Upper-limb Rehabilitation Robot Research Articles

An Image-Based Interactive Training Method of an Upper Limb Rehabilitation Robot

Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.

A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient's training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient's condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.

Compensation-corrective adaptive control strategy for upper-limb rehabilitation robots

Trunk compensation is a common behavior observed in stroke patients during rehabilitation, and it can hinder their recovery outcomes. To address this issue, we developed a new upper-limb rehabilitation robot that takes advantage of both end-effector and exoskeleton robots. Moreover, we propose a compensation-corrective adaptive control (CCAC) strategy, which employs an admittance model and incorporates two estimators. Specifically, the first estimator is designed to assess human intention, allowing for compliant human-robot interaction. The second estimator calculates dynamic assistance that adjusts for trunk compensation, utilizing two virtual forces applied to the hand and shoulder. Based on this novel CCAC strategy, the newly designed robot is capable of assisting upper limb movements and correcting compensatory postures simultaneously. Results indicate a significant reduction in trunk compensation across three types of reaching tasks when the robot provides assistance. Moreover, the CCAC strategy enhances upper-limb motor performance, resulting in reduced position errors and increased shoulder and elbow joint angles. These findings underscore the potential of the proposed CCAC strategy, combined with upper-limb exoskeleton robots, as a promising approach for correcting compensatory postures and optimizing the advantages of robotic stroke rehabilitation.

Mechanical design of an upper limb robotic rehabilitation system

Physical diseases have become an alarming social problem worldwide. Several conditions related to limb mobility are more frequent due to a high level of demand in the repetitive execution of activities. Assistive technologies have been proposed to improve the experience of physical rehabilitation. This paper presents a framework with the mechanical design criteria for an upper limb assistive robotic system. We define the design specifications and construction characteristics of an assistive system. As a result, a 7-degree-of-freedom system for upper limb physical rehabilitation is designed with a novel characteristic of arm-switching configuration for the treatment of both arms.

Unsupervised robot-assisted rehabilitation after stroke: feasibility, effect on therapy dose, and user experience

BackgroundUnsupervised robot-assisted rehabilitation is a promising approach to increase the dose of therapy after stroke, which may help promote sensorimotor recovery without requiring significant additional resources and manpower. However, the unsupervised use of robotic technologies is not yet a standard, as rehabilitation robots often show low usability or are considered unsafe to be used by patients independently. In this paper we explore the feasibility of unsupervised therapy with an upper limb rehabilitation robot in a clinical setting, evaluate the effect on the overall therapy dose, and assess user experience during unsupervised use of the robot and its usability.MethodsSubacute stroke patients underwent a four-week protocol composed of daily 45 min-sessions of robot-assisted therapy. The first week consisted of supervised therapy, where a therapist explained how to interact with the device. The second week was minimally supervised, i.e., the therapist was present but intervened only if needed. After this phase, if participants learnt how to use the device, they proceeded to two weeks of fully unsupervised training. Feasibility, dose of robot-assisted therapy achieved during unsupervised use, user experience, and usability of the device were evaluated. Questionnaires to evaluate usability and user experience were performed after the minimally supervised week and at the end of the study, to evaluate the impact of therapists’ absence.ResultsUnsupervised robot-assisted therapy was found to be feasible, as 12 out of the 13 recruited participants could progress to unsupervised training. During the two weeks of unsupervised therapy participants on average performed an additional 360 min of robot-assisted rehabilitation. Participants were satisfied with the device usability (mean System Usability Scale scores > 79), and no adverse events or device deficiencies occurred.ConclusionsWe demonstrated that unsupervised robot-assisted therapy in a clinical setting with an actuated device for the upper limb was feasible and can lead to a meaningful increase in therapy dose. These results support the application of unsupervised robot-assisted therapy as a complement to usual care in clinical settings and pave the way to its application in home settings.Trial registrationRegistered on 13.05.2020 on clinicaltrials.gov (NCT04388891).

Research on multi-dimensional intelligent quantitative assessment of upper limb function based on kinematic parameters

Research on multi-dimensional intelligent quantitative assessment of upper limb function based on kinematic parameters

Hybrid Adaptive Impedance and Admittance Control Based on the Sensorless Estimation of Interaction Joint Torque for Exoskeletons: A Case Study of an Upper Limb Rehabilitation Robot

Physiotherapy is the treatment to recover a patient’s mobility and limb function after an injury, illness, or disability. Rehabilitation robots can be used to replace human physiotherapists. To ensure safety during robot physical therapy, the patient’s limb needs to be controlled to track a desired joint trajectory, and the torque due to interaction force/torque needs to be measured and regulated. Therefore, hybrid impedance and admittance with position control (HIPC) is required to track the trajectory and simultaneously regulate the contact torque. The literature describes two structures of HIPC: (1) a switched framework between admittance and impedance control operating in parallel (HIPCSW); and (2) a series connection between admittance and impedance control without switching. In this study, a hybrid adaptive impedance and position-based admittance control (HAIPC) in series is developed, which consists of a proportional derivative-based admittance position controller with gravitational torque compensation and an adaptive impedance controller. An extended state observer is used to estimate the interaction joint torque due to human stiff contact with the exoskeleton without the use of force/torque sensor, which is then used in the adaptive algorithm to update the stiffness and damping gains of the adaptive impedance controller. Simulation results obtained using MATLAB show that the proposed HAIPC significantly reduces the mean absolute values of the actuation torques (control inputs) required for the shoulder and elbow joints in comparison with HIPC and HIPCSW.

