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.

Passive assistive devices that compensate gravity can reduce human effort during transportation of heavy objects. The additional reduction of inertial forces, which are still present during deceleration when using gravity compensation, could further increase movement performance in terms of accuracy and duration. This study investigated whether position dependent damping forces (PDD) around targets could assist during planar reaching movements. The PDD damping coefficient value increased linearly from 0 Ns/m to 200 Ns/m over 18 cm (long PDD) or 9 cm (short PDD). Movement performance of reaching with both PDDs was compared against damping free baseline conditions and against constant damping (40 Ns/m). Using a Fitts' like experiment design 18 subjects performed a series of reaching movements with index of difficulty: 3.5, 4.5 and 5.5 bits, and distances 18, 23 and 28 cm for all conditions. Results show that PPD reduced (compared to baseline and constant damping) movement times by more than 30% and reduced the number of target reentries, i.e. increasing reaching accuracy, by a factor of 4. Results were inconclusive about whether the long or short PDD conditions achieved better task performance, although mean human acceleration forces were higher for the short PDD, hinting at marginally faster movements. Overall, PDD is a useful haptic force to get humans to decrease their reaching movement times while increasing their targeting accuracy.