Limb Exoskeleton Robot Research Articles

Evaluation of safety-related performance of wearable lower limb exoskeleton robot (WLLER): A systematic review

Wearable lower limb exoskeleton robots (WLLER) have broad development prospects in the military, industrial and medical fields. The intelligent device comes into intimate contact with the human body, and its safety is an essential factor that developers must consider. With the increasing research on the safety of WLLER, its safety test methods and indicators should gradually improve. By examining current test methods and indicators, this study aims to mobilize this information and summarize the most recent safety research. The safety-related studies reviewed in this paper are not limited to evaluating subjects in clinical trials but are concerned with extensive safety research. The focus of our analysis is the test performance indicators. Some functional evaluation indicators are also summarized to explore a broader and more applicable approach on the safety metrics. We found that, in general, most researchers pay attention to the power-assisting performance of WLLER, but the stability and comfort have been largely ignored. At the same time, our analysis also reveals that although there are a wide variety of existing evaluation indicators, uniform and standard test methods and indicators for safety testing of WLLER are still deficient. Based on these results, we identified and discussed several promising research directions that may help the community to attain a widely accepted test method that can objectively evaluate the safety of WLLER.

A GMM-DTW-Based Locomotion Mode Recognition Method in Lower Limb Exoskeleton

Exoskeleton robots can provide support and protection to the human body as well as be able to perform a series of tasks under control. Locomotion mode recognition is an essential prerequisite for assisted control of lower limb exoskeleton robots. A system is designed to be applied to a variety of individuals with quick and accurate identification. In this article, we propose a novel real-time locomotion mode recognition algorithm based on Gaussian mixture model (GMM) features aligned by dynamic time warping (DTW). The spatial distribution of the sensor data is represented by the GMM features, which emphasize the coupling between joints. This peculiarity promotes the distinction of similarity judgment and shows superior generalization performance. Furthermore, we expand the study of the warping path. We further validate the reasonableness of the DTW algorithm for feature matching to improve the recognition rate. The data of several inertial measurement units (IMUs) and pressure sensors were collected by 17 subjects in three terrains, including level ground, stairs, and ramp. We define the five locomotion modes as walking on level ground, stair ascent (SA), stair descent, ramp ascent, and ramp descent. Compared with the algorithms in previous papers, the GMM-DTW algorithm can achieve substantial recognition rates and good generalization performance with low latency. This work establishes the groundwork for the soft control of exoskeleton robots in the future.

Test–retest reliability of upper limb robotic exoskeleton assessments in children and youths with brain lesions

In children with congenital or acquired brain lesions, impaired upper limb function can affect independence. Assessing upper limb function is important for planning and evaluating neurorehabilitative interventions. Robotic devices increase measurement-objectivity and enable measuring parameters reflecting more complex motor functions. We investigated the relative and absolute test–retest reliability of assessments to measure upper limb functions in children and adolescents with brain lesions with the exoskeleton ChARMin. Thirty children (9 females, mean age ± SD = 12.5 ± 3.3 years) with congenital brain injuries (n = 15), acquired (n = 14), both (n = 1) and impaired upper limb function participated. They performed the following ChARMin assessments and repeated them within three to seven days: active and passive Range of Motion (ROM), Strength, Resistance to Passive Movement, Quality of Movement, Circle, and Workspace. We calculated the systematic difference, Intraclass Correlation Coefficient (ICC) and Smallest Real Difference (SRD) for each parameter. Six parameters of three assessments showed systematic errors. ICCs ranged from little to very high and SRD values varied considerably. Test–retest reliability and measurement errors ranged widely between the assessments. Systematic differences indicated that random day-to-day variability in performance would be responsible for reduced reliability of those parameters. While it remains debatable whether robot-derived outcomes should replace certain routine assessments (e.g., ROM, strength), we recommend applying certain technology-based assessments also in clinical practice.Trial registration: This study was registered prospectively at ClinicalTrials.gov (identifier: NCT02443857) on May 14, 2015.

