Lower Limb Rehabilitation Robot Research Articles

A triple-step controller with linear active disturbance rejection control for a lower limb rehabilitation robot.

Lower limb rehabilitation robots (LLRRs) have shown promising potential in assisting hemiplegic patients to recover their motor function. During LLRR-aided rehabilitation, the dynamic uncertainties due to human-robot coupling, model uncertainties, and external disturbances, make it challenging to achieve high accuracy and robustness in trajectory tracking. In this study, we design a triple-step controller with linear active disturbance rejection control (TSC-LADRC) for a LLRR, including the steady-state control, feedforward control, and feedback control. The steady-state control and feedforward control are developed to compensate for the gravity and incorporate the reference dynamics information, respectively. Based on the linear active disturbance rejection control, the feedback control is designed to enhance the control performance under dynamic uncertainties. Numerical simulations and experiments are conducted to validate the effectiveness of TSC-LADRC. The results of simulations illustrate that the tracking errors under TSC-LADRC are obviously smaller than those under the triple-step controller without LADRC (TSC), especially with the change of external loads. Moreover, the experiment results of six healthy subjects reveal that the proposed method achieves higher accuracy and lower energy consumption than TSC. Therefore, TSC-LADRC has the potential to assist hemiplegic patients in rehabilitation training.

Inverse Dynamics Modeling and Analysis of Healthy Human Data for Lower Limb Rehabilitation Robots

Bio-controllers inspired by the characteristics of the human lower limb play an important role in the study of lower limb rehabilitation robots (LLRRs). However, the inverse dynamics modeling of robots for human lower limb rehabilitation remains a challenging issue due to the non-linear and strong coupling characteristics of the bio-controller. To further improve the inverse dynamics model’s accuracy, this paper proposes the use of a non-parametric modeling approach in order to learn it. In detail, the main idea is to use the motion data of the main joints of the lower limbs of healthy people as an input and the corresponding joint moments as an output, which are learned through the training of a neural network. To ensure that the learned model can be used on LLRRs, all data collected in this paper are real data from human lower limbs. In addition, since the type of data collected is time series, this paper proposes the use of the long short-term memory (LSTM) and gated recurrent unit (GRU) networks to learn the inverse dynamics model of the robot-like human lower limb and to compare the learning effects of the two networks. The evaluation metric for both network models is the root mean square error (RMSE). The experimental results show that both networks have sound learning effects, and that the GRU network has a more significant learning ability than the LSTM network.

SEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training.

The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based gain-tuned compliance control (EGCC) strategy for the lower limb rehabilitation robot. First, the mapping function relationship between the normalized surface electromyography (sEMG) signal and the gain parameter was established and an overall EGCC strategy proposed. Next, the EGCC strategy without sEMG information was simulated and analyzed. The effects of the impedance control parameters on the position correction amount were studied, and the change rules of the robot end trajectory, man-machine contact force, and position correction amount analyzed in different training modes. Then, the sEMG signal acquisition and feature analysis of target muscle groups under different training modes were carried out. Finally, based on the lower limb rehabilitation robot control system, the influence of normalized sEMG threshold on the robot end trajectory and gain parameters under different training modes was experimentally studied. The simulation and experimental results show that the adoption of the EGCC strategy can significantly enhance the compliance of the robot end-effector by detecting the sEMG signal and improve the safety of the robot in different training modes, indicating the EGCC strategy has good application prospects in the rehabilitation robot field.

Human-Robot Cooperative Strength Training Based on Robust Admittance Control Strategy.

A stroke is a common disease that can easily lead to lower limb motor dysfunction in the elderly. Stroke survivors can effectively train muscle strength through leg flexion and extension training. However, available lower limb rehabilitation robots ignore the knee soft tissue protection of the elderly in training. This paper proposes a human–robot cooperative lower limb active strength training based on a robust admittance control strategy. The stiffness change law of the admittance model is designed based on the biomechanics of knee joints, and it can guide the user to make force correctly and reduce the stress on the joint soft tissue. The controller will adjust the model stiffness in real-time according to the knee joint angle and then indirectly control the exertion force of users. This control strategy not only can avoid excessive compressive force on the joint soft tissue but also can enhance the stimulation of quadriceps femoris muscles. Moreover, a dual input robust control is proposed to improve the tracking performance under the disturbance caused by model uncertainty, interaction force and external noise. Experiments about the controller performance and the training feasibility were conducted with eight stroke survivors. Results show that the designed controller can effectively influence the interaction force; it can reduce the possibility of joint soft tissue injury. The robot also has a good tracking performance under disturbances. This control strategy also can enhance the stimulation of quadriceps femoris muscles, which is proved by measuring the muscle electrical signal and interaction force. Human–robot cooperative strength training is a feasible method for training lower limb muscles with the knee soft tissue protection mechanism.

