Lower Limb Exoskeleton Research Articles

Lower Limb Exoskeleton With Energy-Storing Mechanism for Spinal Cord Injury Rehabilitation

Lower Limb Exoskeleton With Energy-Storing Mechanism for Spinal Cord Injury Rehabilitation

Hybrid Filtered Disturbance Observer for Precise Motion Generation of a Powered Exoskeleton

Lower-limb exoskeletons are promising applications of robotic rehabilitation for people with motor impairment. As current studies have tailored the design of gait trajectories for the target users, realizing a high-precision motion control is a critical issue for safe and effective assistance. The walking assistance involves unique characteristic phases that embody different physical constraints and requirements for assistance. Conventional methods often utilized gain-switching control for time-varying adaptation. However, despite their intuitiveness as well as simplicity, the control performance was unsatisfying due to unmodeled responses by human behavior and continuous interaction with the external environment. To tackle these challenges, this study proposes a hybrid control method applied to the disturbance observer that can provide robust robotic rehabilitation. The proposed method adaptively identifies the exoskeletal system as a hybrid nominal model and online exchanges model-based tracking controllers parallelly to the gait phase of a user. Furthermore, a unique filter named allowance filter is introduced to compensate for the plant dynamics, preventing instability of the inverted plant and realizing digital implementation. In this article, a practical user with complete paraplegia participated in the experiments for verification of the proposed methods.

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.

Research on Multimodal Interaction System of Lower Limb Exoskeleton Oriented to Competitive Training

Research on Multimodal Interaction System of Lower Limb Exoskeleton Oriented to Competitive Training

Multi-motion Compound Passive Lower Limb Exoskeleton Design and Its Intelligent Interaction Evaluation

Multi-motion Compound Passive Lower Limb Exoskeleton Design and Its Intelligent Interaction Evaluation

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.

Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization

Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization

Sliding Mode-Based Active Disturbance Rejection Control of Assistive Exoskeleton Device for Rehabilitation of Disabled Lower Limbs.

In this study, a hybrid control strategy is proposed to improve the tracking performance of lower limb exoskeleton system dedicated for rehabilitation the motion of hip and knee limbs in disabled persons. The proposed controller together with exoskeleton device is practically instructive to make exercises for people suffering weakness in their lower limbs. The proposed controller combined both active disturbance rejection control (ADRC) with sliding mode control (SMC) to get their powerful characteristics in terms of rejection capability and robustness characteristics. The dynamic modelling of swinging lower limbs are developed and the controller has been designed accordingly. The numerical simulations have been conducted to validate the effectiveness of proposed controller. A comparison study in performance has been performed between the proposed controller and the traditional controller ADRC based on proportional-derivative controller. The simulated results showed that the proposed controller has better tracking performance than conventional version. In addition, the results showed that the sliding mode-based ADRC can considerably reduce the chattering level and better rejection capability, fast tracking behavior and less control effort.

Effect of lower limb exoskeleton on the modulation of neural activity and gait classification.

Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user's neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten able-bodied volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13 ± 3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3 ± 4.8%, while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4 ± 11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy.

An Intelligent Rehabilitation Assessment Method for Stroke Patients Based on Lower Limb Exoskeleton Robot.

The 6-min walk distance (6MWD) and the Fugl-Meyer assessment lower-limb subscale (FMA-LE) of the stroke patients provide the critical evaluation standards for the effect of training and guidance of the training programs. However, gait assessment for stroke patients typically relies on manual observation and table scoring, which raises concerns about wasted manpower and subjective observation results. To address this issue, this paper proposes an intelligent rehabilitation assessment method (IRAM) for rehabilitation assessment of the stroke patients based on sensor data of the lower limb exoskeleton robot. Firstly, the feature parameters of the patient were collected, including age, height, and duration, etc. The sensor data of the exoskeleton robot were also collected, including joint angle, joint velocity, and joint torque, etc. Secondly, a gait feature model was constructed to deduce the walking gait parameters of the patient according to the sensor data of the exoskeleton, including the support phase to swing phase ratio, step length and leg lift height of the patient, etc. Then, the 6MWD and FMA-LE values were collected by traditional methods, feature parameters, gait parameters and human-machine interaction parameters (joint torque) of the patient were adopted to train the rehabilitation assessment model. Finally, the assessment model was trained by a machine-learning based algorithm. The new stroke patients' the 6MWD and FMA-LE values can be predicted by the trained model. The experimental results present that the prediction accuracy for the 6MWD and FMA-LE values reach to 85.19% and 92.66%, respectively.

