Rehabilitation Training Robot Research Articles

Design and research of eight-link rehabilitation training robot based on single degree of freedomitle

Design and research of eight-link rehabilitation training robot based on single degree of freedomitle

Adaptive Gait Training of a Lower Limb Rehabilitation Robot Based on Human-Robot Interaction Force Measurement.

The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients, ignoring their residual muscle strength. To enhance patient participation and safety in training, this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human-robot interaction force measurement. Firstly, a novel lower limb rehabilitation robot system with several active and passive driven joints is developed, and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human-robot interaction forces. Secondly, a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state. Thereafter, based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors, an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer's movement intention. Finally, interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system. The proposed rehabilitation training robot system with adaptive gaits offers great potential for future high-quality rehabilitation training, e.g., improving participation and safety.

Patient's Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots.

The implementation of a progressive rehabilitation training model to promote patients' motivation efforts can greatly restore damaged central nervous system function in patients. Patients' active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient's healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient's movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient's healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements.

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper.

In recent years, rehabilitation robots have been developed and used in rehabilitation training for patients with hemiplegia. In this paper, a rehabilitation training robot with variable damping is designed to train patients with hemiplegia to recover upper limb function. Firstly, a magnetorheological joint damper (MR joint damper) is designed for the rehabilitation training robot, and its structural design and dynamic model are tested theoretically and experimentally. Secondly, the rehabilitation robot is simplified into a spring-damping system, and the rehabilitation training controller for human movement is designed. The rehabilitation robot dynamically adjusts the excitation current according to the feedback speed and human-machine interaction torque, so that the rehabilitation robot always outputs a stable torque. The magnetorheological joint damper acts as a clutch to transmit torque safely and stably to the robot joint. Finally, the upper limb rehabilitation device is tested. The expected torque is set to 20 N, and the average value of the output expected torque during operation is 20.02 N, and the standard deviation is 0.635 N. The output torque has good stability. A fast (0.5 s) response can be achieved in response to a sudden motor speed change, and the average expected output torque is 20.38 N and the standard deviation is 0.645 N, which can still maintain the stability of the output torque.

Hand Function Rehabilitation Training Robot Based on Ycbcr and CNN

Hand Function Rehabilitation Training Robot Based on Ycbcr and CNN

Design and Optimization of Multifunctional Human Motion Rehabilitation Training Robot EEGO

A multifunctional human motion rehabilitation training robot named EEGO (electric easy go) that could achieve four functions through structural transformation was designed. The four functions achieved by four working modes: the Supporting Posture Mode (SM), the Grasping Posture Mode (GM), the Riding Posture Mode (RM), and the Pet Mode (PM), which are suitable for patients in the middle and late stages of rehabilitation. The size of the equipment under different functions is determined by the height of different postures of the human. During the design process, the equipment was lightweight using size optimization methods, resulting in a 47.3% reduction in mass compared to the original design. Based on the Zero Moment Point (ZMP) stability principle, the stability mechanism of the robot was verified under the three different functions. According to the wanted function of the equipment, the control system of the equipment was designed. Finally, a prototype was prepared based on the analysis and design results for experimental verification, which can effectively assist patients in motion rehabilitation training such as gait, walking, and other movements.

Control strategy based on improved fuzzy algorithm for energy control of wrist rehabilitation robot

With the complex changes in social and demographic structure, the problem of aging is becoming increasingly serious, and the demand for equipment in the field of medical rehabilitation is also increasing. Given the above situation, various countries have proposed using rehabilitation robots to assist patients in treatment, and have achieved certain results. The rehabilitation robot helps the injured area gradually recover autonomous movement through repeated auxiliary actions, thereby achieving the goal of physical rehabilitation training. However, most robot configurations face insufficient energy and power, as well as bulky and inflexible equipment. Based on the above issues, this article adopts an improved fuzzy algorithm control strategy to optimize the energy storage device and applies the energy system to the wrist rehabilitation training robot device. Firstly, a fuzzy adaptive algorithm is added to the traditional energy controller to combine system control with operational status, achieving precise control and dynamic adjustment of energy allocation. The research has added filter optimization design, which improves the accuracy and effectiveness of fuzzy algorithms. Finally, this article establishes an upper limb motion model of the rehabilitation robot and configures relevant hardware systems. At the same time, this article also uses manufacturing materials that meet functional requirements to construct auxiliary equipment. The results indicate that the improved fuzzy algorithm can improve the auxiliary effect in energy control of wrist rehabilitation robots. It has obvious advantages in the process of wrist rehabilitation training.