A Lower Limb Rehabilitation Robot with Rigid-Flexible Characteristics and Multi-Mode Exercises

Lower limb rehabilitation robot (LLRR) can effectively help restore the lower limb’s motor function of patients with hemiplegia caused by stroke through a large number of targeted and repetitive rehabilitation training. To improve the safety and comfort of robot-assisted lower limb rehabilitation, we developed an LLRR with rigid-flexible characteristics; the design of passive joints is used to improve human-machine compatibility; the design of flexible unit makes the mechanism have certain rigid-flexible characteristics. Three different rehabilitation training methods have been developed to adapt to the patients at different stages of rehabilitation, namely, passive exercise, active exercise and resistance exercise, respectively. Experiments with healthy subjects have been conducted to verify the effectiveness of the development of the different training modes of the LLRR, showing good compatibility of the mechanism and good trajectory tracking performance of the developed training methods.

A Joint Acceleration Estimation Method Based on a High-Order Disturbance Observer

Joint acceleration feedback is widely used in the design of controllers and observers since joint accelerations reflect the joint dynamics of robots, especially in physical human-robot interaction. However, joint acceleration acquisition is a technical difficulty for robots. The dynamics-based methods can achieve joint acceleration estimation using only a nominal model. Still, the performance of these methods is limited by the fast time-varying disturbances in the system. This letter proposes a joint acceleration estimation method based on a high-order disturbance observer. This method can observe and compensate for fast time-varying lumped disturbances in the observer while maintaining joint acceleration estimation performance at low frequencies. The finite-time stability of the proposed estimation method is proved using the Lyapunov theory. Simulations and experiments with a lower limb rehabilitation robot are implemented to verify the performance of the proposed method.

Research on Movement Intentions of Human's Left and Right Legs Based on Electro-Encephalogram Signals

Abstract Active rehabilitation can use electro-encephalogram (EEG) signals to identify the patient's left and right leg movement intentions for rehabilitation training, which helps stroke patients recover better and faster. However, the lower limb rehabilitation robot based on EEG has low recognition accuracy so far. A classification method based on EEG signals of motor imagery is proposed to enable patients to accurately control their left and right legs. Firstly, aiming at the unstable characteristics of EEG signals, an experimental protocol of motor imagery was constructed based on multijoint trajectory planning motion of left and right legs. The signals with time-frequency analysis and event-related desynchrony/synchronization (ERD/S) analysis have proved the reliability and validity of the collected EEG signals. Then, the EEG signals generated by the protocol were preprocessed and common space pattern (CSP) was used to extract their features. Support vector machine (SVM) and linear discriminant analysis (LDA) are adapted and their accuracy of classification results are compared. Finally, on the basis of the proposed classifier with excellent performance, the classifier is used in the active control strategy of the lower limb rehabilitation robot, and the average accuracy of the left leg and right leg controlled by two healthy volunteers was 95.7%, 97.3%, 94.9%, and 94.6%, respectively, by using the ten-fold cross test. This research provides a good theoretical basis for the realization and application of brain-computer interfaces in rehabilitation training.

