This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating the technical and clinical specifications of biofeedback systems (BSs), including the complementary use with assistive devices and/or physiotherapist-oriented cues. A literature search was conducted from January 2019 to September 2022 on Cochrane, Embase, PubMed, PEDro, Scopus, and Web of Science databases. Data regarding technical (sensors, biofeedback parameters, actuators, control strategies, assistive devices, physiotherapist-oriented cues) and clinical (participants’ characteristics, protocols, outcome measures, BSs’ effects) specifications of BSs were extracted from the relevant studies. A total of 31 studies were reviewed, which included 660 stroke survivors. Most studies reported visual biofeedback driven according to the comparison between real-time kinetic or spatiotemporal data from wearable sensors and a threshold. Most studies achieved statistically significant improvements on sensor-based and clinical outcomes between at least two evaluation time points. Future research should study the effectiveness of using multiple wearable sensors and actuators to provide personalized biofeedback to users with multiple sensorimotor deficits. There is space to explore BSs complementing different assistive devices and physiotherapist-oriented cues according to their needs. There is a lack of randomized-controlled studies to explore post-stroke stage, mental and sensory effects of BSs.

The adjunctive use of biofeedback systems with exoskeletons may accelerate post-stroke gait rehabilitation. Wearable patient-oriented human-robot interaction-based biofeedback is proposed to improve patient-exoskeleton compliance regarding the interaction torque’s direction (joint motion strategy) and magnitude (user participation strategy) through auditory and vibrotactile cues during assisted gait training, respectively. Parallel physiotherapist-oriented strategies are also proposed such that physiotherapists can follow in real-time a patient’s motor performance towards effective involvement during training. A preliminary pre-post controlled study was conducted with eight healthy participants to conclude about the biofeedback’s efficacy during gait training driven by an ankle-foot exoskeleton and guided by a technical person. For the study group, performance related to the interaction torque’s direction increased during (p-value = 0.07) and after (p-value = 0.07) joint motion training. Further, the performance regarding the interaction torque’s magnitude significantly increased during (p-value = 0.03) and after (p-value = 68.59 × 10⁻³) user participation training. The experimental group and a technical person reported promising usability of the biofeedback and highlighted the importance of the timely cues from physiotherapist-oriented strategies. Less significant improvements in patient–exoskeleton compliance were observed in the control group. The overall findings suggest that the proposed biofeedback was able to improve the participant-exoskeleton compliance by enhancing human-robot interaction; thus, it may be a powerful tool to accelerate post-stroke ankle-foot deformity recovery.

The use of robots in rehabilitation attempts an effective, compliant, and time-efficient gait recovery while adapting the assistance to the user’s needs. Assist-as-needed strategies (AAN), such as adaptive impedance control, have been reported as prominent strategies to enable this recovery effects. This study proposes an interaction-based assist-as-needed impedance control strategy for an ankle robotic orthosis that adapts the robotic assistance by changing the Human-Robot interaction stiffness. The adaptability of the interaction stiffness allows the real-time passage from passive assistance to an active one, approaching AAN gait training. The interaction stiffness was successfully estimated by linear regression of the Human-Robot interaction torque vs angle trajectory curve. From the validation with seven able-bodied subjects, we verified the suitability of this adaptive impedance control for a more compliant, natural, and comfortable motion than the trajectory tracking control. Moreover, the proposed strategy considers the users’ motion intention and encourages them to interact closely with the robotic device while guiding their ankle trajectory according to desired trajectories. These achievements contribute to AAN gait training.

