locomat is a robotic exoskeleton providing guidance force and bodyweight support to facilitate intensive walking training for people with stroke. Although the Locomat has been reported to be effective in improving walking performance, the effects of training parameters on the neuromuscular control remain unclear. This study aimed to compare the muscle activities between Locomat walking and treadmill walking at a normal speed, as well as to investigate the effects of varying bodyweight support and guidance force on muscle activation patterns during Locomat walking in people with stroke.Methods: A cross-sectional study design was employed. Participants first performed an unrestrained walking on a treadmill and then walked in the Locomat with different levels of bodyweight support (30% or 50%) and guidance force (40% or 70%) at the same speed (1.2 m/s). Surface electromyography (sEMG) of seven muscles of the affected leg was recorded. The sEMG envelope was time-normalised and averaged over gait cycles. Mean sEMG amplitude was then calculated by normalising the sEMG amplitude with respect to the peak amplitude during treadmill walking for statistical analysis. A series of Non-parametric test and post hoc analysis were performed with a significance level of 0.05.Results: Fourteen participants with stroke were recruited at the Yangzhi Affiliated Rehabilitation Hospital of Tongji University (female n = 1; mean age 46.1 ± 11.1 years). Only the mean sEMG amplitude of vastus medialis oblique during Locomat walking (50% bodyweight support and 70% guidance force) was significantly lower than that during treadmill walking. Reducing both bodyweight and guidance increased muscle activity of gluteus medius and tibialis anterior. Activity of vastus medialis oblique muscle increased as bodyweight support reduced, while that of rectus femoris increased as guidance force decreased.Conclusions: The effects of Locomat on reducing muscle activity in people with stroke were minimized when walking at a normal speed. Reducing bodyweight support and guidance force increased the activity of specific muscles during Locomat walking. Effects of bodyweight support, guidance force and speed should be taken into account when developing individualized Locomat training protocols for clients with stroke. [ABSTRACT FROM AUTHOR]

This pilot study investigated the efficacy of a four week robot-assisted gait training in twelve children with spastic diparesis. Short-term results and a 3-month follow-up showed statistically significantly increased selective motor control, walking farther distances, gross motor score, and decreased joint contractures. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To investigate combined effects of robot‐assisted training (RAT) and physical therapy versus physical therapy only on balance and gait function of chronic stroke patients after botulinum toxin‐A (BoNT‐A) treatment. Methods: Forty‐eight chronic stroke patients, received BoNT‐A treatment for lower extremity spastic muscles, were randomly assigned to RAT (n = 32) and control (n = 16) groups in a 2:1 ratio. RAT group received 30 min of RAT (RoboGait®) plus 60 min of physical therapy, whereas controls received 90 min of physical therapy for 3 weeks during weekdays. Outcome assessments were measured at baseline and post‐treatment Weeks 6 and 12. Spasticity was assessed by Modified Ashworth Scale and Tardieu Scale. Balance and gait functions were assessed by Berg Balance Scale, Timed Up and Go test, and Rivermead Visual Gait Assessment. Results: Demographic and baseline characteristics were similar in both groups (p > .05) for all parameters. Significant improvements were determined in both RAT and control groups regarding spasticity, balance, and gait functions after treatment. However, at post‐treatment Weeks 6 and 12, change from baseline Timed Up and Go test (p = .003 and p = .002, respectively), Berg Balance Scale (p = .001 and p < .001, respectively), and Rivermead Visual Gait Assessment (p < .001 and p < .001, respectively) were significantly higher in RAT group than those of the control group. Conclusion: Integrated treatment with RAT and physical therapy might provide additional benefits in chronic stroke patients whom spasticity was treated by BoNT‐A. [ABSTRACT FROM AUTHOR]