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

Training intensity of robot-assisted gait training in children with cerebral palsy

AIM

We compared three different intensities of robot-assisted gait training (RAGT) for achieving favourable outcomes in children with cerebral palsy (CP).

METHOD

This study was conducted using a randomized controlled, single-blind design. Thirty children (19 males and 11 females; mean age 6 years 1 month, SD 2 years) with CP classified in Gross Motor Function Classification System levels II and III were assigned to three different RAGT intensity groups: high-intensity (fastest walking speed and lowest body weight support [BWS]), low-intensity (slowest speed and highest BWS), and comfortable intensity (intermediate speed and intermediate BWS). The RAGT intervention was performed three times a week for 6 weeks. Outcome measures included the 88-item Gross Motor Function Measure, stability index, spatiotemporal parameters of gait analysis, paediatric functional independence measure, and the Canadian Occupational Performance Measure.

RESULTS

The 88-item Gross Motor Function Measure was significantly improved after training in the high-intensity (D Δ8.3 ± 15.6; E Δ3.8 ± 4.1) and comfortable intensity (D Δ2.9 ± 3.1; E Δ1.2 ± 2.0) groups, whereas gait speed was improved in the comfortable intensity group, without statistically significant group differences. Only the low-intensity group showed improvement on the stability index (Δ -0.6 ± 0.9, p = 0.05). Everyday functional performance significantly improved in all three groups, with the comfortable intensity group showing the greatest improvement.

INTERPRETATION

Different training intensities produced improvement in different areas; individualized RAGT intensity adjustment is therefore needed based on the rehabilitation goal.

Clinical indications and protocol considerations for selecting initial body weight support levels in gait rehabilitation: a systematic review

BACKGROUND

Body weight support (BWS) training devices are frequently used to improve gait in individuals with neurological impairments, but guidance in selecting an appropriate level of BWS is limited. Here, we aim to describe the initial BWS levels used during gait training, the rationale for this selection and the clinical goals aligned with BWS training for different diagnoses.

METHOD

A systematic literature search was conducted in PubMed, Embase and Web of Science, including terms related to the population (individuals with neurological disorders), intervention (BWS training) and outcome (gait). Information on patient characteristics, type of BWS device, BWS level and training goals was extracted from the included articles.

RESULTS

Thirty-three articles were included, which described outcomes using frame-based (stationary or mobile) and unidirectional ceiling-mounted devices on four diagnoses (multiple sclerosis (MS), spinal cord injury (SCI), stroke, traumatic brain injury (TBI)). The BWS levels were highest for individuals with MS (median: 75%, IQR: 6%), followed by SCI (median: 40%, IQR: 35%), stroke (median: 30%, IQR: 4.75%) and TBI (median: 15%, IQR: 0%). The included studies reported eleven different training goals. Reported BWS levels ranged between 30 and 75% for most of the training goals, without a clear relationship between BWS level, diagnosis, training goal and rationale for BWS selection. Training goals were achieved in all included studies.

CONCLUSION

Initial BWS levels differ considerably between studies included in this review. The underlying rationale for these differences was not clearly motivated in the included studies. Variation in study designs and populations does not allow to draw a conclusion on the effectiveness of BWS levels. Hence, it remains difficult to formulate guidelines on optimal BWS settings for different diagnoses, BWS devices and training goals. Further efforts are required to establish clinical guidelines and to experimentally investigate which initial BWS levels are optimal for specific diagnoses and training goals.

Robot-assisted gait training improves walking and cerebral connectivity in children with unilateral cerebral palsy

BACKGROUND

Robot-assisted gait training (RAGT) is promising to help walking rehabilitation in cerebral palsy, but training-induced neuroplastic effects have little been investigated.

METHODS

Forty unilateral cerebral palsy children aged 4-18 years were randomly allocated in a monocentric study to ten 20-minute RAGT sessions with the G-EO system, five days a week (n = 20) or to a control group (who continued conventional care with six 30-minute physiotherapy sessions, three days a week) (n = 20), two weeks running, from September 2020 to December 2021. Clinical and MRI outcomes were compared before and one month after therapy. The primary outcome was gait speed. Secondary outcomes were a 6-minute walking test distance, Gross Motor Function Measure-88 (GMFM-88) dimensions D and E, Patient Global Impression of Improvement, resting-state functional connectivity within the sensorimotor network, and structural connectivity in the corticospinal tracts.

RESULTS

Gait speed and the 6-minute walking test distance improved more after RAGT. Resting-state functional connectivity increased after RAGT but decreased in controls between superior and lateral healthy or lateral injured sensorimotor networks. GMFM-88 and structural connectivity in corticospinal tracts were unchanged. Impression of improvement in children was better after RAGT.

CONCLUSION

Short-term benefit of repetitive RAGT on walking abilities and functional cerebral connectivity was found in unilateral cerebral palsy children.

