Lower Limb Exoskeleton Robot Research Articles

Embodiment for Robotic Lower-Limb Exoskeletons: A Narrative Review.

Research on embodiment of objects external to the human body has revealed important information about how the human nervous system interacts with robotic lower limb exoskeletons. Typical robotic exoskeleton control approaches view the controllers as an external agent intending to move in coordination with the human. However, principles of embodiment suggest that the exoskeleton controller should ideally coordinate with the human such that the nervous system can adequately model the input-output dynamics of the exoskeleton controller. Measuring embodiment of exoskeletons should be a necessary step in the exoskeleton development and prototyping process. Researchers need to establish high fidelity quantitative measures of embodiment, rather than relying on current qualitative survey measures. Mobile brain imaging techniques, such as high-density electroencephalography, is likely to provide a deeper understanding of embodiment during human-machine interactions and advance exoskeleton research and development. In this review we show why future exoskeleton research should include quantitative measures of embodiment as a metric of success.

Feedback Linearization Control of Lower Limb Exoskeleton Robot for Rehabilitation

Stroke is a serious medical condition that affects a large number of people worldwide. Patients who suffer from stroke often experience difficulty in movement, especially in their lower limbs. Rehabilitation is crucial to help stroke patients regain their mobility and quality of life. One promising solution is the use of exoskeleton robots that can assist patients in their recovery process. These robots are designed to mimic the human skeletal system and can help stroke patients regain their natural gait cycle. The proposed solution is an exoskeleton robot that can assist two joints, the hip and knee joints. However, controlling the exoskeleton robot is a challenging task. To address this issue, the feedback linearization control technique was implemented using MATLAB/Simulink. This control technique can ensure that the robot movements are smooth and precise, helping stroke patients regain their natural gait cycle. To validate our proposed control techniques, three different input reference trajectories were used. The simulation results showed the trajectory tracking performance of our controller, demonstrating the effectiveness of our approach. By successfully implementing the feedback linearization control technique, the exoskeleton robot can assist patients in regaining their mobility, leading to improved quality of life. In addition to the rehabilitation benefits, exoskeleton robots have the potential to assist patients in their daily activities, such as walking and running. This can significantly improve the patient’s independence and quality of life. The use of exoskeleton robots can also reduce the burden on healthcare providers by decreasing the need for physical assistance during rehabilitation.

Human gait tracking for rehabilitation exoskeleton: adaptive fractional order sliding mode control approach

To improve the rehabilitation training effect of hemiplegic patients, in this paper, a discrete adaptive fractional order fast terminal sliding mode control approach is proposed for the lower limb exoskeleton system to implement high-precision human gait tracking tasks. Firstly, a discrete dynamic model is established based on the Lagrange system discretization criterion for the lower limb exoskeleton robot. Then, in order to design a discrete adaptive fractional order fast terminal sliding mode controller, the Grünwald–Letnikov fractional order operator is introduced to combine with fast terminal attractor to construct a fractional order fast terminal sliding surface. An adaptive parameter adjustment strategy is proposed for the reaching law of sliding mode control, which drives the sliding mode to the stable region dynamically. Moreover, the stability of the control system is proved in the sense of Lyapunov, and the guidelines for selecting the control parameters are given. Finally, the simulations are tested on the MATLAB-Opensim co-simulation platform. Compared with the conventional discrete sliding mode control and discrete fast terminal sliding mode control, the results verify the superiority of the proposed method in improving lower limb rehabilitation training.

Landing Area Prediction in Complex Terrains for Walking-assisted Lower-limb Exoskeleton Robot

With the expansion of application scenarios, the walking-assisted lower-limb exoskeleton robot urgently needs to improve its walking ability in complex terrains. However, due to the lack of environmental understanding ability and decision-making ability, the exoskeleton can not meet the need of people with lower-limb disability in complex terrains. To improve the walking abillity, this paper proposes a method of predicting the landing area in complex terrains for walking-assisted lower-limb exoskeleton robot. Multiple sensors were used to obtain the terrain information, step length, and stride length of human walking, and a multi-type input convolutional network was built to establish the mapping relationship to learn the decision-making mechanism of human standing in a complex environment. The result shows that the established network effectively predicts the landing area. Under the condition of full training, the predicted position deviation of the landing area is controlled within 6%, and the prediction rationality of the landing area can reach 90%.

Online gait generator for lower limb exoskeleton robots: Suitable for level ground, slopes, stairs, and obstacle avoidance

The development of lower limb exoskeletons has seen significant interest in recent times. Two types of them are more used that cover two types of needs: gait rehabilitation and human locomotion assistance. An essential subject in controlling the latter kind is trajectory generation, in which there are still challenges. For online controlling of the exoskeleton, gait parameters must have the ability to change at any moment during walking, taking into account human intention and particular conditions. In this paper, an online gait generation method is provided that is suitable for different walking modes. For this purpose, three trajectory generator blocks are proposed. The first block is for the center of mass (CoM) in the double support phase, where the trajectory is generated to make the patient feel more comfortable. The second block is for the support leg in the single support phase. The trajectory is generated using the center of pressure (CoP) criterion to secure backward balance and reduce the forces applied to the arms. The last block is for the swing leg in the single support phase, where a cost function is proposed to minimize the torques of the motors. The performance analysis of the proposed trajectory generator blocks was evaluated, and walking patterns were examined via simulations. Finally, three experimental tests were implemented with a healthy subject wearing Exoped® exoskeleton on level-ground with an obstacle, and stairs.