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 feasibility analysis of magnetorheological flexible joint for upper limb rehabilitation

Traditional upper limb rehabilitation robots have several disadvantageous. For example, they can only conduct rehabilitation training along predetermined trajectories, their safety systems are unreliable, and they lack the ability to adjust or train the affected limb based on the expected torque of the human body. To overcome these limitations, this study proposes a flexible safety system for joint rehabilitation utilising magnetorheological (MR) fluid. MR damper inverters offer significant advantages, including high torque, rapid response, controllable flexibility, and safety assurance. The range of motion trajectories can be adjusted using a four-lever hinge mechanism. The necessary driving force is provided by the motor actuator, and the MR damper imparts flexibility and variable damping characteristics to the output torque. The system uses a force/position impedance safety-control method, and using an internal position closed-loop controller, the MR upper limb rehabilitation flexible joint guides the affected limb to a safe position. A simulation is performed to verify the accuracy of the system’s motion torque and position. Extensive research has been conducted on the safe rehabilitation outcomes of the upper limb rehabilitation system under three working conditions (step, incremental, and equation) involving the interaction moment of the affected limb. Simulation and experimental results demonstrate that the MR damper effectively controls the upper limb rehabilitation system to achieve the desired results, even when subjected to incremental and abrupt interaction forces from the patient. The tracking accuracy error remains within the range of 3%–7% for a certain period, confirming the safety and feasibility of the MR-based upper limb rehabilitation robot design.

Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force

In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient's active rehabilitation willingness, the rehabilitation therapist will use the robot-assisted training strategy for patients who gradually have the ability to generate active force. This study proposed a motor function assessment technology for human upper-limb based on fuzzy recognition on interaction force and human-robot interaction control strategy based on assistance-as-needed. A passive training mode based on the calculated torque controller and an assisted training mode combined with the potential energy field were designed, and then the interactive force information collected by the three-dimensional force sensor during the training process was imported into the fuzzy inference system, the degree of active participation σ was proposed, and the corresponding assisted strategy algorithms were designed to realize the adaptive adjustment of the two modes. The significant correlation between the degree of active participation σ and the surface electromyography signals (sEMG) was found through the experiments, and the method had a shorter response time compared to a control strategy that only adjusted the mode through the magnitude of interaction force, making the robot safer during the training process.

Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base.

In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.

基于SEA的上肢康复机器人按需辅助控制策略

基于SEA的上肢康复机器人按需辅助控制策略

Adaptive neural tracking control for upper limb rehabilitation robot with output constraints

AbstractThe authors investigate the trajectory tracking control problem of an upper limb rehabilitation robot system with unknown dynamics. To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot, an adaptive neural full‐state feedback control is proposed. The neural network is utilised to approximate the dynamics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient. By incorporating a high‐gain observer, unmeasurable state information is integrated into the output feedback control. Taking into consideration the issue of joint position constraints during the actual rehabilitation training process, an adaptive neural full‐state and output feedback control scheme with output constraint is further designed. From the perspective of safety in human–robot interaction during rehabilitation training, log‐type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region. The stability of the closed‐loop system is proved by Lyapunov stability theory. The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.

Assistance control strategy for upper-limb rehabilitation robot based on motion trend

Abstract. Robot-assisted rehabilitation has proven to improve a subject's upper-extremity motor function. However, it is still challenging to control the robot to provide minimal assistance based on the subject's performance. This paper proposes a motion-trend-based assistance control strategy to solve this problem. The control strategy provides the corresponding normal and tangential forces by constructing an adaptive virtual assistance force field around a predetermined training trajectory. In the normal direction, a performance function based on the position-tracking error and normal motion trend is established to adjust the normal assistance force field strength in real time; in the tangential direction, a performance function based on the tangential interaction force and tangential motion trend is established to adjust the tangential assistance force field strength in real time. Additionally, good motion trends can quickly reduce the assistance force field. The normal motion trend represents the state of the subject moving toward the target trajectory, and the tangential motion trend represents the state of increasing tangential interaction force. Finally, the performance of this control strategy was evaluated by training experiments with eight healthy subjects. Preliminary experiments showed that the normal assist force in the active movement phase was 92.48 % smaller than that in the poor phase, and the tangential assist force was 90.73 % smaller than that in the slack phase. And the normal assist force and tangential assist force will become zero within 0.2 s when the subject has a good tendency to move. This shows that the control strategy proposed in this paper can quickly adjust the assistance according to the subject's motor performance. In addition, the assistance can be quickly reduced when the subject has a good movement trend. Future work will incorporate OpenSim (muscle and bone simulation software) to develop a pathway suitable for the subject's arm rehabilitation.

