Structure Design of Lower Limb Exoskeletons for Gait Training

Due to the close physical interaction between human and machine in process of gait training, lower limb exoskeletons should be safe, comfortable and able to smoothly transfer desired driving force/moments to the patients. Correlatively, in kinematics the exoskeletons are required to be compatible with human lower limbs and thereby to avoid the uncontrollable interactional loads at the human-machine interfaces. Such requirement makes the structure design of exoskeletons very difficult because the human-machine closed chains are complicated. In addition, both the axis misalignments and the kinematic character difference between the exoskeleton and human joints should be taken into account. By analyzing the DOF（degree of freedom） of the whole human-machine closed chain, the human-machine kinematic incompatibility of lower limb exoskeletons is studied. An effective method for the structure design of lower limb exoskeletons, which are kinematically compatible with human lower limb, is proposed. Applying this method, the structure synthesis of the lower limb exoskeletons containing only one-DOF revolute and prismatic joints is investigated; the feasible basic structures of exoskeletons are developed and classified into three different categories. With the consideration of quasi-anthropopathic feature, structural simplicity and wearable comfort of lower limb exoskeletons, a joint replacement and structure comparison based approach to select the ideal structures of lower limb exoskeletons is proposed, by which three optimal exoskeleton structures are obtained. This paper indicates that the human-machine closed chain formed by the exoskeleton and human lower limb should be an even-constrained kinematic system in order to avoid the uncontrollable human-machine interactional loads. The presented method for the structure design of lower limb exoskeletons is universal and simple, and hence can be applied to other kinds of wearable exoskeletons.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings.Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm.The Radial Basis Function(RBF)neural network is used widely to compensate for modeling errors.In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory tracking.It innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling errors.The stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the simulation.Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,respectively.The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform

In this study,a humanoid prototype of 2-DOF(degrees of freedom)lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton.To improve the detection accuracy of the humanrobot interaction torque,a BPNN(backpropagation neural networks)is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor.Meanwhile,the backstepping controller is designed to realize the exoskeleton's passive position control,which means that the person passively adapts to the exoskeleton.On the other hand,a variable admittance controller is used to implement the exoskeleton's active followup control,which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance.To improve the wearable comfortable effect,serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters.Finally,the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.

Robust Sliding Mode Control for a 2-DOF Lower Limb Exoskeleton Base on Linear Extended State Observer

For the 2-Degree of Freedom(DOF)lower limb exoskeleton,to ensure the system robustness and dynamic performance,a linearextended-state-observer-based(LESO)robust sliding mode control is proposed to not only reduce the influence of parametric uncertainties,unmodeled dynamics,and external disturbance but also estimate the unmeasurable real-time joint angular velocity directly.Then,via Lyapunov technology,the stability of the corresponding LESO and controller is proven.The appropriate and reasonable simulation was carried out to verify the effectiveness of the proposed LESO and exoskeleton controller.

Unknown system dynamics estimator-based impedance control for lower limb exoskeleton with enhanced performance

In this article, an unknown system dynamics estimator-based impedance control method is proposed for the lower limb exoskeleton to stimulate the tracking flexibility with the terminal target position when suffering parametric inaccuracies and unexpected disturbances. To reinforce the robust performance, via constructing the filtering operation-based dynamic relation, i.e., invariant manifold, the unknown system dynamics estimators are employed to maintain the accurate perturbation identification in both the hip and knee subsystem. Besides, a funnel control technique is designed to govern the convergence process within a minor overshoot and a higher steady-state precision. Meanwhile, an interactive complaint result can be obtained with the aid of the impedance control, where the prescribed terminal trajectory can be adjusted into the interaction variable-based target position by the force–position mapping, revealing the dynamic influence between the impedance coefficient (stiffness and damping) and the adjusted position magnitude. A sufficient stability analysis verifies the ultimately uniformly bounded results of all the error signals, and even the angle errors can be regulated within the predefined funnel boundary in the whole convergence. Finally, some simulations are provided to demonstrate the validity and superiority including the enhanced interaction flexibility and robustness.

