Lower Limb Exoskeleton Robot Research Articles

Analysis and experimental research on stability characteristics of squatting posture of wearable lower limb exoskeleton robot

Aiming at the problems of complex movement posture, many degrees of freedom and flexible movement of the human body’s lower limbs, a dynamic mathematical model of the squat posture was established, and analyzed the dynamic parameters of the hip joint, knee joint and ankle joint during the squatting posture of the wearable lower extremity exoskeleton robot. Based on the MATLAB simulation software, the motion characteristics of the squat posture are simulated and analyzed, and the results show that: in the process of squatting, the theoretical calculation results are quite different from the simulation results, indicating that the process of squatting is unstable. Therefore, based on the Zero Moment Point (ZMP) theory, a stable polygon of the squat posture is established, the instability rate is defined. From a qualitative and quantitative point of view, the effects of the squat posture and the change of the center of gravity (COG) on the motion stability are analyzed. An experimental platform was built and the experiment was performed to verify the COG and ZMP changes in the squatting posture, which verified the correctness of the theoretical calculation conclusions and the influence of COG changes on ZMP.

Vision-Assisted Autonomous Lower-Limb Exoskeleton Robot

Lower-limb exoskeleton robots can effectively help patients with lower-limb disabilities caused by stroke or spinal cord injury to walk again. However, when faced with complex ground surfaces, patients find it difficult to quickly and accurately transmit the motion intention to the robot, resulting in falls or errors. In this article, we develop the vision-assisted autonomous lower-limb exoskeleton robot (VALOR). Based on the principles of human visual feedback and motion decision-making, a vision-assisted autonomous gait pattern planning method is proposed to improve the adaptability of the robot to the environment. The robot obtains environmental information via an RGB-D camera and extracts the ground object features that might affect gait. Then, the robot makes an autonomous decision according to the environmental features, robot state, and safety constraints. Lastly, a suitable step length and height are given to the parameterized gait pattern planning model of robot to assist with walking. The feasibility of the proposed method is verified on VALOR in a controlled indoor environment with limited obstacles, and our results demonstrate that the method can significantly improve robot adaptability to complex walking environments.

Research on a gait detection system and recognition algorithm for lower limb exoskeleton robot

Research on a gait detection system and recognition algorithm for lower limb exoskeleton robot

A BMI Based on Motor Imagery and Attention for Commanding a Lower-Limb Robotic Exoskeleton: A Case Study

Lower-limb robotic exoskeletons are wearable devices that can be beneficial for people with lower-extremity motor impairment because they can be valuable in rehabilitation or assistance. These devices can be controlled mentally by means of brain–machine interfaces (BMI). The aim of the present study was the design of a BMI based on motor imagery (MI) to control the gait of a lower-limb exoskeleton. The evaluation is carried out with able-bodied subjects as a preliminary study since potential users are people with motor limitations. The proposed control works as a state machine, i.e., the decoding algorithm is different to start (standing still) and to stop (walking). The BMI combines two different paradigms for reducing the false triggering rate (when the BMI identifies irrelevant brain tasks as MI), one based on motor imagery and another one based on the attention to the gait of the user. Research was divided into two parts. First, during the training phase, results showed an average accuracy of 68.44 ± 8.46% for the MI paradigm and 65.45 ± 5.53% for the attention paradigm. Then, during the test phase, the exoskeleton was controlled by the BMI and the average performance was 64.50 ± 10.66%, with very few false positives. Participants completed various sessions and there was a significant improvement over time. These results indicate that, after several sessions, the developed system may be employed for controlling a lower-limb exoskeleton, which could benefit people with motor impairment as an assistance device and/or as a therapeutic approach with very limited false activations.

Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network.

