Hip exoskeletons are increasing in popularity due to their effectiveness across various scenarios and their ability to adapt to different users. However, personalizing the assistance often requires lengthy tuning procedures and computationally intensive algorithms, and most existing methods do not incorporate user feedback. In this work, we propose a novel approach for rapidly learning users' preferences for hip exoskeleton assistance. We perform pairwise comparisons of distinct randomly generated assistive profiles, and collect participants preferences through active querying. Users' feed-back is integrated into a preference-learning algorithm that updates its belief, learns a user-dependent reward function, and changes the assistive torque profiles accordingly. Results from eight healthy subjects display distinct preferred torque profiles, and users' choices remain consistent when compared to a perturbed profile. A comprehensive evaluation of users' preferences reveals a close relationship with individual walking strategies. The tested torque profiles do not disrupt kinematic joint synergies, and participants favor assistive torques that are synchronized with their movements, resulting in lower negative power from the device. This straightforward approach enables the rapid learning of users preferences and rewards, grounding future studies on reward-based human-exoskeleton interaction.

Maintaining balance during walking is a critical yet under-addressed challenge in the control of lower-limb exoskeletons, especially for users with progressive neurological conditions such as multiple sclerosis and muscular dystrophy. While assist-as-needed strategies have enabled flexible support in the sagittal plane, most exoskeletons lack active control in the frontal plane, limiting their ability to support mediolateral (ML) balance. In this study, we introduce a 3D Path Control strategy that enables coordinated assistance across hip abduction/adduction, hip flexion/extension, and knee flexion/extension. The controller is designed to provide partial gait assistance while preserving user autonomy and the ability to modulate step width, an essential mechanism for maintaining ML balance. Two experiments with healthy participants were conducted to evaluate the approach. The first experiment showed that increasing ML assistance improved alignment with a nominal coordination pattern and allowed modulation of hip abduction/adduction range of motion, and consequently, lateral foot placement. The second experiment demonstrated that even with constraining controller settings, users could still deviate from the desired path and adopt different step widths. These results suggest that 3D Path Control can simultaneously assist gait and support balance by combining structured inter-joint coordination while still providing flexibility in foot placement to the subjects.

Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.

Balance loss is a significant challenge in lower-limb exoskeleton applications, as it can lead to potential falls, thereby impacting user safety and confidence. We introduce a control framework for omnidirectional recovery step planning by online optimization of step duration and position in response to external forces. We map the step duration and position to a human-like foot trajectory, which is then translated into joint trajectories using inverse kinematics. These trajectories are executed via an impedance controller, promoting cooperation between the exoskeleton and the user. Moreover, our framework is based on the concept of the divergent component of motion, also known as the Extrapolated Center of Mass, which has been established as a consistent dynamic for describing human movement. This real-time online optimization framework enhances the adaptability of exoskeleton users under unforeseen forces thereby improving the overall user stability and safety. To validate the effectiveness of our approach, simulations, and experiments were conducted. Our push recovery experiments employing the exoskeleton in zero-torque mode (without assistance) exhibit an alignment with the exoskeleton’s recovery assistance mode, that shows the consistency of the control framework with human intention. To the best of our knowledge, this is the first cooperative push recovery framework for the lower-limb human exoskeleton that relies on the simultaneous adaptation of intra-stride parameters in both frontal and sagittal directions. The proposed control scheme has been validated with human subject experiments.

In this paper, we propose a novel adaptive feedforward super-twisting sliding mode control algorithm to resolve the tracking control problem of parallel manipulators. The proposed control scheme includes three main terms, (i) the standard super-twisting algorithm, (ii) an adaptive feedforward dynamic model, and (iii) a feedback term to ensure stability. The proposed controller provides robustness towards uncertainties and disturbances, less sensitive to measurement noise, and allows dynamic parameters adaptation of the manipulator while executing a certain task. Real-time experiments are conducted on a 3-DOF non-redundant Delta parallel robot, including two main scenarios, (i) nominal case, and (ii) robustness towards operating acceleration changes. The relevance of the proposed controller is verified experimentally in both scenarios and compared with two other controllers from the literature, including the standard and the feedforward super-twisting sliding mode control algorithms.