Glenohumeral joint trajectory tracking for improving the shoulder compliance of the upper limb rehabilitation robot.

Exoskeletons have become an important tool to help patients with upper extremity motor dysfunction in rehabilitation training and life assistance. In the study of the upper limb exoskeleton, the human glenohumeral joint will produce accompanying movement during the movement of the shoulder joint. This phenomenon causes a positional deviation between the shoulder joint and the exoskeleton, which affects the accuracy of exoskeleton-assisted human movement and the wearing comfort. Spend. Taking the coronal adduction and abduction of the shoulder joint as the research object, the shoulder joint angle and glenohumeral joint bony motion trajectory were fitted by bi-level X-rays, and then the Ultium Motion motion capture system was used to collect the characteristic motion of the shoulder joint surface and establish a model. A back-propagation neural network with shoulder joint motion and shoulder width as input and the coronal position of the glenohumeral joint as output, finally applied the model to the Nimbot exoskeleton upper limb rehabilitation training robot to verify the effectiveness of the algorithm. Real-time prediction of the glenohumeral joint motion trajectory was achieved, and the human-machine coupling compliance during the wearing of the upper limb exoskeleton was improved.

Research on a New Rehabilitation Robot for Balance Disorders.

The treatment of patients with balance disorders is an urgent problem to be solved by the medical community. The causes of balance disorders are diverse. An aging population, traffic accidents, stroke, genetic diseases and so on are all possible factors. It has brought great pain and inconvenience to patients and their families. At present, there are two main types of assisted rehabilitation training robots for patients with balance disorders: exoskeleton robots and end robots. The exoskeleton robot is generally installed on the outside of the patient's body to follow their movement, which can support the weight of the body and provide power support to help the patient train and recover lower limb ability. The use of end robots is usually to secure the patient's foot to the motion platform and control the pedal to drive the lower limbs to conduct gait training. Such passive training is more suitable for patients with severe disorders. The patient has low awareness of active participation. This paper focuses on research on end rehabilitation training robots for balance disorders. In this paper, a robotic system for rehabilitation training of patients with balance disorders is invented. The robot body is a 9 degree of freedom (DOF) redundant series-parallel hybrid motion platform. Two sets of motion platforms with symmetrical mirror images are used together to simulate different motion modes of the human body and drive the human body to move. Each set of motion platforms is composed of a 6-DOF vestibular parallel device and a 3-DOF proprioception parallel device. It has the advantages of DOF decoupling and fast response, proposing a new structural form for the design of proprioceptive and vestibular simulation platforms. The robot's functional level can be divided into a vestibular sense module and a proprioception module according to the structure. The two modules can work independently to achieve different functions or work together to achieve complex motion and multisensory fusion. This robot is a redundant mechanism device with 9 DOFs. Through a reasonable distribution of DOF and motion, the robot's working space can be increased, and the robot's flexibility and motion performance can be improved. In this paper, a trajectory tracking control algorithm for vestibular and proprioceptive simulation is proposed, which can provide unlimited body sense training for patients within the robot's limited motion range.

Gait rehabilitation training robot: A motion-intention recognition approach with safety and convenience