A Comparison of Four Neural Networks Algorithms on Locomotion Intention Recognition of Lower Limb Exoskeleton Based on Multi-source Information

Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.

Review of Power-Assisted Lower Limb Exoskeleton Robot

Power-assisted lower limb exoskeleton robot is a wearable intelligent robot system involving mechanics,materials,electronics,control,robotics,and many other fields.The system can use external energy to provide additional power to humans,enhance the function of the human body,and help the wearer to bear weight that is previously unbearable.At the same time,employing reasonable structure design and passive energy storage can also assist in specific actions.First,this paper introduces the research status of power-assisted lower limb exoskeleton robots at home and abroad,and analyzes several typical prototypes in detail.Then,the key technologies such as structure design,driving mode,sensing technology,control method,energy management,and human-machine coupling are summarized,and some common design methods of the exoskeleton robot are summarized and compared.Finally,the existing problems and possible solutions in the research of power-assisted lower limb exoskeleton robots are summarized,and the prospect of future development trend has been analyzed.

A review of the design of load-carrying exoskeletons

The increasing necessity of load-carrying activities has led to greater human musculoskeletal damage and an increased metabolic cost.With the rise of exoskeleton technology,researchers have begun exploring different approaches to developing wearable robots to augment human load-carrying ability.However,there is a lack of systematic discussion on biomechanics,mechanical designs,and augmentation performance.To achieve this,extensive studies have been reviewed and 108 references are selected mainly from 2013 to 2022 to address the most recent development.Other earlier 20 studies are selected to present the origin of different design principles.In terms of the way to achieve load-carrying augmentation,the exoskeletons reviewed in this paper are sorted by four categories based on the design principles,namely load-suspended backpacks,lower-limb exoskeletons providing joint torques,exoskeletons transferring load to the ground and exoskeletons transferring load between body segments.Specifically,the driving modes of active and passive,the structure of rigid and flexible,the conflict between assistive performance and the mass penalty of the exoskeleton,and the autonomy are discussed in detail in each section to illustrate the advances,challenges,and future trends of exoskeletons designed to carry loads.

A Novel Gait Pattern Recognition Method Based on LSTM-CNN for Lower Limb Exoskeleton

This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exoskeleton to collect motion information,which is used for LSTM-CNN input.This article considers five common gait patterns,including walking,going up stairs,going down stairs,sitting down,and standing up.In the LSTM-CNN model,the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features.To optimize the deep neural network structure proposed in this paper,some hyperparameter selection experiments were carried out.In addition,to verify the superiority of the proposed recognition method,the method is compared with several common methods such as LSTM,CNN and SVM.The results show that the average recognition accuracy can reach 97.78%,which has a good recognition eff ect.Finally,according to the experimental results of gait pattern switching,the proposed method can identify the switching gait pattern in time,which shows that it has good real-time performance.

Compliant Control of Lower Limb Rehabilitation Exoskeleton Robot Based on Flexible Transmission

To ensure the safety, comfort, and effectiveness of lower limb rehabilitation exoskeleton robots in the rehabilitation training process, compliance is a prerequisite for human–machine interaction safety. First, under the premise of considering the mechanical structure of the lower limb rehabilitation exoskeleton robot (LLRER), when conducting the dynamic transmission of the exoskeleton knee joint, the soft axis is added to ensure that the rotation motion and torque are flexibly transmitted to any position to achieve flexible force transmission. Second, to realize the active compliance control of LLRER, the sliding mode impedance closed-loop controller is developed based on the kinematics and dynamics model of LLRER, and the stability of the designed control system is verified by Lyapunov method. Then the experiment is designed to track the collected bicycle rehabilitation motion data stably, and the algorithm and dynamic model are verified to satisfy the experimental requirements. Finally, aiming at the transmission efficiency and response performance of the soft shaft in the torque transmission process of the knee joint, the soft shaft transmission performance test is carried out to test the soft shaft transmission performance and realize the compliance of the LLRER, so as to ensure that the rehabilitation training can be carried out in a safe and comfortable interactive environment. Through the design of rehabilitation exercise training, it is verified that the LLRER of flexible transmission under sliding mode impedance control has good adaptability in the actual environment, and can achieve accurate and flexible control. During the experiment, the effectiveness of monitoring rehabilitation training is brought through the respiratory belt.

Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system,which conducts natural and close cooperation with the human by recognizing human locomotion timely.Requiring subject-specific training is the main challenge of the existing approaches,and most methods have the problem of insufficient recognition.This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition(LMR)method based on GA-CNN for a lower limb exoskeleton system.The LMR method is a combination of Convolutional Neural Networks(CNN)and Genetic Algorithm(GA)-based multi-sensor information selection.To improve network performance,the hyper-parameters are optimized by Bayesian optimization.An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method.Twelve locomotion modes,which composed an integral locomotion system for the daily application of the exoskeleton,can be recognized by the proposed method.According to a series of experiments,the recognizer shows strong comprehensive abilities including high accuracy,low delay,and sufficient adaption to different subjects.

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

As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer’s movement,a motion generation process often plays an important role in high-level control.One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations.In this paper,we first describe a novel motion modeling method based on probabilistic movement primitive(ProMP)for a lower limb exoskeleton,which is a new and powerful representative tool for generating motion trajectories.To adapt the trajectory to different situations when the exoskeleton is used by different wearers,we propose a novel motion learning scheme based on black-box optimization(BBO)PIBB combined with ProMP.The motion model is first learned by ProMP offline,which can generate reference trajectories for use by exoskeleton controllers online.PIBB is adopted to learn and update the model for online trajectory generation,which provides the capability of adaptation of the system and eliminates the effects of uncertainties.Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.

Minimum-Time and Minimum-Jerk Gait Planning in Joint Space for Assistive Lower Limb Exoskeleton

Assistive lower limb exoskeleton robot has been developed to help paraplegic patients walk again.A gait planning method of this robot must be able to plan a gait based on gait parameters,which can be changed during the stride according to human intention or walking conditions.The gait is usually planned in cartesian space,which has shortcomings such as singularities that may occur in inverse kinematics equations,and the angular velocity of the joints cannot be entered into the calculations.Therefore,it is vital to have a gait planning method in the joint space.In this paper,a minimum-time and minimum-jerk planner is proposed for the robot joints.To do so,a third-order system is defined,and the cost function is introduced to minimize the jerk of the joints throughout the stride.The minimum time required is calculated to keep the angular velocity trajectory within the range specified by the motor’s maximum speed.Boundary conditions of the joints are determined to secure backward balance and fulfill gait parameters.Finally,the proposed gait planning method is tested by its implementation on the Exoped®exoskeleton.

Estimation of the interaction force between human and passive lower limb exoskeleton device during level ground walking

To develop sophisticated and efficient control strategies for exoskeleton devices,acquiring the information of interaction forces between the wearer and the wearable device is essential.However,obtaining the interaction force via conventional methods,such as direct measurement using force sensors,is problematic.This paper proposes a kinematic data-based estimation method to evaluate the interaction force between human lower limbs and passive exoskeleton links during level ground walking.Unlike conventional methods,the proposed method requires no force sensors and is computationally cheaper to obtain the calculation results.To obtain more accurate kinematic data,a marker refinement algorithm based on bilevel optimization framework is adopted.The interaction force is evaluated by a spring model,which is used to imitate the binding behavior between human limbs and the exoskeleton links.The deflection of the spring model is calculated based on the assumption that the phase delay between human limb and exoskeleton link can be presented by the sequence of frames of kinematic data.Experimental results of six subjects indicate that our proposed method can estimate the interaction forces during level ground walking.Moreover,a case study of bandage location optimization is conducted to demonstrate the usefulness of obtaining the interaction information.