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.

A Cable-driven Exoskeleton with Personalized Assistance Improves the Gait Metrics of People in Subacute Stroke.

Most stroke survivors have mobility deficits and show a pathological gait pattern. Seeking to enhance the gait performance among this population, we have developed a hybrid cable-driven lower limb exoskeleton (called SEAExo). This study aimed to determine the effects of SEAExo with personalized assistance on immediate changes in gait performance of people after stroke. Gait metrics (i.e., the foot contact angle, knee flexion peak, temporal gait symmetry indices) and muscle activities were the primary outcomes to evaluate the assistive performance. Seven subacute stroke survivors participated and completed the experiment with three comparison sessions, i.e., walking without SEAExo (served as baseline) and without/with personalized assistance, at their preferred walking speeds. Compared to the baseline, we observed increases in the foot contact angle and knee flexion peak by 70.1% (p < 0.05) and 60.0% (p < 0.05) with personalized assistance. Personalized assistance contributed to the improvements in temporal gait symmetry of more impaired participants (p < 0.05), and it led to a 22.8% and 51.3% (p < 0.05) reduction in the muscle activities of ankle flexor muscles. These results demonstrate that SEAExo with personalized assistance has the potential to enhance post-stroke gait rehabilitation in real-world clinical settings.

Efficacy of stumble recovery assistance in a knee exoskeleton for individuals with simulated mobility impairment: A pilot study.

Falls due to stumbles are a major cause of injury for many populations, and as such interventions to reduce fall risk have been a key focus of rehabilitation research. However, dedicated stumble recovery assistance in a powered lower-limb exoskeleton has yet to be explored as a fall mitigation intervention. Thus young, healthy adults () were recruited for a stumble recovery experiment to test the efficacy of knee exoskeleton stumble recovery assistance in improving an impaired stumble recovery response (i.e., the elevating strategy response). Leg weights were attached unilaterally to each participant's shank to simulate walking and stumble recovery impairment, and a unilateral powered knee exoskeleton was worn on the same leg for walking and stumble recovery assistance. Ultimately, knee exoskeleton stumble recovery assistance served to improve participants' elevating limb kinematics (i.e., increase thigh and knee motion) and reduce overall fall risk (i.e., reduce trunk motion and improve foot placement) during responses relative to their impaired response (i.e., with the leg weights and no assistance), and relative to their response while receiving only walking assistance. This initial exploration provides a first indication that knee exoskeleton stumble recovery assistance is a viable approach to improving an impaired stumble recovery response, which could serve two important use cases: (1) a safety mechanism for existing exoskeleton wearers, who may be less capable of recovering from stumbles due to the added weight or joint impedance of the device; (2) an external stumble recovery aid for fall-prone populations, such as the elderly or stroke survivors.

A Human Lower Limb Mechanical Phantom for the Testing of Knee Exoskeletons.

The development of assistive lower-limb exoskeletons can be time-consuming. Testing prototype medical devices on vulnerable populations such as children also has safety concerns. Mechanical phantoms replicating the lower-limb kinematics provide an alternative for the fast validation and iteration of exoskeletons. However, most phantoms treat the limbs as rigid bodies and fail to capture soft tissue deformation at the human/exoskeleton interface. Human soft tissue can absorb and dissipate energy when compressed, leading to a mismatch between simulated and human exoskeleton testing outcomes. We have developed a methodology for quickly testing and validating the performance of knee exoskeletons using a mechanical phantom capable of emulating knee kinematics soft-tissue deformation of the lower-limb. Our phantom consisted of 3D-printed bones surrounded by ballistic gel. A motorized hexapod moved the knee to follow a walking trajectory. A custom inverse dynamics model estimated the knee assistance moment from marker and load cell data. We applied this methodology to quantify the effects of soft-tissue deformation on exoskeleton assistance by loading the phantom knee with a torsional spring exoskeleton interfacing and bypassing the ballistic gel. We found that including soft-tissue deformation led to a lower knee assistance moment and stiffness. Some but not all of this difference could be explained by the deflection of the exoskeleton relative to the knee angle, suggesting energy absorption within soft tissue. The direct measurements of exoskeleton assistance provide insight into increasing the assistive moment transmission efficacy. The phantom provided a relatively accurate framework for knee exoskeleton testing, aiding future exoskeleton design.