Fuzzy radial-based impedance controller design for lower limb exoskeleton robot

AbstractThe lower extremity rehabilitation exoskeleton is mainly used to help patients with movement disorders complete rehabilitation training. For the human-machine interaction problem of the lower limb rehabilitation exoskeleton, a fuzzy radial-based impedance (RBF-FVI) controller is proposed in this study. A six degree of freedom (DOF) lower extremity rehabilitation exoskeleton was developed, and the human-machine coupling dynamics model was established. To realize the compliance control of the human-machine coupling system, a novel RBF-FVI controller is designed, which includes an inner-loop fuzzy position control module and an outer-loop impedance control module. The inner-loop fuzzy position control module is mainly used to achieve the tracking control of the desired training trajectory and position adjustment amount. The outer-loop impedance control module regulates the impedance parameters and compensates for the uncertainty terms. The superiority of the proposed controller in trajectory following is verified through simulation and comparison tests. The hardware test of the human-machine coupling system was carried out, and the test results showed that the subject and the exoskeleton system could realize a coordinated and smooth movement.

A composite position control of flexible lower limb exoskeleton based on second-order sliding mode

Aiming at the problem that the trajectory tracking error of flexible lower limb exoskeleton robot is too large under the condition of external disturbance and parameters uncertainty, a composite position control method based on second-order sliding mode control was proposed. Firstly, the flexible lower limb exoskeleton robot is modeled by Lagrange function. Secondly, considering that the system is affected not only by matched disturbance but also by unmatched disturbance, two finite time state observers are used to observe and compensate the two disturbances in real time. The super-twisting method is employed in the position control section to ensure that the trajectory tracking error of the knee joint converges to zero in finite time. Finally, the Lyapunov function proves the stability of the suggested control technique. The experimental findings reveal that the suggested control approach has a better trajectory tracking effect and resilience than classic PD control and sliding mode control, indicating its superiority.

Gait multi-objectives optimization of lower limb exoskeleton robot based on BSO-EOLLFF algorithm

AbstractAiming at problems of low optimization accuracy and slow convergence speed in the gait optimization algorithm of lower limb exoskeleton robot, a novel gait multi-objectives optimization strategy based on beetle swarm optimization (BSO)-elite opposition-based learning (EOL) levy flight foraging (LFF) algorithm was proposed. In order to avoid the algorithm from falling into the local optimum, the EOL strategy with global search capability, the LFF strategy with local search capability and the dynamic mutation strategy with high population diversity were introduced to improve optimization performance. The optimization was performed by establishing a multi-objectives optimization function with the robot’s gait zero moment point (ZMP) stability margin and driving energy consumption. The joint comparative tests were carried out in SolidWorks, ADAMS and MATLAB software. The simulation results showed that compared with the particle swarm optimization algorithm and the BSO algorithm, the ZMP stability margin obtained by the BSO-EOLLFF algorithm was increased, and the average driving energy consumption was reduced by 25.82% and 17.26%, respectively. The human-machine experiments were conducted to verify the effectiveness and superiority. The robot could realize stable and smooth walking with less energy consumption. This research will provide support for the application of exoskeleton robot.

Humanoid control of lower limb exoskeleton robot based on human gait data with sliding mode neural network

Lower limb exoskeleton robots offer an effective treatment for patients with lower extremity dysfunction. In order to improve the rehabilitation training effect based on the human motion mechanism, this paper proposes a humanoid sliding mode neural network controller based on the human gait. A humanoid model is constructed based on the human mechanism, and the parameterised gait trajectory is used as target to design the humanoid control system for robots. Considering the imprecision of the robot dynamics model, the neural network is adopted to compensate for the uncertain part of the model and improve the model accuracy. Moreover, the sliding mode control in the system improves the response speed, tracking performance, and stability of the control system. The Lyapunov stability analysis proves the stability of the control system theoretically. Meanwhile, an evaluation method using the similarity function is improved based on joint angle, velocity, and acceleration to evaluate the comfort of humans in rehabilitation training more reasonably. Finally, to verify the effectiveness of the proposed method, simulations are carried out based on experimental data. The results show that the control system could accurately track the target trajectory, of which the robot is highly similar to the human.

