12,068 research outputs found

    A Novel Approach to Simulating Realistic Exoskeleton Behavior in Response to Human Motion

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    Simulation models are a valuable tool for exoskeleton development, especially for system optimization and evaluation. It allows an assessment of the performance and effectiveness of exoskeletons even at an early stage of their development without physical realization. Due to the closed physical interaction between the exoskeleton and the user, accurate modeling of the human–exoskeleton interaction in defined scenarios is essential for exoskeleton simulations. This paper presents a novel approach to simulate exoskeleton motion in response to human motion and the interaction forces at the physical interfaces between the human and the exoskeleton. Our approach uses a multibody model of a shoulder exoskeleton in MATLAB R2021b and imports human motion via virtual markers from a digital human model to simulate human–exoskeleton interaction. To validate the human-motion-based approach, simulated exoskeleton motion and interaction forces are compared with experimental data from a previous lab study. The results demonstrate the feasibility of our approach to simulate human–exoskeleton interaction based on human motion. In addition, the approach is used to optimize the support profile of an exoskeleton, indicating its potential to assist exoskeleton development prior to physical prototyping

    Modeling and simulation study of electromechanically system of the human extremity exoskeleton

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    In order to design a suitable control scheme for human extremity exoskeleton, the interaction force control scheme which regards human body as the work environment of human extremity exoskeleton was proposed. And then, the electromechanical system of human extremity exoskeleton was simplified, and the modeling and simulation study of the electromechanical system by using Matlab/Simulink module was carried out. The angular deviations between human extremity exoskeleton joints and human body joints were obtained by the simulation. Besides, the torques provided by human body and motors for human extremity exoskeleton joints to bear heavy payload were calculated. The analysis of the simulation calculation results proves that the interaction force control scheme can achieve good man-machine coordinated walking as well as help human body bear heavy payload. Besides, the upper extremity exoskeleton experiment was conducted, and the same conclusion with the simulation study was obtained

    Analysis of the human interaction with a wearable lower-limb exoskeleton

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    The design of a wearable robotic exoskeleton needs to consider the interaction, either physical or cognitive, between the human user and the robotic device. This paper presents a method to analyse the interaction between the human user and a unilateral, wearable lower-limb exoskeleton. The lower-limb exoskeleton function was to compensate for muscle weakness around the knee joint. It is shown that the cognitive interaction is bidirectional; on the one hand, the robot gathered information from the sensors in order to detect human actions, such as the gait phases, but the subjects also modified their gait patterns to obtain the desired responses from the exoskeleton. The results of the two-phase evaluation of learning with healthy subjects and experiments with a patient case are presented, regarding the analysis of the interaction, assessed in terms of kinematics, kinetics and/or muscle recruitment. Human-driven response of the exoskeleton after training revealed the improvements in the use of the device, while particular modifications of motion patterns were observed in healthy subjects. Also, endurance (mechanical) tests provided criteria to perform experiments with one post-polio patient. The results with the post-polio patient demonstrate the feasibility of providing gait compensation by means of the presented wearable exoskeleton, designed with a testing procedure that involves the human users to assess the human-robot interaction

    Human-Exoskeleton Interaction Portrait

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    Human-robot physical interaction contains crucial information for optimizing user experience, enhancing robot performance, and objectively assessing user adaptation. This study introduces a new method to evaluate human-robot co-adaptation in lower limb exoskeletons by analyzing muscle activity and interaction torque as a two-dimensional random variable. We introduce the Interaction Portrait (IP), which visualizes this variable's distribution in polar coordinates. We applied this metric to compare a recent torque controller (HTC) based on kinematic state feedback and a novel feedforward controller (AMTC) with online learning, proposed herein, against a time-based controller (TBC) during treadmill walking at varying speeds. Compared to TBC, both HTC and AMTC significantly lower users' normalized oxygen uptake, suggesting enhanced user-exoskeleton coordination. IP analysis reveals this improvement stems from two distinct co-adaptation strategies, unidentifiable by traditional muscle activity or interaction torque analyses alone. HTC encourages users to yield control to the exoskeleton, decreasing muscular effort but increasing interaction torque, as the exoskeleton compensates for user dynamics. Conversely, AMTC promotes user engagement through increased muscular effort and reduced interaction torques, aligning it more closely with rehabilitation and gait training applications. IP phase evolution provides insight into each user's interaction strategy development, showcasing IP analysis's potential in comparing and designing novel controllers to optimize human-robot interaction in wearable robots

    Motion optimization and parameter identification for a human and lower-back exoskeleton model

