Lower Limb Rehabilitation Training Research Articles

Research and experiment on active training of lower limb based on five-bar mechanism of man-machine integration system

AbstractIn view of the fact that the current research on active and passive rehabilitation training of lower limbs is mainly based on the analysis of exoskeleton prototype and the lack of analysis of the actual movement law of limbs, the human-machine coupling dynamic characteristics for active rehabilitation training of lower limbs are studied. In this paper, the forward and inverse kinematics are solved on the basis of innovatively integrating the lower limb and rehabilitation prototype into a human-machine integration system and equivalent to a five-bar mechanism. According to the constraint relationship of hip joint, knee joint and ankle joint, the Lagrange dynamic equation and simulation model of five-bar mechanism under the constraint of human physiological joint motion are constructed, and the simulation problem of closed-loop five-bar mechanism is solved. The joint angle experimental system was built to carry out rehabilitation training experiments to analyze the relationship between lower limb error and height, weight and BMI, and then, a personalized training planning method suitable for people with different lower limb sizes was proposed. The reliability of the method is proved by experiments. Therefore, we can obtain the law of limb movement on the basis of traditional rehabilitation training, appropriately reduce the training speed or reduce the man-machine position distance and reduce the training speed or increase the man-machine distance to reduce the error to obtain the range of motion angle closer to the theory of hip joint and knee joint respectively, so as to achieve better rehabilitation.

Development of a Postoperative Rehabilitation Training Device for Patients with Upper and Lower Limb Muscular Atrophy

Objective: To develop a rehabilitation training device applicable to upper limb and lower limb muscle atrophy in postoperative patients, early postoperative rehabilitation intervention, reasonable rehabilitation training, and exercise to reduce muscle atrophy, and to help improve the ability of postoperative patients to move independently. Methods: The upper limb rehabilitation training device is comprised of a bed body and a sliding mechanism, with the sliding mechanism having a fixed component, a telescopic component, a snap-on component, and a power component. The sliding mechanism on the bed body moves back and forth, and the corresponding exercise can be performed in any position on the bed body. The lower limb rehabilitation training device comprises a base plate, a fixing box, and a seat, with the fixing box's structure comprising a fixation frame, a moving box, a pedal, a pressing block, an activity block, and other devices. The upper limb training device can ensure the safety of patients and accompanying personnel, while exercising multiple parts of the patient's muscle groups, and increasing the device's applicability; the lower limb training device can be positioned in time; and the height of the pedal can be adjusted to solve the problem of the training device's rebound force accidentally shocking the lower limbs and the inconvenience of the pedal height, which afflict patients. Conclusion: The use of rehabilitation training device training, leads to early recovery of postoperative patients' independent activities, improvement of patients' postoperative self-care ability, promotion of patients' postoperative rehabilitation, enhancement of patients' quality of life, and a decrease in postoperative complications.

A Decoding Method Using Riemannian Local Linear Feature Construction for a Lower-Limb Motor Imagery Brain–Computer Interface System

Recently, motor imagery brain–computer interfaces (BCIs) have been developed for use in motor function assistance and rehabilitation engineering. In particular, lower-limb motor imagery BCI systems are receiving increasing attention in the field of motor rehabilitation, because these systems could accurately and rapidly identify a patient’s lower-limb movement intention, which could improve the practicability of the motor rehabilitation. In this study, a novel lower-limb BCI system combining visual stimulation, auditory stimulation, functional electrical stimulation, and proprioceptive stimulation was designed to assist patients in lower-limb rehabilitation training. In addition, the Riemannian local linear feature construction (RLLFC) algorithm is proposed to improve the performance of decoding by using unsupervised basis learning and representation weight calculation in the motor imagery BCI system. Three in-house experiment were performed to demonstrate the effectiveness of the proposed system in comparison with other state-of-the-art methods. The experimental results indicate that the proposed system can learn low-dimensional features and correctly characterize the relationship between the testing trial and its k-nearest neighbors.