Gait Deviation Correction Method for Gait Rehabilitation With a Lower Limb Exoskeleton Robot

Exoskeleton robots play an essential role in the gait rehabilitation of lower limbs in stroke patients. Existing lower limb exoskeleton robots commonly employ reference trajectories to perform gait rehabilitation training for patients. However, a significant gait deviation may occur between the gait trajectory of the wearer and the reference trajectory due to the following reasons. They are the physically flexible connection and the applied compliance control strategy between the wearer and the exoskeleton. This paper proposes a gait deviation correction method (GDCM) to decrease the gait deviation for lower limb exoskeleton robots. First, the gait curves of the exoskeleton and the wearer were obtained by using the clinical gait analysis (CGA) curve as a reference trajectory for the exoskeleton. Then, a gait correction model was trained by a database containing the CGA curve, the gait trajectory of both the exoskeleton and wearer obtained from 15 participants, and their body feature parameters such as height, weight, and hip-length. Finally, the gait correction model was introduced into the control system to modify the input trajectory of the exoskeleton. The experimental results showed that the gait trajectories of the wearer’s hip and knee after correction were closer to the CGA curve than without modification. The minor gait trajectory deviation should lead to a better efficacy of the training with the robot if the reference trajectory is assumed to be optimal.

Robust Iterative Learning Control Algorithm for Lower Limb Rehabilitation Proactive Human-robot Collaboration

At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

Human-Gait-Based Tracking Control for Lower Limb Exoskeleton Robot

Research shows that it is practical for the normal human movement mechanism to assist the patients with stroke in robot-assisted gait rehabilitation. In passive training, the effect of rehabilitation training for patients can be improved by imitating normal human walking. To make the lower limb exoskeleton robot (LLER) move like a normal human, a tracking control scheme based on human gait data is proposed in this paper. The real human gait data is obtained from healthy subjects using a three-dimensional motion capture platform (3DMCP). Furthermore, the normal human motion characteristics are adopted to enhance the scientificity and effectiveness of assistant rehabilitation training using LLER. An adaptive radial basis function network (ARBFN) controller based on feed-forward control is presented to improve the trajectory tracking accuracy and tracking performance of the control system, where the ARBFN controller is deployed to predict the uncertain model parameters. The feed-forward controller based on the tracking errors is used to compensate for the input torque of LLER. The effectiveness of the presented control scheme is confirmed by simulation results based on experimental data.

Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton.

Motion assistance exoskeletons are designed to support the joint movement of people who perform repetitive tasks that cause damage to their health. To guarantee motion accompaniment, the integration between sensors and actuators should ensure a near-zero delay between the signal acquisition and the actuator response. This study presents the integration of a platform based on Imocap-GIS inertial sensors, with a motion assistance exoskeleton that generates joint movement by means of Maxon motors and Harmonic drive reducers, where a near zero-lag is required for the gait accompaniment to be correct. The Imocap-GIS sensors acquire positional data from the user’s lower limbs and send the information through the UDP protocol to the CompactRio system, which constitutes a high-performance controller. These data are processed by the card and subsequently a control signal is sent to the motors that move the exoskeleton joints. Simulations of the proposed controller performance were conducted. The experimental results show that the motion accompaniment exhibits a delay of between 20 and 30 ms, and consequently, it may be stated that the integration between the exoskeleton and the sensors achieves a high efficiency. In this work, the integration between inertial sensors and an exoskeleton prototype has been proposed, where it is evident that the integration met the initial objective. In addition, the integration between the exoskeleton and IMOCAP is among the highest efficiency ranges of similar systems that are currently being developed, and the response lag that was obtained could be improved by means of the incorporation of complementary systems.

Application of a Tactile Sensor for the Gait Phase Classification for an Exoskeleton

This paper presents the design and implementation of fuzzy logic control for human walking using lab-developed sensors and the second lower limb robotic exoskeleton (LLRE-II) to detect gait phases. A piezoresistive tactile sensor (PRTS) was developed and used to measure the foot pressure to predict human walking behavior. The proposed sensor with grid structures was then fabricated using polymer composites of multi-walled carbon nanotubes (MWCNTs) combined with polydimethylsiloxane (PDMS). The doping ratio and design of the grid structure of the sensors were optimized to achieve a good sensing range. The foot reactive forces measured by the four sensors were calculated and converted to force ratio factors to improve the performance of gait phase detection by the fuzzy logic control. Based on the sensor tests, the most stable doping ratio was fixed as 7 wt%, and the ratio of the grid structure (line width:line spacing:thickness) was 1:1:1. Sensors were set in the soles of the shoes on the foot pads of the exoskeleton, and the foot reactive forces were captured for analysis. The performance of the proposed algorithm for gait phase detection was verified experimentally. The results indicated that both regular and irregular walking could be detected by the proposed method.