The lateral prefrontal cortex (LPFC) serves as a critical hub for higher-order cognitive and executive functions in the human brain, coordinating brain networks whose disruption has been implicated in many neurological and psychiatric disorders. While transcranial brain stimulation treatments often target the LPFC, our current understanding of connectivity profiles guiding these interventions based on electrophysiology remains limited. Here, we present a high-resolution probabilistic map of bidirectional effective connectivity between the LPFC and widespread cortical and subcortical regions. This map is derived from intracranial evoked potential analysis of 48 797 intracranial direct electrical stimulation runs across 759 implantations in 724 patients with refractory epilepsy (368 male, 354 female, two unspecified; mean age ± standard deviation: 24 ± 13.5 years). We mapped probabilistic connectivity between brain parcels with adaptive resolution—higher resolution in the LPFC in the hemisphere of interest and lower elsewhere—maintaining statistical power while achieving 95% average confidence interval of ∼0.03 for connectivity probability estimates. In addition, the significance threshold (P-value) for probabilistic connectivity was obtained from surrogate distributions. Overall, we observed remarkable symmetry between afferent and efferent connectivity patterns of the LPFC, with a slight preference for efferent connections (mean slope = 0.92 ± 0.09, mean R2 = 0.93 ± 0.025). For example, connections between the inferior frontal gyrus (IFG) and anterior cingulate showed notable directional asymmetry. The IFG strongly projected to most brain networks compared to other LPFC regions, with the strongest connectivity to the ventral attention network (0.26 ± 0.01 compared to values between 0.15 and 0.21 in other LPFC regions). Posterior dorso-lateral prefrontal cortex (DLPFC) demonstrated stronger connectivity to brain networks compared to anterior DLPFC regions (e.g. 0.21 ± 0.01 versus 0.15 ± 0.01 for connectivity to ventral attention network), with the exception of the limbic cortex. All LPFC subregions strongly projected to the fronto-parietal (greater than 0.17) and ventral attention (greater than 0.15) networks, with moderate connections to the default network (between 0.1 and 0.15, with the maximum corresponding to superior DLPFC). Finally, latency analysis suggested that the left LPFC's influence on ipsilateral emotion-related regions is primarily polysynaptic, with particularly strong pathways from IFG to amygdala (0.16 ± 0.02) and hippocampus (0.12 ± 0.01). Taken together, these comprehensive connectivity maps provide a new detailed electrophysiological foundation for understanding the functional anatomy of LPFC and guiding targeted brain stimulation protocols.

Gait disabilities affect the human quality of life. Current directions for time-effective robotic gait rehabilitation require the inclusion of biofeedback systems (BSs) as a complementary robotic tool for efficient motor relearning. This work aims to present the user-centered design and validation of a wearable BS to foster users’ active participation and enable therapists’ effective participation during robotic gait rehabilitation driven by active orthoses. The multimodal BS comprises a development board to manage the activation of the stimuli (vibrotactile through the vibrotactile waist and shank bands, sonorous via single earphone, and visual using RGB LED) according to data tracked by orthosis embedded sensors. The BS’s versatility allows its functioning as a modular and stand-alone system or integrated into the orthotic system. The system’s operability was validated with four healthy subjects walking on a treadmill with the orthotic system and BS at 1 km/h. The results showed an operable system with good usability during robotic gait rehabilitation. This wearable BS has the potential to boost symmetric gait recovery and to effectively augment the user’s active participation during robotic gait therapy; thus, contributing to accelerating the user’s motor recovery.

The movements of sit-to-stand and stand-to-sit are frequently executed on daily life. To develop robotic assistive devices for people with mobility problems, it is important to study how a healthy human performs these tasks. The goal of this study is to present a mathematical model based on acquired kinematic and kinetic data that represents a healthy human body performing these movements. The results revealed that the movements of sit-to-stand and stand-to-sit have symmetric ground reaction force, joint angles and torques. The joint angles and the torques are very similar between each leg. The knee and the hip show more variation of the angle and achieve higher values of torque in comparison to the ankle. Although, ankle has higher torque during standing position. The ground reaction force shows that it is necessary to create an additional force to the weight force to achieve the final position of the movement. The acquired data describe as expected the sit-to-stand and stand-to-sit movements and can be used, in the future, to validate the presented model.