A lower-limb exoskeleton robot identifies the wearer′s walking intention and assists the walking movement through mechanical force; thus, it is important to be able to identify the wearer′s movement in real-time. Measurement of the angle of the knee and ankle can be difficult in the case of patients who cannot move the lower-limb joint properly. Therefore, in this study, the knee angle as well as the angles of the talocrural and subtalar joints of the ankle were estimated during walking by applying the neural network to two inertial measurement unit (IMU) sensors attached to the thigh and shank. First, for angle estimation, the gyroscope and accelerometer data of the IMU sensor were obtained while walking at a treadmill speed of 1 to 2.5 km/h while wearing an exoskeleton robot. The weights according to each walking speed were calculated using a neural network algorithm programmed in MATLAB software. Second, an appropriate weight was selected according to the walking speed through the IMU data, and the knee angle and the angles of the talocrural and subtalar joints of the ankle were estimated in real-time during walking through a feedforward neural network using the IMU data received in real-time. We confirmed that the angle estimation error was accurately estimated as 1.69° ± 1.43 (mean absolute error (MAE) ± standard deviation (SD)) for the knee joint, 1.29° ± 1.01 for the talocrural joint, and 0.82° ± 0.69 for the subtalar joint. Therefore, the proposed algorithm has potential for gait rehabilitation as it addresses the difficulty of estimating angles of lower extremity patients using torque and EMG sensors.

Research on HMM-based Exoskeleton Robot Falling Prediction Algorithm

Exoskeleton robot is essentially a wearable robot. In recent years, the research of exoskeleton robots has become a new hot spot, and has gradually been widely used in military, medical and civilian fields. However, the balance and safety issues of power-assisted exoskeleton robots need to be solved urgently. At present, although there are many researches on exoskeleton robots at home and abroad, the research on the falling problem of assisted lower limb exoskeleton robots is not in-depth. Existing studies on falls are mainly focused on the detection of falls, while the prediction of falls is relatively small, and these studies rely more on a single sensor, and its accuracy needs to be improved. In response to the status quo, This paper proposes a fall prediction algorithm based on HMM to study the fall behaviour of the exoskeleton human-machine system in advance, it provides a reference for the anti-fall measures of the exoskeleton robot and aims to effectively reduce the damage of the human-machine system.

Robust adaptive PD-like control of lower limb rehabilitation robot based on human movement data.

The combination of biomedical engineering and robotics engineering brings hope of rehabilitation to patients with lower limb movement disorders caused by diseases of the central nervous system. For the comfort during passive training, anti-interference and the convergence speed of tracking the desired trajectory, this paper analyzes human body movement mechanism and proposes a robust adaptive PD-like control of the lower limb exoskeleton robot based on healthy human gait data. In the case of bounded error perturbation, MATLAB simulation verifies that the proposed method can ensure the global stability by introducing an S-curve function to make the design robust adaptive PD-like control. This control strategy allows the lower limb rehabilitation robot to track the human gait trajectory obtained through the motion capture system more quickly, and avoids excessive initial output torque. Finally, the angle similarity function is used to objectively evaluate the human body for wearing the robot comfortably.

Impedance control based on optimal adaptive high order super twisting sliding mode for a 7-DOF lower limb exoskeleton

In this paper, a novel hybrid optimal Adaptive High-Order Super Twisting Sliding Mode (AHOSTWSM) impedance control is proposed for a lower limb exoskeleton robot with 7 active joints, which takes the interaction forces between the robot and the user into account. First, the dynamic model of the robot is extracted using Lagrange approach. Then, an adaptive super twisting sliding mode controller is designed based on Lyapanov theory. Moreover, the interaction forces are obtained in every time-step and applied to the robot as a compensating torque to guard against disturbances. The desired trajectory of the upper limb joint has been extracted so that the stability of the robot is achieved based on Zero Moment Point (ZMP) criterion at any moment. Moreover, to achieve optimal performance (maximum stability, minimum tracking error, reduced interacting forces, and optimal energy consumption), parameters of the proposed controller, the upper limb desired trajectory, and the impedance system are optimized using Harmony Search Algorithm (HSA). Finally, in order to demonstrate the effectiveness of the proposed controller, optimal sliding mode controller (SMC) is designed and performance of the AHOSTWSM is compared with SMC. The simulation results reveal the superiority of the controller in comparison with SMC.