Leader–follower Stackelberg game oriented adaptive robust constraint-following control design for fuzzy exoskeleton robot systems

To comprehensively enhance the robust tracking performance, a leader–follower Stackelberg game oriented adaptive robust constraint-following control scheme has been proposed for a class of uncertain mechanical systems. In the proposed scheme, a fuzzy set-theoretic description represents the uncertainties (possibly fast time-varying), and an adaptive robust constraint-following control ensures the robust stability. With the fuzzy uncertainty description and control performance analysis, a leader–follower game theory is employed to obtain multiple optimal gains for the proposed control scheme. Further, the existence of these optimal parameters can be verified. The proposed approach is successfully applied to a lower limb exoskeleton (LLE) robot system for rehabilitation training.

Metric Learning for Robust Gait Phase Recognition for a Lower Limb Exoskeleton Robot Based on sEMG

This study aims to develop a robust myoelectric control method for gait phase recognition in a lower-limb exoskeleton robot. In the proposed method, a metric learning-based temporal convolution network (ML-TCN) was utilized to extract discriminative features of the surface electromyography (sEMG) signals and recognize four gait phases: heel strike (HS), foot flat (FF), heel off (HO), and swing (SW). Vicon validated the effectiveness of the sEMG data-acquisition system. The proposed method acquires significantly more discriminative features than long short-term memory (LSTM) or common temporal convolutional network (TCN). The experimental results show that the proposed model has a higher prediction accuracy and stronger robustness against disturbances in complex terrain. Finally, under the complex terrain of level ground-ramp ascending-ramp descending walking, the proposed model’s accuracy of gait phase recognition is 96.22%, which is better than LSTM’s 91.20%. Noise disturbances of 10%, 20%, 30%, 40%, and 50% were added to the test set. Compared with LSTM, the resistance to disturbances of the proposed method increased by 8.15%, 8.79%, 9.67%, 10.6%, and 10.61%, respectively.

A Rehabilitation Training Interactive Method for Lower Limb Exoskeleton Robot

Rehabilitation exoskeleton robot plays an important role in rehabilitation training for limb-disabled patients and exoskeleton robots are becoming popular in rehabilitation area. To encourage the patient's active participation, the patient's subjective motion intention needs to be considered. In this paper, a rehabilitation training interactive method of lower limb exoskeleton robot based on patient's intention is proposed. The proposed method benefits patients to adjust the training trajectory in a safe range of motion according to their intentions. That is, the patient can adjust the amplitude of the trajectory and even the initial point of the trajectory by applying external interaction force to the human-robot system. To identify the patient's intention, the classical momentum observer is introduced to detect the interaction force between the patient and the exoskeleton. In addition, joint space trajectories and Cartesian space trajectories with different amplitudes are designed to enrich the training contents. Then, a trajectory switching algorithm based on external interaction recognition and designed training trajectories is developed. Finally, the proposed method is supported by the simulation results on a lower limb exoskeleton with 2 degrees of freedom (DoF).

Robust tracking control design with a novel leakage-type adaptive mechanism for an uncertain lower limb exoskeleton robot

In this paper, a novel adaptive robust control approach has been proposed for a class of uncertain mechanical systems. First, aiming for the unknown uncertainties which may be fast time-varying, a novel adaptive mechanism in leakage-type will be developed. Unlike the prevalent adaptive methods, this adaptive mechanism put forward mainly accounts for estimating all the lumped uncertainty terms, and does not require any information of uncertainties other than they are bounded; Second, through transforming the gait following assignment to the constraint control, an adaptive robust constraint-following controller constructed will render the constraints uniformly bounded and uniformly ultimately bounded. To testify the efficacy of proposed approach, a lower limb exoskeleton robot is considered as an illustrative example, the simulations indicate that the proposed control can indeed improve the level of passive rehabilitation training.