IMPACT STATEMENT

Short-term repetitive robot-assisted gait training improves gait speed and walking resistance and increases cerebral functional connectivity in unilateral cerebral palsy. GMFM dimensions D and E were unchanged after short-term repetitive robot-assisted gait training in unilateral cerebral palsy.

Effects of non-pharmacological interventions on gait and balance of persons with Multiple Sclerosis: A narrative review

BACKGROUND

Multiple Sclerosis (MS) is among the most common reasons for disability in young adults. Mobility impairment, primarily related to gait and balance, is ranked as the preeminent concern among persons with MS (PwMS). Gait and balance dysfunction can directly affect the quality of life and activities of daily life in PwMS, hence the importance of effective treatment strategies. Previous studies have demonstrated the positive effect of various non-pharmacological rehabilitation methods, including physiotherapy and electrical stimulation, on gait and mobility in PwMS. Non-pharmacological methods can be tailored to the individual needs and abilities of each patient, allowing healthcare providers to create personalized training programs. Furthermore, these methods typically result in minimal or no side effects.

PURPOSE

This review provides a comprehensive overview of an array of non-pharmacological treatment approaches aimed at enhancing ambulatory performance in PwMS.

METHODS

We performed a narrative review of the original papers available in PubMed, investigating the effects of different nonmedical approaches on the gait and balance performance of the PwMS. Reviewed treatment approaches include "exercise, physical rehabilitation, dual-task (DT) rehabilitation, robot-assisted rehabilitation, virtual reality-assisted rehabilitation, game training, electrical stimulation devices, auditory stimulation, visual feedback, and shoe insoles".

RESULTS AND CONCLUSIONS

Eighty articles were meticulously reviewed. Our study highlights the positive effects of non-pharmacological interventions on patients' quality of life, reducing disability, fatigue, and muscle spasticity. While some methods, including exercise and physiotherapy, showed substantial promise, further research is needed to evaluate whether visual biofeedback and auditory stimulation are preferable over conventional approaches. Additionally, approaches such as functional electrical stimulation, non-invasive brain stimulation, and shoe insoles demonstrate substantial short-term benefits, prompting further investigation into their long-term effects. Non-pharmacological interventions can serve as a valuable complement to medication-based approaches.

1D-Convolutional Neural Networks can Quantify Therapy Content of Children and Adolescents Walking in a Robot-Assisted Gait Trainer

Therapy content, consisting of device parameter settings and therapy instructions, is crucial for an effective robot-assisted gait therapy program. Settings and instructions depend on the therapy goals of the individual patient. While device parameters can be recorded by the robot, therapeutic instructions and associated patient responses are currently difficult to capture. This limits the transferability of successful therapeutic approaches between clinics. Here, we propose that 1D-convolutional neural networks can be used to relate patient behavior during individual steps to the instructions given as a surrogate for the patient's intent. Our model takes the surface electromyography patterns of two leg muscles as input and predicts the given instruction as output. We tested this approach with data from 20 healthy children walking in a robot-assisted gait trainer with 5 different instructions. Our model performs well, with a classification accuracy of almost 90%, when the instruction targets specific aspects of gait, such as step length. This shows that 1D-convolutional neural networks are a viable tool for quantifying therapy content. Thus, they could help compare therapy approaches and identify effective strategies.

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

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

Adaptive walking assistance does not impact long-range stride-to-stride autocorrelations in healthy people

Many studies have demonstrated in the past that the level of long-range autocorrelations in series of stride durations, characterizing natural gait variability, is impacted by external constraints, such as treadmill or metronome, or by pathologies, such as Parkinson's or Huntington's disease. Nevertheless, no one has analyzed the effects on this metric of a gait constrained by a robot-mediated walking assistance, which intrinsically tends to normalize the gait pattern. This paper focuses on the influence of a wearable active pelvis orthosis on the level of long-range autocorrelations in series of stride durations. Ten healthy participants, aged between 55 and 77 yr, performed four overground walking sessions, wearing this orthosis, and with different assistive parameters. This study showed that the adaptive assistance provided by this device has the potential of improving gait metrics that are typically affected by aging, such as the hip range of motion, walking speed, stride length, and stride duration, without impacting natural gait variability, i.e., the level of long-range autocorrelations in series of stride durations. This combination is virtuous toward the design of an assistive device for people with locomotion disorders resulting in deteriorated levels of long-range autocorrelations, such as patients with Parkinson's disease.NEW & NOTEWORTHY This study is the first that investigates the effects of a wearable active pelvis orthosis using an oscillator-based adaptive assistance on the level of long-range autocorrelations in series of stride durations during overground walking. It is also the first to compare the effects of different assistance settings on spatiotemporal gait metrics.