Sensitivity Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

The lower-limb exoskeleton for human performance augmentation (LEHPA) in sensitivity amplification control (SAC) is vulnerable to model parameter uncertainties and unmodeled dynamics due to its large sensitivity to external disturbances. This paper proposes sensitivity adaptation based on deep reinforcement learning (SADRL) to reduce the dependence on the model accuracy and tackle the ever-changing human-exoskeleton interaction (HEI) dynamics by interpreting the sensitivity adjustment as a Markov Decision Process (MDP). The agent learns an appropriate sensitivity for each exoskeleton state. To train the control policy safely and efficiently, a multibody simulation environment is created to implement the training process, accompanied by a novel hybrid inverse-forward dynamics simulation method to carry out the simulation. For comparison purposes, the SAC controller is introduced as a benchmark. A novel performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to evaluate the control effect of the trained SADRL controller quantitatively. The SADRL controller is compared with the SAC controller at five specified walking speeds, resulting in a lumped HEI force ratio of 0.54. The total decrease of HEI forces demonstrates the superior control effect of the SADRL strategy.

End-to-End High-Level Control of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

End-to-End High-Level Control of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

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%.

A Novel Deep Reinforcement Learning Based Framework for Gait Adjustment

Nowadays, millions of patients suffer from physical disabilities, including lower-limb disabilities. Researchers have adopted a variety of physical therapies based on the lower-limb exoskeleton, in which it is difficult to adjust equipment parameters in a timely fashion. Therefore, intelligent control methods, for example, deep reinforcement learning (DRL), have been used to control the medical equipment used in human gait adjustment. In this study, based on the key-value attention mechanism, we reconstructed the agent’s observations by capturing the self-dependent feature information for decision-making in regard to each state sampled from the replay buffer. Moreover, based on Softmax Deep Double Deterministic policy gradients (SD3), a novel DRL-based framework, key-value attention-based SD3 (AT_SD3), has been proposed for gait adjustment. We demonstrated the effectiveness of our proposed framework in gait adjustment by comparing different gait trajectories, including the desired trajectory and the adjusted trajectory. The results showed that the simulated trajectories were closer to the desired trajectory, both in their shapes and values. Furthermore, by comparing the results of our experiments with those of other state-of-the-art methods, the results proved that our proposed framework exhibited better performance.

Interaction learning control with movement primitives for lower limb exoskeleton.

Research on robotic exoskeletons both in the military and medical fields has rapidly expanded over the previous decade. As a human-robot interaction system, it is a challenge to develop an assistive strategy that makes the exoskeleton supply efficient and natural assistance following the user's intention. This paper proposed a novel interaction learning control strategy for the lower extremity exoskeleton. A powerful representative tool probabilistic movement primitives (ProMPs) is adopted to model the motion and generate the desired trajectory in real-time. To adjust the trajectory by the user's real-time intention, a compensation term based on human-robot interaction force is designed and merged into the ProMPs model. Then, compliant impedance control is adopted as a low-level control where the desired trajectory is put into. Moreover, the model will be dynamically adapted online by penalizing both the interaction force and trajectory mismatch, with all the parameters that can be further learned by learning algorithm PIBB. The experimental results verified the effectiveness of the proposed control framework.

Feasibility and Application of the B.E.A.T. Testbed for Assessing the Effects of Lower Limb Exoskeletons on Human Balance

Assessing the performance of exoskeletons in assisting human balance is important for their design process. This study proposes a novel testbed, the B.E.A.T (Balance Evaluation Automated Testbed) to address this aim. We applied the B.E.A.T to evaluate how the presence of a lower limb exoskeleton influenced human balance. The B.E.A.T. consists of a robotic platform, standardized protocols, and performance indicators. Fifteen healthy subjects were enrolled and subjected to repeatable step-type ground perturbations in different directions using the multi-axis robotic platform. Each participant performed three trials, both with and without the exoskeleton (EXO and No-EXO conditions). Nine performance indicators, divided into kinematic and body stability indicators, were computed. The reliability of performance indicators was assessed by computing the Inter Class Correlation (ICC). The indicators showed good (0.60 ≤ ICC &lt; 0.75) to excellent (ICC ≥ 0.75) reliability. The comparison between the EXO and No-EXO conditions revealed a significant increase in the joint range of motion and the center of pressure displacement while wearing the exoskeleton. The main differences between the EXO and No-EXO conditions were found in the range of motion of the knee joints, with an increment up to 17° in the sagittal plane.