Research on control method of upper limb exoskeleton based on mixed perception model

AbstractAs one of the research hotspots in the field of rehabilitation robotics, the upper limb exoskeleton robot has been widely used in the field of rehabilitation. However, the existing methods cannot comprehensively and accurately reflect the motion state of patients, which may lead to overtraining and secondary injury of patients in the process of rehabilitation training. In this paper, an upper limb exoskeleton control method based on mixed perception model of motion intention and intensity is proposed, which is based on the 6 degree-of-freedom upper limb rehabilitation exoskeleton in the laboratory. First, the kinematic information and heart rate information in the rehabilitation process of patients are collected, corresponding to patients’ motion intention and motion intensity, and fused to obtain the mixed perception vector. Second, the motion perception model based on long short-term memory neural network is established to realize the prediction of upper limb motion trajectory of patients and compared with back-propagation neural network to prove its effectiveness. Finally, the control system is built, and both offline and online test of the control method proposed are implemented. The experimental results show that the method can achieve comprehensive motion state perception of patients, realize real-time and accurate prediction trajectory according to human motion intention and intensity. The average prediction accuracy is 95.3%, and predicted joint angle error is less than 5 degrees. Therefore, the control method based on mixed perception model has good robustness and universality, which provides a new method for the active control of upper limb exoskeleton.

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion.

In recent decades, although the research on gait recognition of lower limb exoskeleton robot has been widely developed, there are still limitations in rehabilitation training and clinical practice. The emergence of interactive information fusion technology provides a new research idea for the solution of this problem, and it is also the development trend in the future. In order to better explore the issue, this paper summarizes gait recognition based on interactive information fusion of lower limb exoskeleton robots. This review introduces the current research status, methods, and directions for information acquisition, interaction, fusion, and gait recognition of exoskeleton robots. The content involves the research progress of information acquisition methods, sensor placements, target groups, lower limb sports biomechanics, interactive information fusion, and gait recognition model. Finally, the current challenges, possible solutions, and promising prospects are analysed and discussed, which provides a useful reference resource for the study of interactive information fusion and gait recognition of rehabilitation exoskeleton robots.

Development of a Prototype Overground Pelvic Obliquity Support Robot for Rehabilitation of Hemiplegia Gait.

In this work, we present the overground prototype gait-rehabilitation robot for using motion assistance and training for paralyzed patients. In contrast to the existing gait-rehabilitation robots, which focus on the sagittal plane motion of the hip and knee, we aim to develop a mobile-based pelvic support gait-rehabilitation system that includes a pelvic obliquity support mechanism and a lower-limb exoskeleton. To achieve this, a scissor mechanism is proposed to generate the paralyzed patient’s pelvic obliquity motion and weight support. Moreover, the lower limb exoskeleton robot is integrated with the developed system to provide the patient’s gait by correcting mechanical aids. We used computer-aided analysis to verify the performance of the prototype hardware itself. Through these methods, it was shown that our motor can sufficiently lift 100 kg of user weight through the scissor mechanism, and that the mobile driving wheel motor can operate at a speed of 1.6 m/s of human walking, showing that it can be used for gait rehabilitation of patients in need of a lower speed. In addition, we verified that the system drives the model by generating pelvic motion, and we verified the position controller of the integrated system, which supports the multi-degree motion by creating hip/knee/pelvic motion with a human dummy mannequin and systems. We believe that the proposed system can help address the complex rehabilitation motion assistance and training of paralyzed patients.

Optimal robust control with cooperative game theory for lower limb exoskeleton robot

For achieving trajectory tracking issue of the lower limb exoskeleton robot, a novel optimal robust control with cooperative game theory is proposed. The uncertainties are considered (possible time-varying, bounded and fast), and the fuzzy set theory is creatively adopted to describe the boundary. From the view of analytical mechanics, the trajectory tracking is treated as the constraints control problem, including holonomic and nonholonomic constraints, which need to satisfy the conditions of human motion. Combining the robust control and optimal design, optimal robust control is formulated to satisfy both performances guaranteeing and optimal. The Pareto optimal solution is obtained to guarantee the minimum control cost. In the simulation, the adaptive robust control is chosen as a comparison. The existence of Pareto optimality and the effectiveness of optimal robust control have been verified via simulation results.