Within- and between-therapist agreement on personalized parameters for robot-assisted gait therapy: the challenge of adjusting robotic assistance

BACKGROUND

Stationary robotic gait trainers usually allow for adjustment of training parameters, including gait speed, body weight support and robotic assistance, to personalize therapy. Consequently, therapists personalize parameter settings to pursue a relevant therapy goal for each patient. Previous work has shown that the choice of parameters influences the behavior of patients. At the same time, randomized clinical trials usually do not report the applied settings and do not consider them in the interpretation of their results. The choice of adequate parameter settings therefore remains one of the major challenges that therapists face in everyday clinical practice. For therapy to be most effective, personalization should ideally result in repeatable parameter settings for repeatable therapy situations, irrespective of the therapist who adjusts the parameters. This has not yet been investigated. Therefore, the aim of the present study was to investigate the agreement of parameter settings from session to session within a therapist and between two different therapists in children and adolescents undergoing robot-assisted gait training.

METHODS AND RESULTS

Fourteen patients walked in the robotic gait trainer Lokomat on 2 days. Two therapists from a pool of 5 therapists independently personalized gait speed, bodyweight support and robotic assistance for a moderately and a vigorously intensive therapy task. There was a very high agreement within and between therapists for the parameters gait speed and bodyweight support, but a substantially lower agreement for robotic assistance.

CONCLUSION

These findings imply that therapists perform consistently at setting parameters that have a very clear and visible clinical effect (e.g. walking speed and bodyweight support). However, they have more difficulties with robotic assistance, which has a more ambiguous effect because patients may respond differently to changes. Future work should therefore focus on better understanding patient reactions to changes in robotic assistance and especially on how instructions can be employed to steer these reactions. To improve the agreement, we propose that therapists link their choice of robotic assistance to the individual therapy goals of the patients and closely guide the patients during walking with instructions.

Safety, Feasibility and Efficacy of Lokomat® and Armeo®Spring Training in Deconditioned Paediatric, Adolescent and Young Adult Cancer Patients

BACKGROUND

Rehabilitation robotics is a field of study which aims to understand and augment rehabilitation through the use of robotics devices.

OBJECTIVE

This proof of concept study aimed to test the safety (no. adverse events, incidence of infection), feasibility (program demand, adherence, participant satisfaction) and efficacy (Peak Oxygen uptake (VO_2peak), 6-min walk test, gait speeds, Canadian Occupational Performance Measure, quality of life) of Lokomat^® and Armeo^®Spring training in children and adolescents and young adults (AYAs) during or soon after cancer treatment.

METHOD

This was a 6-week single arm pre-post study. Cancer patients with significant musculoskeletal, neurological, gait and/or upper limb deficiency aged 5 to 25 years were recruited. The rehabilitation program included access to two robotic orthoses: the Lokomat^® and/or Armeo^®Spring. Robotic devices utilised real-time biofeedback and computer games to engage and guide participants through a repetitive functional range of movement aimed at improving functional deficiencies. Progressive increases in exercise intensity and duration were encouraged.

RESULTS

Twentey-eight participants were approached for study; twenty-one consented. Seventy-six percent completed the six-week intervention with an overall adherence of 83%. The mean participant satisfaction score was 8.8/10. Forty-nine adverse events were recorded throughout the course of the study, forty-five grade 1, three grade 2 and one grade 3. No adverse events led to withdrawal from the study. Preliminary efficacy results indicate large beneficial effects on VO_2peak (r = 0.63), 10 m comfortable pace walk (r = 0.51) and maximal pace walk (r = 0.60), 6-min walk test (r = 0.60), maximal back and leg strength (r = 0.71), trunk flexibility (r = 0.60), The European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ C30) (r = 0.61), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT F) r = 0.53 and the Canadian Occupational Performance Measure, satisfaction (r = 0.88) and performance scores (r = 0.83), and moderate beneficial effects on Leisure Score Index (LSI) (r = 0.30).

CONCLUSION

Our results suggest that Lokomat^® and Armeo^®Spring training is safe and feasible for use in children and AYAs who are currently undergoing or have recently completed cancer therapy. A larger controlled trial investigating the efficacy of robotics rehabilitation in this cohort is warranted.

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research. Randomized clinical trials investigating stationary gait robots have not shown the superiority of these devices over comparable interventions regarding clinical effectiveness, and there are clinical practice guidelines that even recommend against their use. Nevertheless, these devices are still widely used, and our field needs to find ways to apply these devices more effectively. The authors of the article mentioned above feed different machine learning algorithms with patients’ data from the beginning of a robot-assisted gait training intervention using the robot Lokomat. The output of these algorithms allows predictions of the clinical outcome (i.e., functional ambulation categories) while the patients are still participating in the intervention. Such an analysis based on the collection of the device’s data could optimize the application of these devices. The article provides an example of how our field of research could make progress as we advance, and in this opinion piece, I would like to present my view on the prioritization of upcoming research on robot-assisted gait training. Furthermore, I briefly speculate on some drawbacks of randomized clinical trials in the field of robot-assisted gait training and how the quality and thus the effectiveness of robot-assisted gait training could potentially be improved based on the collection and analysis of clinical training data, a better patient selection and by giving greater weight to the motivational aspects for the participants.