Gait pattern generation algorithm for lower-extremity rehabilitation–exoskeleton robot considering wearer’s condition

Various exoskeleton robots are being developed according to users’ conditions. Lower limb exoskeleton robots have attracted much attention for performing walking motions, a basic exercise for paralyzed patients. In this study, the walking environment of an exoskeleton robot wearer who walks using a crutch is identified as the foot position of the crutch support point and the support leg; therefore, the upcoming foothold location is calculated and sets as a control target. Rather than using preset pedestrian patterns, pedestrian patterns that can match the environment are created using the dynamic movement primitives machine learning technology. We evaluated the exoskeleton robot through an experiment with a healthy 29-year-old male. The experimental results showed that the exoskeleton robot was able to demonstrate the adaptive strides according to the wearer’s intention.

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

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.

Design of a 2DoF Ankle Exoskeleton with a Polycentric Structure and a Bi-Directional Tendon-Driven Actuator Controlled Using a PID Neural Network

Lower limb exoskeleton robots help with walking movements through mechanical force, by identifying the wearer’s walking intention. When the exoskeleton robot is lightweight and comfortable to wear, the stability of walking increases, and energy can be used efficiently. However, because it is difficult to implement the complex anatomical movements of the human body, most are designed simply. Due to this, misalignment between the human and robot movement causes the wearer to feel uncomfortable, and the stability of walking is reduced. In this paper, we developed a two degrees of freedom (2DoF) ankle exoskeleton robot with a subtalar joint and a talocrural joint, applying a four-bar linkage to realize the anatomical movement of a simple 1DoF structure mainly used for ankles. However, bidirectional tendon-driven actuators (BTDAs) do not consider the difference in a length change of both cables due to dorsiflexion (DF) and plantar flexion (PF) during walking, causing misalignment. To solve this problem, a BTDA was developed by considering the length change of both cables. Cable-driven actuators and exoskeleton robot systems create uncertainty. Accordingly, adaptive control was performed with a proportional-integral-differential neural network (PIDNN) controller to minimize system uncertainty.

Control of twin-double pendulum lower extremity exoskeleton system with fuzzy logic control method

In this article, a two degree of freedom lower-limb exoskeleton (LLE) design control is developed to reduce the fatigue level of healthy people and increase their load-carrying capacity. The LLE robot system is designed by comparing it to the twin-double pendulum system. One of the twin pendulums models is the human leg (with a knee and hip joint), and the other twin pendulum model is the exoskeleton robot. The movement of the human knee and hip joints in a walking pattern is recreated with a joint angle generator and applied to the joints with the help of a linear motor. The exoskeleton robot is provided to follow the movements of the leg with the help of a fuzzy controller. The control simulation with the mathematical model of the twin-double pendulum system and the fuzzy logic method was made using the MATLAB/Simulink program. The system response was analyzed and graphed for two different limb sizes (45 cm and 50 cm) and three different load conditions (no load, 25 Nm and 75 Nm). The maximum tracking errors are 3.2105° and 3.4730° for the hip and knee joint, respectively, with a 75 Nm load disturbance condition. These tracking error values can be interpreted as very low with high tracking success. The FL controller is robust to load changing and limb size-changing factors, and for this reason, it was suitable for use in LLEs.

Design on electrohydraulic servo driving system with walking assisting control for lower limb exoskeleton robot

According to the characteristics of human gait and the requirements of power assistance, locomotive mechanisms and electrohydraulic servo driving are designed on a lower limb exoskeleton robot, in which the miniaturization and lightweight of driving system are realized. The kinematics of the robot is analyzed and verified via the typical movements of the exoskeleton. In this article, the simulation on the power of joints during level walking was analyzed in ADAMS 2016, which is a multibody simulation and motion analysis software. Motion ranges and driving strokes are then optimized. A proportional integral derivative (PID) control method with error estimation and pressure compensation is proposed to satisfy the requirements of joints power assistance and comply with the motion of human lower limb. The proposed method is implemented into the exoskeleton for assisted walking and is verified by experimental results. Finally, experiments show that the tracking accuracy and power-assisted performance of exoskeleton robot joints are improved.