Mechanism Design and Performance Analysis of a Sitting/Lying Lower Limb Rehabilitation Robot

To meet the various need of stroke patients’ rehabilitation training and carry out complex task training in real scenes, the structure of a lower limb rehabilitation robot with movements in the sagittal plane and coronal plane is usually complicated. A new sitting/lying lower limb rehabilitation robot (LOBO) with a simple mechanism form is proposed, which is designed based on a 2-PRR parallel mechanism. First, the kinematics, singularity, and condition number of the 2-PRR parallel mechanism are analyzed, which provides the basis for mechanism parameter design. Then, through the proportional–derivative control principle, real-time tracking of LOBO’s designed trajectory is realized. Finally, the length parameters of volunteers’ lower limbs are collected, and experimental verification is conducted in LOBO’s passive training mode. The experimental results show the feasibility of LOBO’s movement in the human sagittal and coronal planes. LOBO will help human lower limbs realize the synchronous continuous rehabilitation training of hip, knee, and ankle joints spatially, which could drive the rehabilitation movement of patients’ lower limbs in the sagittal plane and coronal plane in future clinical research. LOBO can also be applied to muscle strength training for the elderly to combat the effects of aging.

Virtual Reality Game for Physical and Emotional Rehabilitation of Landmine Victims.

Landmine victims require an engaging and immersive rehabilitation process to maintain motivation and therapeutic adherence, such as virtual reality games. This paper proposes a virtual reality exercise game called Exogames, which works with Nukawa, a lower limb rehabilitation robot (LLRR). Together, they constitute the general Kina system. The design and development process of Exogames is reported, as well as the evaluation of its potential for physical and emotional rehabilitation. In an initial survey designed ad-hoc, 13 health professionals evaluated compliance with various requirements. They agreed that Exogames would help the user focus on rehabilitation by providing motivation; 92.3% said that the user will feel safe in the virtual world, 66.7% of them agreed or totally agreed that it presents characteristics that may enhance the physical rehabilitation of lower limbs for amputees, 83.3% stated that it would promote the welfare of landmine victims, and 76.9% responded that the graphical interface and data report are useful for real-time assessment, and would be helpful for four interventional areas in all rehabilitation stages. In a second evaluation, using standardized surveys, five physical therapists and one lower limb amputee tried the Kina system as users. They filled out the System Usability Scale (SUS), the Physical Activity Enjoyment Scale (PACES), and the Game Experience Questionnaire (GEQ). The usability of the Kina system overall score was 69 (66, 79) out of 100, suggesting an acceptable though improvable usability. The overall PACES score of 110 (108, 112) out of 126 suggests that users enjoyed the game well. Finally, users indicated a positive effect with a good sense of immersion and smooth of gameplay during the tests, as indicated by the GEQ results. In summary, the evaluations showed that Exogames has the potential to be used as a virtual reality game for the physical and emotional rehabilitation of landmine victims.

Motion Rehabilitation Robot Control Based on Human Posture Information

In order to meet the needs of postoperative rehabilitation training of lower limbs, a motion rehabilitation robot control system based on human posture information is proposed in this paper. The functions of active/passive training mode control, movement posture and EMG signal acquisition, WiFi communication, safety protection, etc. of the lower limb rehabilitation robot are realized. The recognition and analysis of the training process are realized by using random forest machine learning algorithm and linear regression algorithm. The experimental results show that in the first row of the confusion matrix of the random forest algorithm, 7316 data are correctly identified as speed a and only one data is incorrectly identified as speed B , which is superior to other algorithms. In conclusion, the developed control and monitoring system of lower limb rehabilitation robot can be portable controlled by Android and can realize intelligent analysis of the training process through the monitoring signals in the training process. At the same time, the random forest algorithm has more advantages than the linear regression algorithm in motion recognition, which is of positive significance to the automatic monitoring and intelligent control of the training process.

Adaptive Robust Variable Impedance Controller for Lower Limb Rehabilitation Robot with Augmented Type-2 Fuzzy System

Adaptive Robust Variable Impedance Controller for Lower Limb Rehabilitation Robot with Augmented Type-2 Fuzzy System

Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

Lower limb rehabilitation robot (LLRR) users, to successfully conduct isotonic exercises, require real-time feedback on the torque they exert on the robot to meet the goal of the treatment. Still, direct torque measuring is expensive, and indirect encoder-based estimation strategies, such as inverse dynamics (ID) and Nonlinear Disturbance Observers (NDO), are sensitive to Body Segment Inertial Parameters (BSIPs) uncertainties. We envision a way to minimize such parametric uncertainties. This paper proposes two human–robot interaction torque estimation methods: the Identified ID-based method (IID) and the Identified NDO-based method (INDO). Evaluating in simulation the proposal to apply, in each rehabilitation session, a sequential two-phase method: (1) An initial calibration phase will use an online parameter estimation to reduce sensitivity to BSIPs uncertainties. (2) The torque estimation phase uses the estimated parameters to obtain a better result. We conducted simulations under signal-to-noise ratio (SNR) = 40 dB and 20% BSIPs uncertainties. In addition, we compared the effectiveness with two of the best methods reported in the literature via simulation. Both proposed methods obtained the best Coefficient of Correlation, Mean Absolute Error, and Root Mean Squared Error compared to the benchmarks. Moreover, the IID and INDO fulfilled more than 72.2% and 88.9% of the requirements, respectively. In contrast, both methods reported in the literature only accomplish 27.8% and 33.3% of the requirements when using simulations under noise and BSIPs uncertainties. Therefore, this paper extends two methods reported in the literature and copes with BSIPs uncertainties without using additional sensors.

Lower limb motion recognition based on surface electromyography signals and its experimental verification on a novel multi-posture lower limb rehabilitation robots☆

The construction of motion decoder based on surface Electromyography (sEMG) signals is an important part in the clinical trial of rehabilitation robot. Its performance directly determines the success of clinical trial. However, feature extraction is essential in motion decoder. A single feature such as time domain and frequency domain can achieve good classification results, but it is only suitable for a single posture. Hence, for the lying and sitting postures, different feature analysis and their combination are used in this study to improve the sEMG-based lower limb motion classification performance. Nine participants in the clinical trial performed four different movements respectively. Through feature extraction and pattern recognition of sEMG, the trained motion decoders were obtained. The control commands are sent to the robot through labels retrieval to drive the lower limbs for corresponding rehabilitation training. The effectiveness of the based on sEMG signals control method is verified through real-time experimental analysis.

Prevention and Detection Research of Intelligent Sports Rehabilitation under the Background of Artificial Intelligence.

Artificial intelligence can bring convenience to human life. In the field of sports rehabilitation, the application of artificial intelligence is becoming more and more in-depth. This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. In the inner loop, a sliding mode iterative learning controller is designed to suppress periodic disturbance and abnormal jitter and achieve stable tracking of the target trajectory. Finally, the control method is simulated and verified by matlab/simulink, and a statistical experiment is done on the patient's recovery. The experimental results show that the use of artificial intelligence technology can effectively increase the sensitivity of the control system and improve the recovery rate of patients. Compared with the traditional sports rehabilitation control system, the sensitivity is increased by 22.7%, and the patient recovery rate is increased by 10.4%, which is of great significance in the field of sports rehabilitation.

Design of a control framework for lower limb exoskeleton rehabilitation robot based on predictive assessment

BackgroundPatients suffering from lower limb dyskinesia, especially in early stages of rehabilitation, have weak residual muscle strength in affected limb and require passive training by the lower limb rehabilitation robot. Anatomy indicates that the biceps femoris short head muscle has a strong influence on knee motion at the swing phase of walking. We sought to explore how it would influence on gait cycle in optimization framework. However, the training trajectory of conventional rehabilitation robots performing passive training usually follows gait planning based on general human gait data, which cannot simultaneously ensure both effective rehabilitation of affected limbs with varying severity pathological gait and comfort of the wearer within a safe motion trajectory. MethodsTo elucidate the effects of weakness and contracture, we systematically introduced isolated defects into the musculoskeletal model and generated walking simulations to predict the gait adaptation due to these defects. An impedance control model of the rehabilitation robot is developed. Knee joint parameters optimized by predictive forward dynamics simulation are adopted as the expected values for the robot controller to achieve customized adjustment of the robot motion trajectory. FindingsSevere muscle contracture leads to severe knee flexion; severe muscle weakness induces a significant posterior tilt of the upper trunk, which hinders walking speed. InterpretationOur simulation results attempt to reveal pathological gait features, which may help to reproduce the simulation of pathological gait. Furthermore, the robot simulation results show that the robot system achieves a speedy tracking by setting a larger stiffness value. The model also allows the implementation of different levels of damping or elasticity effects.Trial registration: The method proposed in this paper is an initial basic study that did not reach clinical trials and therefore retains retrospectively registered.