User Experience Evaluation of Upper Limb Rehabilitation Robots: Implications for Design Optimization: A Pilot Study.

With the development of science and technology, people are trying to use robots to assist in stroke rehabilitation training. This study aims to analyze the result of the formative test to provide the orientation of upper limb rehabilitation robot design optimization. We invited 21 physical therapists (PTs) and eight occupational therapists (OTs) who had no experience operating any upper limb rehabilitation robots before, and 4 PTs and 1 OT who had experience operating upper limb rehabilitation robots. Data statistics use the Likert scale. The general group scored 3.5 for safety-related topics, while the experience group scored 4.5. In applicability-related questions, the main function score was 2.3 in the general group and 2.4 in the experience group; and the training trajectory score was 3.5 in the general group and 5.0 in the experience group. The overall ease of use score was 3.1 in the general group and 3.6 in the experience group. There was no statistical difference between the two groups. The methods to retouch the trajectory can be designed through the feedback collected in the formative test and gathering further detail in the next test. Further details about the smooth trajectory must be confirmed in the next test. The optimization of the recording process is also important to prevent users from making additional effort to know it well.

Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control

To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb’s structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.

Research on fuzzy impedance control of upper-limb rehabilitation robot based on membership functions online optimization learning strategy

PurposeTo improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose.Design/methodology/approachFirst, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control.FindingsThis method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test.Originality/valueThe TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.

A Review of Research on Upper Extremity Rehabilitation Robots

Background:: Most of the patients with limb paralysis caused by stroke and traumatic brain injury are unable to take care of themselves, which not only causes great physical and psychological stress to the patients but also poses a heavy burden on their families and society. The global population is entering the aging phase. The number and proportion of older people is increasing in almost every country in the world. Population aging has the potential to become one of the most important social trends of the 21st century, and almost all areas of society are affected by it, including labor and financial markets, demand for goods and services, such as housing, transportation, and social security, family structures, and intergenerational relationships. Rehabilitation robotics is a research field that has emerged from this demand. Objective:: Through the analysis of the current situation and development of upper limb rehabilitation robots, several types of upper limb rehabilitation robots with the most widely used structures today are extracted from the representative results, and each robot is analyzed and elaborated in detail. It provides a reference for readers to choose the appropriate upper limb rehabilitation robots. Methods:: We have classified the upper limb rehabilitation robots through several patents, briefly explained and analyzed the robots grouped, summarized the advantages and disadvantages of various robots, and provided an outlook on future development trends. Results:: Through the research and analysis of the degree of freedom and control system of the upper limb rehabilitation robot, it can be seen that certain upper limb rehabilitation robots have large structures and complex systems, and their application scope is limited; most domestic rehabilitation robots have fewer degrees of freedom and can only provide rehabilitation training for single joints or a few degrees of freedom, especially for the shoulder, which has a small spatial range, too low range of motion and amplitude, and a single mode of motion, and cannot complete a wide range of motion for certain joints, as well as a compound motion for multiple joints, thus failing to provide adequate stimulation of the central nerve. Conclusion:: The analysis shows that the existing upper limb rehabilitation robots are poorly adaptable and not well-suited for situations, such as for different patients and different recovery periods of the same patient. The upper limb rehabilitation robot solution needs to be more appropriately designed to meet all requirements in the rehabilitation movement of patients.

Human–Robot Interaction Evaluation-Based AAN Control for Upper Limb Rehabilitation Robots Driven by Series Elastic Actuators

Human–Robot Interaction Evaluation-Based AAN Control for Upper Limb Rehabilitation Robots Driven by Series Elastic Actuators

Customized Trajectory Optimization and Compliant Tracking Control for Passive Upper Limb Rehabilitation.

Passive rehabilitation training in the early poststroke period can promote the reshaping of the nervous system. The trajectory should integrate the physicians' experience and the patient's characteristics. And the training should have high accuracy on the premise of safety. Therefore, trajectory customization, optimization, and tracking control algorithms are conducted based on a new upper limb rehabilitation robot. First, joint friction and initial load were identified and compensated. The admittance algorithm was used to realize the trajectory customization. Second, the improved butterfly optimization algorithm (BOA) was used to optimize the nonuniform rational B-spline fitting curve (NURBS). Then, a variable gain control strategy is designed, which enables the robot to track the trajectory well with small human-robot interaction (HRI) forces and to comply with a large HRI force to ensure safety. Regarding the return motion, an error subdivision method is designed to slow the return movement. The results showed that the customization force is less than 6 N. The trajectory tracking error is within 12 mm without a large HRI force. The control gain starts to decrease in 0.5 s periods while there is a large HRI force, thereby improving safety. With the decrease in HRI force, the real position can return to the desired trajectory slowly, which makes the patient feel comfortable.