Subjective Evaluation of the Effect of Exoskeleton Robots for Rehabilitation Training

Recently, the interest in the lower-limb exoskeleton robot is increasing within rehabilitation practice. While research on advancing the gait rehabilitation exoskeleton robot (ER) is being actively conducted from a technical point of view, studies evaluating usability and user value of ERs are insufficient. In this study, an evaluation tool was developed and verified, and an evaluation of an ER-based rehabilitation training program was conducted using the evaluation tool. A total of 102 rehabilitation patients (51 using ER and 51 under the existing training program [ETP] without robots) and 38 therapists participated in this experiment. The ER used in this study was Exowalk (HR-02, HMH Co. Ltd, South Korea). As a result of the study, the usability of the ERs consists of trust and overall satisfaction. The patient group using the ER showed statistically higher trust and overall satisfaction scores than the patient group with the ETP. On the other hand, the user value of the ER includes self-direction and security. The self-direction score of the patients using the ER was significantly higher than that of the patients with the ETP, but the security score showed the opposite tendency. This study is unique in that measures were developed to evaluate the usability and user value of the gait rehabilitation ER, especially for the Exowalk, verified by various user groups, and statistically analyzed to be used as a reference for the design of usability and user value in other gait rehabilitation ERs.

Robust model reference adaptive control for five-link robotic exoskeleton

In this paper, H∞-based model reference adaptive control (MRAC) for controlling five-link lower limb robotic exoskeleton is developed during the single support phase with uncertainties and unknown external disturbances. Unlike classical MRAC, the presented scheme using H∞ control and MRAC with adaptive error gain is proposed under uncertain parameter vector and external disturbance. For robustness, H∞ method is applied to minimise the impact of perturbation. Moreover, robust asymptotic stability of the overall system is investigated by Barbalat Lemma selecting a suitable Lyapunov functional candidate. Finally, the efficacy of developed scheme is validated by a relevant example.

Разработка экзоскелета нижних конечностей человека для медицинской реабилитации

The article deals with the solution of the problem of optimizing the characteristics of controlled motion of human lower limb exoskeleton robot for improving medical rehabilitation. The aim of the work is to develop a rehabilitation device capable of providing controlled motion in two planes, as well as maintaining balance without loss of mobility. The design and control system of a rehabilitation trainer designed for performing mechanotherapy of the lower limbs of patients with locomotive disorders are proposed and characterized. The developed system has a number of significant differences from analogues and can be recommended for experimental research on patients with impaired locomotive functions

An IMUs-Based Extended Kalman Filter to Estimate Gait Lower Limb Sagittal Kinematics for the Control of Wearable Robotic Devices

Inertial sensors have gained relevance as wearable sensors to acquire the kinematics of human limbs through fusion sensor algorithms and biomechanical models. However, there are some limitations to the use of Inertial Measurement Units in the control of wearable robotic devices: 1) Some approaches use magnetometer readings to estimate the orientation of the sensor, and, as a result, they are prone to errors due to electromagnetic interferences; 2) Biomechanical model-based approaches require complex and time-consuming calibration procedures. In order to address these issues, this paper proposes an Extended Kalman Filter to estimate sagittal lower limb kinematics during gait, based on gyroscopes and accelerometers and without requiring any calibration or sensor alignment process. As magnetometer measurements are not involved, this method is not affected by electromagnetic disturbances. Our approach calculates the knee rotation axis in real-time, and it estimates hip and ankle sagittal axes considering that the movements in that plane occur around parallel axes. We carried out an experimental validation with eight healthy subjects walking on a treadmill at different velocities. We obtained waveform RMS errors about 3.8°, 3.6°, and 4.8° for hip, knee, and ankle in the sagittal plane. We also assessed the performance of this method as a tool for controlling lower-limb robotic exoskeletons by detecting gait events or estimating the phase and frequency of the gait in real-time through an Adaptive Frequency Oscillator. The average RMS delay in the detection of gait events was lower than 60 ms, and the RMSE in the estimation of the gait phase was about 3% of the gait cycle. We conclude that the described method could be used as a controller for wearable robotic devices.