This article deals with the development and implementation of a novel feedforward super-twisting sliding mode controller for robotic manipulators. A full stability analysis based on a Lyapunov candidate is established showing a local asymptotic finite-time convergence of the proposed controller in the presence of upper bounded disturbances. Its robustness toward parametric uncertainties and system disturbances, thanks to the super-twisting approach, is pointed out. In addition, the feedforward dynamic term of the proposed controller that can compensate for the model nonlinearities is not sensitive to measurement noise. Real-time experiments have been conducted on two parallel manipulators: a 5-DOF SPIDER4 PKM and a 3-DOF Delta PKM. The effectiveness of the proposed controller is validated in different scenarios, including the nominal case and robustness toward parametric variations (payload) and speed changes

Freezing of Gait (FoG) is a common condition in patients with Parkinson's disease (PD). It often leads to falls, and it severely affects the patient's quality of life. Although the neural mechanism of FoG is not well-known, wearable sensor-based assistive systems have been shown to effectively monitor FoG and help patients resume walking through rhythmic auditory cues when FoG is detected in real-time. With the development of technologies based on wearable sensors, accurate detection of FoG events is important for resume walking, clinical diagnosis, and treatment. Here, we propose a deep residual network to detect FoG. Offline analysis performed on a publicly available dataset with 10 patients shows the superiority of the proposed approach compared to traditional method (Moore's algorithm) and several deep learning techniques. Under 1s window size, the proposed method can achieve 85.7% sensitivity and 94.0% specificity. The geometric mean of the proposed method is 37.4% ahead of Moore's algorithm. Our approach can help improve the patients with PD quality of life and evaluate symptoms of FoG.

Foot devices have been ubiquitously used in surgery to control surgical equipment. Most common applications are foot switches for electro-surgery, endoscope positioning and tele-robotic consoles. Switches fall short of providing continuous control as required for precise use of instruments. We developed a haptic foot interface to provide continuous assistance in surgical procedures. This paper concerns the foot control of simultaneous five degrees of freedom (DoF) of a surgical laparoscopic gripper. We assess systematically precision at controlling position and orientation at the target and closing of the forceps. Our controller provides position:position mapping between the foot and the robotic tool, as well as haptic feedback, compensating for gravity of the lower limb of the operator so as to alleviate fatigue. A dynamic model compensation and closed loop force feedback is used to achieve high transparency and backdrivability. The assistance is based on a novel type of haptic fixtures combining spring-damper with selective dynamic compensation in the direction aligned with the task of grasping, so as to simplify control of certain poses, made difficult due to the coupling between human lower limbs’ DoF’s. We experimentally evaluated the control strategy with six users on a position control surgical task in simulation. Results show the proposed assistance greatly eases the foot grasping task leading to higher completeness, efficiency, and lower mental and physical load.

In this paper, we propose a novel time-varying feedback control strategy based on the Robust Integral of the Sign of the Error (RISE). The main motivation is to enhance the tracking performance of RISE controller at high dynamic operating conditions. RISE control law ensures a semi-global asymptotic tracking without introducing severe restrictions on the uncertain and nonlinearly parametrized systems. More nonlinearities are added to the original RISE control law by replacing the static feedback gains with nonlinear ones which depend on the system state variables. The proposed contribution is implemented in real-time experiments on a non-redundant three-degrees-of-freedom parallel manipulator named Delta. Comparing to the standard RISE controller, experimental results show better tracking performances of the proposed time-varying feedback RISE controller.

In this paper we present a combination of neuroscience experiments with the use of a parallel armrest robot to study the effects of temporal delays and spatial biases on the movement perception. A dedicated armrest to guide and manipulate the arm has been developed for these experiments. It is a three-degree-of-freedom Delta structure. In combination with a virtual reality application, the movement perception has been evaluated. The results of this study demonstrate that experimental conditions such as spatial deviations and temporal delays impair the correct self-attribution of the movements done with the motorized Delta armrest. This result shows that the motorized Delta armrest can be successfully used to study the effect of movement perception, opening up the way to new studies about the movement perception using robotic system and virtual reality.

This paper furnishes the theoretical basis to perform the calibration of one or more ultra-high-precision industrial robots operating in the same workspace. We propose a new calibration procedure that keeps in account the factors that lower the robot accuracy at nanometer scale. To validate this approach, we present the practical case of the calibration of a two industrial robots system. Finally, we propose nano-indentation as an alternative method to evaluate the final accuracy reached by the system after calibration.

Pelvic motions are of great importance while walking, and have thus to be taken into account when developing and controlling rehabilitation devices. This paper will first introduce a new reeducation device for paraplegic people: the WalkTrainer. This device is composed of a leg and pelvic orthosis, an active bodyweight support and closed loop muscle stimulation. Second, the six degrees of freedom (DOF) of the pelvis will be measured by using the WalkTrainer on a population of twenty healthy subjects. Each DOF was successfully measured and can be analyzed as a function of time or gait cycle. Third several models that predict the pelvic motion amplitude as a function of various parameters (speed, size, ...) will be proposed and analyzed. Fourth, pelvic trajectories will be programmed on the WalkTrainer and applied on healthy subjects by the mean of the pelvic orthosis. In that phase one of the previously proposed models will be implemented. A force reduction of 20% is measured on the pelvic orthosis when the pelvic motion amplitude prediction model is used. At last muscle identification and stimulation will be introduced in the future works chapter.