Electrically Driven Lower Limb Exoskeleton Rehabilitation Robot Based on Anthropomorphic Design

To help people with impairment of lower extremity movement regain the ability to stand and walk, and to enhance limb function, this study proposes an anthropomorphic design of an electrically driven, lower-limb exoskeleton rehabilitation robot. The angular range of the robot’s motion was determined according to the characteristics of the targeted lower-limb joints; the robot was given an active–passive anthropomorphic design with 12 degrees of freedom. The multi-degree-of-freedom hip exoskeleton, bionic artificial knee exoskeleton and passive rigid-flexible coupling ankle exoskeleton can assist patients in rehabilitation exercises with better wear comfort and exercise flexibility. A kinetic model of the seven-rod lower-limb exoskeleton rehabilitation robot was built, and data analysis of the dynamically captured motion trajectory was conducted. These provided a theoretical basis for gait planning and the control system of the lower-limb exoskeleton rehabilitation robot. The results show that the lower-limb exoskeleton rehabilitation robot system possesses sound wearing comfort and movement flexibility, and the degree of freedom of movement of the exoskeleton robot matches well with that of human movement. The robot can thus provide effective assistance to patients’ standing and walking rehabilitation training.

Design of Adaptive PID Controller for Lower Limb Rehabilitation Robot Based on Particle Swarm Optimization Algorithm.

The proportional-integral-derivative (PID) is still the most common controller and stabilizer used in industry due to its simplicity and ease of implementation. However, in most of the real applications, the controlled system has parameters that slowly vary or are uncertain. Thus, PID gains must be adapted to cope with such changes.
 In this research, an Adaptive Proportional-Integral-derivative controller (APID) is proposed to control the 2-DOF lower limb rehabilitation robot system. The parameters gains of the proposed controller are optimized using the Particle Swarm Optimization algorithm (PSO). The simulation results show no overshoot and zero steady-state error, but large settling time (ts=3.654 sec. for link1 and ts=2.844 sec. for link2) for linear path, and the actual path tracks the desired path with a large error for the nonlinear path. The results illustrate that the robot's performance is inefficient for linear and nonlinear paths when using the APID controller to control the lower limb rehabilitation robot. Therefore, the controller needs to modify for controlling the robot efficiently.

Gait Measurement System Using Laser Range Sensor Based on Lower-limb Rehabilitation Robot

Spatiotemporal gait parameters provide important information for the rehabilitation of patients with gait dysfunction. These parameters are often obtained by complex systems such as optical motioncapture system and pressure plates. However, these systems cannot be deployed at the lower-limb rehabilitation robot easily because of high costs, large area occupation and wearable requirements. We present a gait measurement system with a Light Detection And Ranging(LIDAR) laser sensor based on the lower-limb rehabilitation robot. Firstly, to calculate gait parameters, the data are aggregated into left and right legs by the clustering algorithm and the legs contour is fitted with two circles respectively according to the least square method. Then, the spatiotemporal gait parameters are defined based on the time and position of initial contact(IC) and toe off(TO). Finally, to verify the validity of the proposed system, we compared the results of the proposed system with a 3D motion capture system based on a lower-limb rehabilitation robot. Experimental results showed that the gait detection system can measure the parameters within a small range of error that testified the validation of the proposed system. This system proved to be a valid and reliable method for the measurement of gait parameters.

Research of Wire-driven Parallel Lower Limb Rehabilitation Robot

To realize the lower limb rehabilitation training with multiple-degree-of-freedom, a wire-driven parallel lower limb rehabilitation robot with six wires is designed. The static and dynamic models of the parallel robot are established, and the feasible wrench workspace of the robot is calculated based on the motion law of lower limb rehabilitation in lying posture. And the motion trajectory of a wire-driven robot is planned according to the human lower limb D-H model. The variation laws of wire length and speed are calculated by MATLAB simulation, which verifies the feasibility of the wire-driven parallel lower limb rehabilitation robot.

Development and Evaluation of a Wearable Lower Limb Rehabilitation Robot

Development and Evaluation of a Wearable Lower Limb Rehabilitation Robot