Applying Hip Stiffness With an Exoskeleton to Compensate Gait Kinematics.

Neurological disorders and aging induce impaired gait kinematics. Despite recent advances, effective methods using lower-limb exoskeleton robots to restore gait kinematics are as yet limited. In this study, applying virtual stiffness using a hip exoskeleton was investigated as a possible method to guide users to change their gait kinematics. With a view to applications in locomotor rehabilitation, either to provide assistance or promote recovery, this study assessed whether imposed stiffness induced changes in the gait pattern during walking; and whether any changes persisted upon removal of the intervention, which would indicate changes in central neuro-motor control. Both positive and negative stiffness induced immediate and persistent changes of gait kinematics. However, the results showed little behavioral evidence of persistent changes in neuro-motor control, not even short-lived aftereffects. In addition, stride duration was little affected, suggesting that at least two dissociable layers exist in the neuro-motor control of human walking. The lack of neuro-motor adaptation suggests that, within broad limits, the central nervous system is surprisingly indifferent to the details of lower limb kinematics. The lack of neuro-motor adaptation also suggests that alternative methods may be required to implement a therapeutic technology to promote recovery. However, the immediate, significant, and reproducible changes in kinematics suggest that applying hip stiffness with an exoskeleton may be an effective assistive technology for compensation.

Robot dynamic trajectory tracking control algorithm based on steady-state closed-loop learning

In order to improve the stability control ability of flexible lower limb exoskeleton robot, a dynamic trajectory tracking control algorithm of flexible lower limb exoskeleton robot based on steady-state closed-loop learning is proposed. Gyroscope and rangefinder are used as information sensors of flexible lower limb exoskeleton robot to collect position information of flexible lower limb exoskeleton robot, fuse collected positioning information of flexible lower limb exoskeleton robot, fuse physical information and measure parameters of flexible lower limb exoskeleton robot by using dynamic information measurement method, and obtain mapping feature set of Cartesian space according to trajectory components of flexible lower limb exoskeleton robot. The pose information of the flexible lower limb exoskeleton robot is obtained through the forward kinematics, and the information is enhanced according to the spatial position information. The steady-state closed-loop learning method is adopted to realize the adaptive learning of the robot dynamic trajectory tracking control. The simulation results show that this method is adaptive to the dynamic trajectory tracking control of the robot, and the positioning control ability of the robot is strong.

Human Motion Capture System Based 3D Reconstruction on Rehabilitation Assistance Stability of Lower Limb Exoskeleton Robot Climbing Upstairs Posture

Stair-climbing posture is a common motion posture of lower extremity exoskeleton robots. In the process of climbing stairs, with the change of the gravity center of system, it has a direct impact on the dynamic stability characteristics of human-machine system. Based on this, a complete diagram of the stair-climbing posture can be set up, which can be divided into the starting stage, the intermediate stage and the ending stage. The foot trajectory, COG trajectory and ZMP trajectory of human-machine system in the intermediate stage of stair-climbing posture can be analyzed by drawing stair-climbing motion diagram of six-step. Based on the inverted pendulum theory, a virtual inverted pendulum model with variable height (VCHIPM) is proposed to analyze the influence of the change of the gravity center on the dynamic stability of the human-machine system in the upstairs posture. The test results show us that COG position can be changed by adjusting the value of a, so as to adjust the position of ZMP, reduce the error of ZMP and ensure the stability of human-machine system.