Effects of control strategies on gait in robot-assisted post-stroke lower limb rehabilitation: a systematic review

Abstract

Background Stroke related motor function deficits affect patients' likelihood of returning to professional activities, limit their participation in society and functionality in daily living. Hence, robot-aided gait rehabilitation needs to be fruitful and effective from a motor learning perspective. For this reason, optimal human–robot interaction strategies are necessary to foster neuroplastic shaping during therapy. Therefore, we performed a systematic search on the effects of different control algorithms on quantitative objective gait parameters of post-acute stroke patients. Methods We conducted a systematic search on four electronic databases using the Population Intervention Comparison and Outcome format. The heterogeneity of performance assessment, study designs and patients’ numerosity prevented the possibility to conduct a rigorous meta-analysis, thus, the results were presented through narrative synthesis. Results A total of 31 studies (out of 1036) met the inclusion criteria, without applying any temporal constraints. No controller preference with respect to gait parameters improvements was found. However, preferred solutions were encountered in the implementation of force control strategies mostly on rigid devices in therapeutic scenarios. Conversely, soft devices, which were all position-controlled, were found to be more commonly used in assistive scenarios. The effect of different controllers on gait could not be evaluated since conspicuous heterogeneity was found for both performance metrics and study designs. Conclusions Overall, due to the impossibility of performing a meta-analysis, this systematic review calls for an outcome standardisation in the evaluation of robot-aided gait rehabilitation. This could allow for the comparison of adaptive and human-dependent controllers with conventional ones, identifying the most suitable control strategies for specific pathologic gait patterns. This latter aspect could bolster individualized and personalized choices of control strategies during the therapeutic or assistive path.

Research on finite time stability of lower limb rehabilitation exoskeleton based on compliance control

“…According to the established geometric model of the mechanism, the coordinates of the end point B in the coordinate system can be calculated for the lower exoskeleton leg, as shown in Eqn. (1). The forward kinematics of the lower limb exoskeleton is:…”

“…In the previous section, the kinematics model describes the relationship between the joint angle and the spatial pose of the exoskeleton, and the Jacobian matrix J can be obtained from Eqn. (1). The velocity relationship between task space and joint space can be correlated by J, which is expressed as:     …”

“…At present, for patients with lower limb dysfunction caused by stroke or traffic accidents, in addition to necessary drug treatment, regular and moderateintensity rehabilitation training helps patients return to normal life [1]. Therefore, repeated exercise training for lower limb of stroke patients is helpful to stimulate the reorganization and reconstruction of cerebral cortex motor function.…”

Feasibility of an Intelligent Algorithm Based on an Assist-as-Needed Controller for a Robot-Aided Gait Trainer (Lokomat) in Neurological Disorders: A Longitudinal Pilot Study

“…In position control, the robotic joints are programmed to follow predefined trajectories; the impedance of the controller regulates how compliant they are to deviations. The relationship between deviation and applied force is regulated by the impedance, which can be fixed or adaptable (manually or automatically) [ 10 , 11 ]. High impedance leads to a very constrained gait pattern, while low impedance increases compliance while reducing physical support to the movement.…”

sEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training

“…Lin et al designed an sEMG-triggered controller for the artificial muscle-driven lower limb rehabilitation robot, and the methods of discrete wavelet transformation and the support vector machine are used to predict the lower limb movement intention [ 34 ]. Compared with force and position signals, the sEMG signals can reflect the activity level of specific muscle groups, which can monitor and control the movement of limbs in more detail [ 35 ].…”