Lower-limb exoskeletons (LLE) have proven to be effective tools in rehabilitation for diminishing gait impairments. However, modern LLE control systems based on the assistance-as needed concept face challenges in providing appropriate interaction torques to the user. This is especially true when it is necessary to make the exoskeleton transparent, i.e. with zero user robot interaction torques (τUR), even during walking speed variations, when no assistance is required. To address these limitations, in this work we designed a novel transparent control system that utilizes a baseline torque controller, which generates a torque profile based on τUR from previous strides, and drives the actuators, leading to a lower τUR, along with a synchronization layer comprising a pool of adaptive oscillators (AOs) to estimate the user's gait phase and adapt the controller to different walking speeds. Additionally, a zero-torque controller was used in parallel to the main torque controller to enhance users' volitional control and balance. The proposed controller was experimentally tested on a six-DoF LLE with eight healthy participants who walked for 200 gait strides at three walking speeds. The baseline torque controller was shown to adapt to different walking speeds and successfully improve the exoskeleton transparency in terms of user-robot interaction, reducing the average τUR by 40% of the entire exoskeleton and 70% of the hip joint at 0.8 m/s, and gait kinematics compared to our prior controller based on a zero impedance model.

Robot-assisted rehabilitation is expected to reduce locomotor limitations of children and young adults with Cerebral Palsy (CP). However, to achieve this result, it is essential that the robot is transparent, allowing the user to move freely, and generate joint torques only when the exoskeleton joints significantly deviate from physiological gait patterns. Nevertheless, the development of transparent operation in lower-limb exoskeletons is still an open problem with several implementation challenges. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground exoskeleton designed for children and young adults with CP. The approach employs a feedback zero-torque controller with feedforward compensations for the exoskeleton's dynamics and actuators' impedance. We experimented the proposed system in five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff). The proposed transparent controller reduced the user-robot interaction torque and improved user gait kinematics relative to ExoOff. This is a significant step toward an overground gait training exoskeleton for CP population.

The evaluation of systems under non-ideal conditions is a research problem, particularly in robotic applications for the rehabilitation of people with disabilities. Accordingly, the evaluation of algorithmic strategies for robustness validation under different non-ideal conditions is a current challenge for the scientific community. Therefore, in this study, a computational methodology based on Extreme Learning Machine (ELM) was evaluated for the recognition of seven hand gestures using sEMG under five non-ideal conditions. The shift of eight sEMG electrodes, three upper-limb postures, increased muscle fatigue, and inter-subject and inter-day variabilities were evaluated. The results indicate that the proposed methodology performs well under specific conditions in comparison with previous strategies reported in the literature using Machine Learning classifiers. Therefore, the findings of this study are potentially important for the field of robotics; however, more efforts are still needed to develop more robust computational methods to obtain higher accuracy under non-ideal conditions, with the aim of implementing more controllable, usable, and reliable systems.

Lower limb exoskeletons have been successfully used in robotic-assisted rehabilitation. However, the design limitations of exoskeletons mechanics, such as weight and the lack of kinematic compatibility relative to the user's joints, limit the outcomes of treatment. To address these shortcomings, this work presents the design of a magneto-rheological fluid-based actuator for a knee exoskeleton, namely MRKE. The system was designed to ensure better mobility of the user, presenting high backdrivability and kinematic compatibility with the knee joint. The power train of system is a BLDC 70 W motor integrated to a harmonic drive gearbox. To improve kinematic compatibility relative to the user's knee, a four-bar crossed linkage mechanism (FBLM) was designed to follow the trajectory of the knee center of motion. A customized MR clutch was projected to decouple the motor-reducer from the FBLM, thus enabling high backdrivability. Preliminary results showed a small error (< 3 mm) between the FBLM and the knee center of rotation. Moreover, the MR clutch allowed for low backdrive torque (1.0 N.m) compared to the torque to backdrive the motor-reducer (16.6 N.m).