Recent studies on ankle exoskeletons have shown the feasibility of this technology for post-stroke gait rehabilitation. The main contribution of the present work is a comprehensive experimental analysis and protocol that focused on evaluating a wide range of biomechanical, usability and users' perception metrics under three different walking conditions: without exoskeleton, with an ankle exoskeleton unpowered, and with an ankle exoskeleton powered. To carry out this study, we developed the ABLE-S exoskeleton that can provide time-adapted ankle plantarflexion and dorsiflexion assistance. Tests with five participants with chronic stroke showed that walking with the ABLE-S exoskeleton significantly corrected foot drop by 25 % while reducing hip compensatory movements by 21 %. Furthermore, asymmetrical spatial gait patterns were significantly reduced by 51 % together with a significant increase in the average foot tilting angle at heel strike by 349 %. The total time to don, doff and set-up the device was of 7.86 ± 2.90 minutes. Finally, 80 % of the participants indicated that they were satisfied with their walking performance while wearing the exoskeleton, and 60 % would use the device for community ambulation. The results of this study add to the existing body of evidence supporting that ankle exoskeletons can improve gait biomechanics for post-stroke individuals.

Background In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes. Methods Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy. Results (1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke. Conclusions Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients’ specific pathology outperform current control strategies.

BackgroundIn the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. Within this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) Providing an updated structured framework of current control strategies, (2) Analyzing the methodology of clinical validations used in the robotic interventions, and (3) Reporting the potential relation between the employed control strategies and clinical outcomes. MethodsFour databases were searched using database-specific search terms from 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy. Results(1) We found that adaptive assistive control (100 \% of exoskeletons) that followed rule-based algorithms (72 \%) based on ground reaction force thresholds (63 \%) in conjunction with trajectory-tracking control (97 \%) were the most implemented control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With moderate grade of evidence, associated to the high heterogeneity in the experimental protocol and low number of studies, we found that adaptive control strategies, which followed threshold-based or adaptive oscillator algorithms together with trajectory-tracking control, resulted in the highest improvements on clinical outcomes for people with stroke. ConclusionsDespite the efforts to develop novel more effective controllers for gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. The most promising controllers seem to be those that adapt to key biomechanical descriptors based on the patients' specific pathology.

<p>The effectiveness of robotic exoskeletons for post-stroke gait rehabilitation might be limited as the control parameters of these devices do not adapt to key biomechanical descriptors. The main contribution of this study is to examine post-stroke gait with the aim of finding relationships between exoskeleton control parameters and a comprehensive set of biomechanical metrics. Five stroke survivors walked with the assistance of a wearable ankle exoskeleton (ABLE-S) using different levels of plantarflexion (PF) and dorsiflexion (DF) peak torque, as well as different timings of PF peak torque. We found that DF peak magnitude had significant negative relations with the temporal symmetry index (p = 0.033) and the paretic foot absolute angle at heel strike (p = 0.019). Changes in the applied PF assistance parameters were significantly correlated with a high variety of temporal and spatial parameters, e.g., walking speed (p = 0.009), stride length (p = 0.011), non-paretic step length (p = 0.024), foot clearance (p = 0.003) and hip hiking (p = 0.038), and the muscle activation for the non-paretic side, e.g., Tibialis Anterior (p = 0.049) and Gastrocnemius Medialis (p = 0.049). Based on our results, we propose a set of control laws for adapting the assistance of ankle exoskeletons that will be evaluated in future work.</p>