Motion-intention recognition is a vital prerequisite in active training when employing a gait rehabilitation training robot. To accurately recognize the motion intention of the elderly and the people with inconveniences in the human–robot interaction process, a novel approach of motion intention recognition with safety, accuracy, and convenience, including directional intention recognition (DIR) and speed intention recognition (SIR) is proposed in this paper. Firstly, the structures of the gait rehabilitation training robot and its motion-intention recognition system are illustrated. To ensure that the user walks in any desired direction safely and naturally, an improved distance-type fuzzy reasoning algorithm combined with a shake-intent filter and second-order optimization algorithm is proposed. It effectively eliminates the control error caused by body shaking and usage habits in human–robot interaction. Furthermore, by extracting from the pressure sensor data, a novel algorithm, taking advantage of the Gaussian probability density function (PDF)’s characteristics, is proposed for SIR, which does not increase the system complexity. Finally, a multi-directional fuzzy reasoning experiment and a human–robot interaction experiment are implemented. The results show that the algorithm can accurately recognize the motion direction intention and motion speed intention of people with weak motion capability, which also improves the safety and convenience of the interaction approach. The proposed method can be integrated into a walker with similar structures, and the whole system can be applied in hospitals, families, and other places for assisting the elderly and the disabled.

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.

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.

Study on the design and control method of a wire-driven waist rehabilitation training parallel robot

AbstractWith the increasing demand for rehabilitation and the lack of professional rehabilitation personnel, robot-assisted rehabilitation technology plays an increasingly important role in neurological rehabilitation. In order to recover the exercise ability of patients with waist injury, a new type of wire-driven waist rehabilitation training parallel robot (WWRTPR) is designed. According to the motion trajectory planning of waist rehabilitation training, two coordinate systems are established: moving coordinate system and static coordinate system. The inverse kinematics modeling analysis is carried out, and the dynamic model of the robot is established by using Newton–Euler method. An intelligent control method of force/position hybrid control based on radial basis function neural network is proposed. The stability of the closed-loop system is analyzed, and the results show that WWRTPR tends to be stable. The simulation analysis of rehabilitation training on WWRTPR is carried out, and the simulation results show that the proposed intelligent control method can effectively control the robot system, which provides a reference for the development of a flexible intelligent rehabilitation training 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.

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.

Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems.

Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of the level of motor recovery through the measurement of force and movement patterns. However, in actual robotic rehabilitation training, emergency stops occur frequently to prevent injury to patients. However, frequent stopping is a waste of time and resources of both therapists and patients. Therefore, early detection of emergency stops in real-time is essential to take appropriate actions. In this paper, we propose a novel deep-learning-based technique for detecting emergency stops as early as possible. First, a bidirectional long short-term memory prediction model was trained using only the normal joint data collected from a real robotic training system. Next, a real-time threshold-based algorithm was developed with cumulative error. The experimental results revealed a precision of 0.94, recall of 0.93, and F1 score of 0.93. Additionally, it was observed that the prediction model was robust for variations in measurement noise.

Review of Research on Knee-postoperative Rehabilitation Training Robot

Review of Research on Knee-postoperative Rehabilitation Training Robot

Appraisement and Analysis of Dynamical Stability of Under-Constrained Cable-Driven Lower-Limb Rehabilitation Training Robot

SUMMARYThe dynamical stability of the cable-driven lower-limb rehabilitation training robot (CLLRTR) is a crucial question. Based on the established dynamics model of CLLRTR, the solution to the wrench closure of the under-constrained system is presented. Secondly, the stability index of CLLRTR is proposed by the Krasovski method. Finally, in order to analyze the stability distribution of CLLRTR in the workspace, the stability evaluation index in the workspace is calculated using the eigenvalue decomposition method. The stability distribution laws of CLLRTR are further verified by the experimental study. The results provide references for studying trajectory planning and anti-pendulum control of CLLRTR.

Control of a New Cycling Rehabilitation Robot Based on Fuzzy PID

For completing the standardized rehabilitation training of the affected limb based on the lower limb rehabilitation training robot system, it is designed that a lower limb rehabilitation robot control system based on fuzzy PID control method to realize stable and accurate control of the rehabilitation training process. First of all, this paper analyzes the ranges of motion of human hip and knees, and a single degree of freedom lower limb rehabilitation training robot system based on the belt transmission principle is designed. Then the fuzzy PID control is analyzed. And the Matlab is used to simulate the passive rehabilitation mode of the lower limb rehabilitation training robot system under different control strategies. The designed rehabilitation training test shows that the control system of the lower limb rehabilitation robot based on the fuzzy PID control method has a small speed overshoot, high control accuracy and smoother operation.