Deep Image Processing for Lower Limb Rehabilitation Training Action and Effect Recognition: GaitSet Algorithm and Full-Field Optical Flow Approaches

Deep Image Processing for Lower Limb Rehabilitation Training Action and Effect Recognition: GaitSet Algorithm and Full-Field Optical Flow Approaches

Study on the rehabilitation training effect of exoskeletal diseases based on the skeletal muscle geometry modeling method

Abstract Plantar pressure is the main mechanical parameter to understand human lower limb movement, which can fully show the health status of human spine, foot, leg and other regions.At present, researchers have carried out empirical analysis on the rehabilitation training process of lower limbs, but due to the defects of guidance methods and too vague evaluation indicators, it is impossible to conduct comprehensive monitoring and feedback on the rehabilitation process of patients. Therefore, on the basis of the modeling method based on the understanding of skeletal muscle set, the rest of the lower limbs dyskinesia patients rehabilitation training, puts forward the plantar pressure changes as the core of human lower limb rehabilitation training monitoring and feedback methods, at the same time using autoregressive model to construct the algorithm model of lower limb exoskeleton, updating method of the weighted coefficient of contrast research. The final results show that the least mean square error (LMS) algorithm can provide effective driving force for the rehabilitation training model of lower extremity exoskeleton disease, and feedback evaluation of the whole rehabilitation training process

Energy optimization of a wearable lower limb rehabilitation robot based on deep learning

The aging process of society requires more rehabilitation talents and medical resources to help the elderly and patients who need lower limb rehabilitation training. The wearable lower limb rehabilitation robot can help doctors to help patients complete the corresponding exercise training, breaking through the time and space constraints. The energy of such robots mainly comes from batteries. In addition to the corresponding battery management, the energy optimization of robots is its development trend. In this paper, the key gait recognition model of robot is constructed based on deep learning, and an energy optimization strategy is introduced to achieve energy saving by planning most trajectories. The experimental results show that the results of models with different layers tend to be stable. Comparing different gait pattern recognition errors, it is found that the error recognition rate between walking on the ground and going up and down stairs is higher, and the recognition error between squatting posture and standing posture is also higher. The model can effectively recognize and classify gait patterns. Compared with other algorithms, the error rate of the model is small, and the recognition and classification results are consistent with the actual motion features. Compared with other models, the wearable lower limb rehabilitation robot based on deep learning can effectively recognize different gait patterns, effectively reduce the error rate and achieve better classification results. At the same time, it can effectively realize the classification of gait stages under different motion states, while maintaining high accuracy.

Event-Triggered Sliding Mode Impulsive Control for Lower Limb Rehabilitation Exoskeleton Robot Gait Tracking

Lower limb rehabilitation exoskeleton robots (LLRERs) play an important role in lower limb rehabilitation training and assistance walking for patients with lower limb movement disorders. In order to reduce and eliminate adverse effects on the accuracy of human motion gait tracking during walking with an LLRER, which is caused by the gravity and friction, the periodic ground shock force, and the human–exoskeleton interaction force, this paper proposes a feedforward–feedback hybrid control strategy of sliding mode impulsive control with gravity and friction compensation, based on the event-triggered mechanism of Lyapunov function. Firstly, to realize high-precision gait tracking with bounded error, some constraints on controller parameters are deduced by analyzing the Lyapunov-based stability. Secondly, the Zeno behavior of impulsive event triggers is excluded by the analysis of three different cases of the triggering time sequence. Finally, the effectiveness of the proposed hybrid controller is verified by the numerical simulation of the LLRER human–exoskeleton integrated system based on a three-link simplified model. It shows that an event-triggered sliding mode impulsive control strategy with gravity and friction compensation can achieve complete gait tracking with bounded error and has excellent dynamic performance under the constraints.

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.

Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method.

Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb. In addition, upper triangle filter bank sparse spatial pattern (UTFB-SSP) is proposed to automatically select the optimal frequency sub-bands related to desynchronization rhythm during enhanced imaginary movement to improve the decoding performance. The effectiveness of the proposed MI-BCI system is demonstrated on an the in-house experimental dataset and the BCI competition IV IIa dataset. The experimental results show that the proposed system can effectively enhance the MI performance by inducing the α, β and γ rhythms in lower-limb movement imagery tasks.