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    Designing an exoskeleton to reduce the risk of low-back injury during lifting is challenging. Computational models of the human-robot system coupled with predictive movement simulations can help to simplify this design process. Here, we present a study that models the interaction between a human model actuated by muscles and a lower-back exoskeleton. We provide a computational framework for identifying the spring parameters of the exoskeleton using an optimal control approach and forward-dynamics simulations. This is applied to generate dynamically consistent bending and lifting movements in the sagittal plane. Our computations are able to predict motions and forces of the human and exoskeleton that are within the torque limits of a subject. The identified exoskeleton could also yield a considerable reduction of the peak lower-back torques as well as the cumulative lower-back load during the movements. This work is relevant to the research communities working on human-robot interaction, and can be used as a basis for a better human-centered design process

    Systematic evaluation of a knee exoskeleton misalignment compensation mechanism using a robotic dummy leg

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    The objective and quantitative assessment of physical human-exoskeletons interaction (pHEI) represents a pressing necessity in the wearable robots field. This process remains of difficult execution, especially for early stage devices, in which the inclusion of human testing could pose ethical and safety concerns. This manuscript proposes a methodology for pHEI assessment based on an active dummy leg named Leg Replica, which is able to sense interaction forces while wearing an exoskeleton. We tested this methodology on a wearable active knee exoskeleton prototype, with the goal to evaluate the effects of a misalignment compensation mechanism. Through this methodology, it was possible to show how the misalignment compensation mechanism was able to reduce the interaction forces during passive exoskeleton motion. Such reduction was less evident when the exoskeleton was active. The tests allowed to identify specific points of improvements for the exoskeleton, enabling a more specific upgrade of the device based on these experimental results. This study demonstrates the ability of the proposed methodology to objectively benchmark different aspects of pHEI, and to accelerate the iterative development of new devices prior to human testing

    A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions

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    This paper presents an admittance controller based on the passivity theory for a powered upper-limb exoskeleton robot which is governed by the nonlinear equation of motion. Passivity allows us to include a human operator and environmental interaction in the control loop. The robot interacts with the human operator via F/T sensor and interacts with the environment mainly via end-effectors. Although the environmental interaction cannot be detected by any sensors (hence unknown), passivity allows us to have natural interaction. An analysis shows that the behavior of the actual system mimics that of a nominal model as the control gain goes to infinity, which implies that the proposed approach is an admittance controller. However, because the control gain cannot grow infinitely in practice, the performance limitation according to the achievable control gain is also analyzed. The result of this analysis indicates that the performance in the sense of infinite norm increases linearly with the control gain. In the experiments, the proposed properties were verified using 1 degree-of-freedom testbench, and an actual powered upper-limb exoskeleton was used to lift and maneuver the unknown payload.Comment: Accepted in IEEE/ASME Transactions on Mechatronics (T-MECH

    A mechatronic leg replica to benchmark human-exoskeleton physical interactions

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    : Evaluating human-exoskeleton interaction typically requires experiments with human subjects, which raises safety issues and entails time-consuming testing procedures. This paper presents a mechatronic replica of a human leg, which was designed to quantify physical interaction dynamics between exoskeletons and human limbs without the need for human testing. In the first part of this work, we present the mechanical, electronic, sensory system and software solutions integrated in our leg replica prototype. In the second part, we used the leg replica to test its interaction with two types of commercially available wearable devices, i.e. an active full leg exoskeleton and a passive knee orthosis. We ran basic test examples to demonstrate the functioning and benchmarking potential of the leg replica to assess the effects of joint misalignments on force transmission. The integrated force sensors embedded in the leg replica detected higher interaction forces in the misaligned scenario in comparison to the aligned one, in both active and passive modalities. The small standard deviation of force measurements across cycles demonstrates the potential of the leg replica as a standard test method for reproducible studies of human-exoskeleton physical interaction

    Subject-exoskeleton contact model calibration leads to accurate interaction force predictions

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    Knowledge of human–exoskeleton interaction forces is crucial to assess user comfort and effectiveness of the interaction. The subject-exoskeleton collaborative movement and its interaction forces can be predicted in silico using computational modeling techniques. We developed an optimal control framework that consisted of three phases. First, the foot-ground (Phase A) and the subject-exoskeleton (Phase B) contact models were calibrated using three experimental sit-to-stand trials. Then, the collaborative movement and the subject-exoskeleton interaction forces, of six different sit-to-stand trials were predicted (Phase C). The results show that the contact models were able to reproduce experimental kinematics of calibration trials (mean root mean square differences - RMSD - coordinates = 1.1° and velocities = 6.8°/s), ground reaction forces (mean RMSD= 22.9 N), as well as the interaction forces at the pelvis, thigh, and shank (mean RMSD = 5.4 N). Phase C could predict the collaborative movements of prediction trials (mean RMSD coordinates = 3.5° and velocities = 15.0°/s), and their subject-exoskeleton interaction forces (mean RMSD = 13.1° N). In conclusion, this optimal control framework could be used while designing exoskeletons to have in silico knowledge of new optimal movements and their interaction forces.Postprint (author's final draft
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