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.
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.
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 movements of sit-to-stand and stand-to-sit are frequently executed on daily life. To develop robotic assistance 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 joint angles, torques and ground reaction force. The joint angles and the torques are very similar on 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 show that is necessary create a force additional to the standard weight force to achieve a more stable position that is sitting or standing. The acquired data describe as expected the sit-to-stand and stand-to-sit movements and can be used to validate the presented model.
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