DESIGN AND RESEARCH OF A LOWER LIMB CYCLING REHABILITATION ROBOT

A cycling rehabilitation robot is developed to assist in rehabilitation training for stroke patients. The robot can realize flexion movements of the patient’s hip, knee and ankle. First, based on physiological principles, the range for flexion motion angles of the lower limbs is presented. The mathematical modeling of the lower limb rehabilitation motion angles is established. The angular motion characteristics of the lower limb hip and knee joints are analyzed, and the relationship of the motion between the lower limb hip and knee joints is presented. To verify the correctness of the theoretical model, the structure of the lower limb rehabilitation training robot is designed. The experiments show that the rehabilitation robot can achieve flexion rehabilitation training of the lower limbs effectively, which proves the correctness of the proposed theoretical model. The results provide scientific guidance for patients using cycling rehabilitation devices.

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.

Model Analysis and Experimental Study of Lower Limb Rehabilitation Training Device Based on Gravity Balance

More hemiplegia patients tend to use equipment for rehabilitation training due to the lack of physical therapists and the low effect of manual training. Nowadays, lower limb rehabilitation training devices for patients in grade 2 of the Medical Research Council (MRC-2) scale are still scarce and have some issues of poor autonomy and cannot relieve the muscle weakness of patients. To address these problems, a prototype based on gravity balance was designed with the combination of springs and linkages to enable patients to passively experience the rehabilitation training in the state of balancing the gravity of lower limbs. The motion of the mechanism was analyzed to obtain the functional relation between the motor rotation angle and the joints’ angle. Based on the principle of constant potential energy, a gravity balance mathematical model of the device was established, analyzed, and simulated. Moreover, through the training experiment, the results show that when subjects in three different weights were trained under the rehabilitation device with and without gravity balance, the required torques of the motor and EMG signal strength of the knee and hip joints decreased by a degree of significance, which verified the effectiveness of the device’s gravity balancing characteristics for MRC-2 patients.

Intervention Effect of Rehabilitation Robotic Bed Under Machine Learning Combined With Intensive Motor Training on Stroke Patients With Hemiplegia.

It was aimed to discuss the effect of bed-type rehabilitation robots under machine learning combined with intensive motor training on the motor function of lower limbs of stroke patients with hemiplegia. A total of 80 patients with stroke hemiplegia were taken as the subjects, who all had a course of treatment for less than 6 months in the Rehabilitation Medicine Department of Ganzhou Hospital. These patients were divided into the experimental group (40 cases) and the control group (40 cases) by random number method. For patients in the control group, conventional intensive motor training was adopted, whereas the conventional intensive motor training combined with the bed-type rehabilitation robot under machine learning was applied for patients in the experimental group. Fugl-Meyer Assessment of Lower Extremity (FMA-LE), Rivermead Mobility Index (RMI), and Modified Barthel Index (MBI) were used to evaluate the motor function and mobility of patients. The human–machine collaboration experiment system was constructed, and the software and hardware of the control system were designed. Then, the experimental platform for lower limb rehabilitation training robots was built, and the rehabilitation training methods for stroke patients with hemiplegia were determined by completing the contact force experiment. The results showed that the prediction effect of back-propagation neural network (BPNN) was better than that of the radial basis neural network (RBNN). The bed-type rehabilitation robot under machine learning combined with intensive motor training could significantly improve the motor function and mobility of the lower limbs of stroke patients with hemiplegia.

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.

Design and Simulation Analysis of a Robot-Assisted Gait Trainer with the PBWS System.

In response to the ever-increasing demand of lower limb rehabilitation, this paper presents a novel robot-assisted gait trainer (RGT) to assist the elderly and the pediatric patients with neurological impairments in the lower limb rehabilitation training (LLRT). The RGT provides three active degrees of freedom (DoF) to both legs that are used to implement the gait cycle in such a way that the natural gait is not significantly affected. The robot consists of (i) the partial body weight support (PBWS) system to assist patients in sit-to-stand transfer via the precision linear rail system and (ii) the bipedal end-effector (BE) to control the motions of lower limbs via two mechanical arms. The robot stands out for multiple modes of training and optimized functional design to improve the quality of life for those patients. To analyze the performance of the RGT, the kinematic and static models are established in this paper. After that, the reachable workspace and motion trajectory are analyzed to cover the motion requirements and implement natural gait cycle. The preliminary results demonstrate the usability of the robot.

The Design of a Track Monitoring System for Sports Injury Rehabilitation Training.