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.

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.

Prediction of ground reaction forces using the artificial neural network from capacitive self-sensing values of composite ankle springs for exo-robots

Exoskeleton robots, which are wearable robots that assist human movement, have recently been developed to augment the performance of the human body. In this study, the ankle spring of lower-limb exoskeleton robots was created from a foam core sandwich-structured composite (FCSC) to support the running movement by storing and releasing elastic energy and to predict kinetic data using the self-sensed capacitance data from the structure. Through finite element analysis, the deformation behaviors of the FCSC ankle spring were analyzed and compared with those of a typical fiber-reinforced plastic composite ankle spring. Using a universal test machine, the capacitive-based self-sensing capability of the FCSC ankle spring was examined under several loading rates and cyclic loading. Furthermore, running tests with the lower-limb exoskeleton robot equipped with the FCSC ankle spring were performed on a treadmill. Horizontal and vertical ground reaction forces (GRFs) and capacitance values were obtained from the force plate embedded in the treadmill and FCSC ankle spring, respectively, at various running speeds. These data were used to train an artificial neural network (ANN) model as input and output data. Consequently, the trained ANN model predicted the kinetic data using the capacitance values of the FCSC ankle spring without additional sensors.

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.

A Novel Method for Detecting Misclassifications of the Locomotion Mode in Lower-Limb Exoskeleton Robot Control

Lower-limb exoskeleton robots can support hemiplegic patients’ affected limbs and assist in their rehabilitation. In order to set effective control strategies, it is necessary to obtain the user’s motion intention accurately and timeously. These requirements pose many challenges. The surface electromyography (sEMG) signal has long been used for detecting a person’s locomotion mode. However, most traditional myoelectric motion recognition methods need to collect multi-channel sEMG signals (more than eight) and use feature engineering to obtain the desired accuracy. Additionally, traditional methods make decisions when the recognition probability is still uncertain, resulting in misclassification during continuous recognition. In order to overcome these limitations, a dual-purpose autoencoder-guided temporal convolution network (DA-TCN) is proposed, which is used as a motion detection model for a lower-limb exoskeleton robot based on sEMG. In the proposed method, we first obtain four-channel sEMG signals without any feature engineering. Next, the DA-TCN is used to obtain the discriminative deep features of the sEMG signals. Compared with separable features from common TCNs, discriminative features assist in obtaining a more discriminative twins-loss. Finally, the classification result is redetermined by the decoder, based on the reconstruction of deep features, and misclassifications are rejected based on the value of the twins-loss. The performance of the proposed method was evaluated by obtaining data from seven volunteer subjects in seven locomotion modes. The proposed method was able to effectively detect misclassifications and significantly improved the stability of continuous recognition.

An sEMG-Based Human-Exoskeleton Interface Fusing Convolutional Neural Networks With Hand-Crafted Features.

In recent years, the human-robot interfaces (HRIs) based on surface electromyography (sEMG) have been widely used in lower-limb exoskeleton robots for movement prediction during rehabilitation training for patients with hemiplegia. However, accurate and efficient lower-limb movement prediction for patients with hemiplegia remains a challenge due to complex movement information and individual differences. Traditional movement prediction methods usually use hand-crafted features, which are computationally cheap but can only extract some shallow heuristic information. Deep learning-based methods have a stronger feature expression ability, but it is easy to fall into the dilemma of local features, resulting in poor generalization performance of the method. In this article, a human-exoskeleton interface fusing convolutional neural networks with hand-crafted features is proposed. On the basis of our previous study, a lower-limb movement prediction framework (HCSNet) in patients with hemiplegia is constructed by fusing time and frequency domain hand-crafted features and channel synergy learning-based features. An sEMG data acquisition experiment is designed to compare and analyze the effectiveness of HCSNet. Experimental results show that the method can achieve 95.93 and 90.37% prediction accuracy in both within-subject and cross-subject cases, respectively. Compared with related lower-limb movement prediction methods, the proposed method has better prediction performance.

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.

Coordination Between Partial Robotic Exoskeletons and Human Gait: A Comprehensive Review on Control Strategies.

Lower-limb robotic exoskeletons have become powerful tools to assist or rehabilitate the gait of subjects with impaired walking, even when they are designed to act only partially over the locomotor system, as in the case of unilateral or single-joint exoskeletons. These partial exoskeletons require a proper method to synchronize their assistive actions and ensure correct inter-joint coordination with the user’s gait. This review analyzes the state of the art of control strategies to coordinate the assistance provided by these partial devices with the actual gait of the wearers. We have analyzed and classified the different approaches independently of the hardware implementation, describing their basis and principles. We have also reviewed the experimental validations of these devices for impaired and unimpaired walking subjects to provide the reader with a clear view of their technology readiness level. Eventually, the current state of the art and necessary future steps in the field are summarized and discussed.