Specific instructions are important: A cross-sectional study on device parameters and instruction types while walking with a robot in children and adolescents.

OBJECTIVE: To evaluate how gait kinematics and muscle activity during robot-assisted gait training are affected by different combinations of parameter settings and a number of instruction types, ranging from no instructions to goal-specific instructions. DESIGN: Robots for gait therapy provide a haptic guidance, but too much guidance can limit the active participation. Therapists can stimulate this active participation either with instructions or by adapting device parameters. How these two factors interact is still unknown. In the present study, we test the interaction of 3 different parameter settings and 4 instruction types in a cross-sectional study with 20 children and adolescents without impairment. Gait kinematics and surface electromyography were measured to evaluate the immediate effects. RESULTS: We found that only goal-specific instructions in combination with a low guidance led to a moderate but significant change in gait kinematics. The muscle activity was altered by all instructions, but the biggest effect was found for goal-specific instructions with a 2.5 times higher sEMG amplitude compared to no instruction. CONCLUSIONS: Goal-specific instructions are a key element of robot-assisted gait therapy interventions and device parameter adjustments may be used to modulate their effects. Therapists should pay close attention to how they instruct patients.

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.

The FreeD module's lateral translation timing in the gait robot Lokomat: a manual adaptation is necessary.

BACKGROUND: Pelvic and trunk movements are often restricted in stationary robotic gait trainers. The optional FreeD module of the driven gait orthosis Lokomat offers a combined, guided lateral translation and transverse rotation of the pelvis and may therefore support weight shifting during walking. However, from clinical experience, it seems that the default setting of this timing does not correspond well with the timing of the physiological pelvic movement during the gait cycle. In the software, a manual adaptation of the lateral translation's timing with respect to the gait cycle is possible. The aim of this study was to investigate if such an offset is indeed present and if a manual adaptation by the therapist can improve the timing towards a more physiological pattern comparable to physiological overground walking. METHODS & RESULTS: Children and adolescents with neurologic gait disorders and a Gross Motor Function Classification System level I-IV completed two different walking conditions (FreeD Default and FreeD Time Offset) in the Lokomat. The medio-lateral center of mass positions were calculated from RGB-Depth video recordings with a marker-less motion capture algorithm. Data of 22 patients (mean age: 12 ± 3 years) were analyzed. Kinematic analyses showed that in the FreeD Default condition, the maximum lateral center of mass excursion occurred too early. In the FreeD Time Offset condition, the manual adaptation by the therapists led to a delay of the maximum center of mass displacement by 8.2% in the first phase of the gait cycle and by 4.9% in the second phase of the gait cycle compared to the FreeD Default condition. The maximum lateral center of mass excursion was closer to that during physiological overground walking in the FreeD Time Offset condition than in the FreeD Default condition. CONCLUSION: A manual adaptation of the timing of the FreeD module in the Lokomat shifts pelvis kinematics in a direction of physiological overground walking. We recommend therapists to use this FreeD Time Offset function to adjust the phase of weight shifting for each patient individually to optimize the kinematic walking pattern when a restorative therapy approach is adopted.

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.

Clustering trunk movements of children and adolescents with neurological gait disorders undergoing robot-assisted gait therapy: the functional ability determines if actuated pelvis movements are clinically useful.

INTRODUCTION: Robot-assisted gait therapy is frequently used for gait therapy in children and adolescents but has been shown to limit the physiological excursions of the trunk and pelvis. Actuated pelvis movements might support more physiological trunk patterns during robot-assisted training. However, not every patient is expected to react identically to actuated pelvis movements. Therefore, the aim of the present study was to identify different trunk movement patterns with and without actuated pelvis movements and compare them based on their similarity to the physiological gait pattern. METHODS AND RESULTS: A clustering algorithm was used to separate pediatric patients into three groups based on different kinematic reactions of the trunk to walking with and without actuated pelvis movements. The three clusters included 9, 11 and 15 patients and showed weak to strong correlations with physiological treadmill gait. The groups also statistically differed in clinical assessment scores, which were consistent with the strength of the correlations. Patients with a higher gait capacity reacted with more physiological trunk movements to actuated pelvis movements. CONCLUSION: Actuated pelvis movements do not lead to physiological trunk movements in patients with a poor trunk control, while patients with better walking functions can show physiological trunk movements. Therapists should carefully consider for whom and why they decide to include actuated pelvis movements in their therapy plan.

Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study.

Introduction: Measuring kinematic behavior during robot-assisted gait therapy requires either laborious set up of a marker-based motion capture system or relies on the internal sensors of devices that may not cover all relevant degrees of freedom. This presents a major barrier for the adoption of kinematic measurements in the normal clinical schedule. However, to advance the field of robot-assisted therapy many insights could be gained from evaluating patient behavior during regular therapies. Methods: For this reason, we recently developed and validated a method for extracting kinematics from recordings of a low-cost RGB-D sensor, which relies on a virtual 3D body model to estimate the patient's body shape and pose in each frame. The present study aimed to evaluate the robustness of the method to the presence of a lower limb exoskeleton. 10 healthy children without gait impairment walked on a treadmill with and without wearing the exoskeleton to evaluate the estimated body shape, and 8 custom stickers were placed on the body to evaluate the accuracy of estimated poses. Results & Conclusion: We found that the shape is generally robust to wearing the exoskeleton, and systematic pose tracking errors were around 5 mm. Therefore, the method can be a valuable measurement tool for the clinical evaluation, e.g., to measure compensatory movements of the trunk.

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

BACKGROUND: Lokomat therapy for gait rehabilitation has become increasingly popular. Most evidence suggests that Lokomat therapy is equally effective as but not superior to standard therapy approaches. One reason might be that the Lokomat parameters to personalize therapy, such as gait speed, body weight support and Guidance Force, are not optimally used. However, there is little evidence available about the influence of Lokomat parameters on the effectiveness of the therapy. Nevertheless, an appropriate reporting of the applied therapy parameters is key to the successful clinical transfer of study results. The aim of this scoping review was therefore to evaluate how the currently available clinical studies report Lokomat parameter settings and map the current literature on Lokomat therapy parameters. METHODS AND RESULTS: A systematic literature search was performed in three databases: Pubmed, Scopus and Embase. All primary research articles performing therapy with the Lokomat in neurologic populations in English or German were included. The quality of reporting of all clinical studies was assessed with a framework developed for this particular purpose. We identified 208 studies investigating Lokomat therapy in patients with neurologic diseases. The reporting quality was generally poor. Less than a third of the studies indicate which parameter settings have been applied. The usability of the reporting for a clinical transfer of promising results is therefore limited. CONCLUSION: Although the currently available evidence on Lokomat parameters suggests that therapy parameters might have an influence on the effectiveness, there is currently not enough evidence available to provide detailed recommendations. Nevertheless, clinicians should pay close attention to the reported therapy parameters when translating research findings to their own clinical practice. To this end, we propose that the quality of reporting should be improved and we provide a reporting framework for authors as a quality control before submitting a Lokomat-related article.