To realize the remote monitoring design in the process of rehabilitation training for athletes after an injury using computer technology, using Visual Studio 2010 development platform, and using ASP as the development language, NET as the development framework, the injury rehabilitation of injured athletes for dynamic monitoring of information management system, and its functions, system architecture and other detailed design. This article identified six laboratory workers outside the sample as experimental subjects. The experimental subjects' blood oxygen flow, degree, and rate were measured in the normal condition, and the pulse wave was recorded. Then, the upper and lower limbs rehabilitation training robot developed by the laboratory was used for about 15 minutes of rehabilitation training with 3-gear difficulty. The results show that the data measured by the system are the same as the data measured by the Lu Yue brand finger clip type YX301 blood oxygen saturation detector and meet the design requirements. Conclusion. The monitoring accuracy of the system is high, the resistance signal waveform is basically consistent with the actual waveform, and the monitoring effect is good.

Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients

Carrying out the immediate rehabilitation interventional therapy will better improve the curative effect of rehabilitation therapy, after the condition of bedridden stroke patients becomes stable. A new lower limb rehabilitation training module, as a component of a synchronous rehabilitation robot for bedridden stroke patients’ upper and lower limbs, is proposed. It can electrically adjust the body shape of patients with a different weight and height. Firstly, the innovative mechanism design of the lower limb rehabilitation training module is studied. Then, the mechanism of the lower limb rehabilitation module is simplified and the geometric relationship of the human–machine linkage mechanism is deduced. Next, the trajectory planning and dynamic modeling of the human–machine linkage mechanism are carried out. Based on the analysis of the static moment safety protection of the human–machine linkage model, the motor driving force required in the rehabilitation process is calculated to achieve the purpose of rationalizing the rehabilitation movement of the patient’s lower limb. To reconstruct the patient’s motor functions, an active training control strategy based on the sandy soil model is proposed. Finally, the experimental platform of the proposed robot is constructed, and the preliminary physical experiment proves the feasibility of the lower limb rehabilitation component.

Design, Synthesis, and Experiment of Foot-Driven Lower Limb Rehabilitation Mechanisms

Abstract A foot-driven rehabilitation mechanism is suitable for home healthcare due to its advantages of simplicity, effectiveness, small size, and low price. However, most of the existing studies on lower limb rehabilitation movement only consider the trajectory of the ankle joint and ignore the influence of its posture angle, which makes it difficult to ensure the rotation requirements of the ankle joint and achieve a better rehabilitation effect. Aiming at the shortcomings of the current research, this article proposes a new single degree-of-freedom (DOF) configuration that uses a noncircular gear train to constrain the three revolute joints (3R) open-chain linkage and expounds its dimensional synthesis method. Then, a parameter optimization model of the mechanism is established, and the genetic algorithm is used to optimize the mechanism parameters. According to the eight groups of key poses and position points of the ankle joint and the toe, the different configurations of the rehabilitation mechanism are synthesized and compared, and it is concluded that the newly proposed 3R open-chain noncircular gear-linkage mechanism exhibits better performance. Finally, combined with the requirements of rehabilitation training, a lower limb rehabilitation training device is designed based on this new configuration, and a prototype is developed and tested. The test results show that the device can meet the requirements of the key position points and posture angles of the ankle joint and the toe and verify the correctness of the proposed dimensional synthesis and optimization methods.

Development of a New Lower Limb Rehabilitation Robot for Bedside Training

With the development of aging society, a great number of stroke patients are rapidly increasing. To meet the rehabilitation training needs of stroke patients, this paper proposed a simple, reliable and universally applicable lower limb rehabilitation training robot based on human lower limb movement characteristics. The robot is mainly composed of trolley, electric lifting column, screw adjustment component and left and right training mechanism. The kinematic model was developed by the D-H parameters method. Based on the rehabilitation mechanism, two modes of active training and passive training were designed. The robot can replace the work of therapists, significantly reduce the workload of therapists, and effectively alleviate the social situation of therapist shortage.

Design of sitting lower limb rehabilitation training robot

Based on the theory of sports rehabilitation medicine, aiming at the needs of early, middle and late rehabilitation training of patients with lower limb paralysis, a sitting lower limb rehabilitation robot is designed to train muscles with impaired motor function of the limbs. The Innovative design of the lower limb rehabilitation training robot consists two parts. They are the lower limb rehabilitation mechanism and the seat auxiliary mechanism, which can restore and reconstruct the impaired motor function of patients to the greatest extent.