Upper-limb Rehabilitation Robot Research Articles

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.

Bionic Design of a Novel Portable Hand-Elbow Coordinate Exoskeleton for Activities of Daily Living

This paper presents the mechanical design and test of a portable hand-elbow combination linkage upper limb rehabilitation robot, which can realize the joint movement of the hand joint and elbow joint and reproduce the complete grasping action. The joints that need bionic support are determined according to the characteristics of human upper limbs and hands, and the overall bionic mechanism is designed. The Motion module in SolidWorks is used to simulate and analyze the rehabilitation robot. The measurement experiment and grasping experiment of joint mobility are carried out on the experimental prototype. As a result, the angular displacement and linear displacement curves obtained via the simulation results are smooth. The measurement experiment of the joint range of motion confirms that the joint range of motion is also within the range of the normal joint angle of the human body, and the grasping experiment shows that the exoskeleton can grasp and lift a 1.801-kg cylindrical object and other daily necessities of different shapes. This result shows that the design of the portable hand-elbow combination linkage upper limb rehabilitation robot is reasonable, can satisfy the rehabilitation training requirements of the hand and upper limb, and has some ability to assist users in daily life.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.89180,2.67530 and 8.02580, respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

Effects of Error Modulation-Based Visual and Haptic Feedback Fusion Strategies on Motor Learning and Motivation

Visual or haptic feedback based on error modulation has been used to improve the effect of robot-assisted rehabilitation training. However, there are several investigations on the effects of error modulation-based visual and haptic feedback fusion strategies on motor learning and motivation. To observe the influence of different feedback fusion strategies on motor learning and motivation, a parallel controlled study was conducted, dividing 30 healthy subjects into three groups with similar skill levels. The no error modulation group received visual and haptic feedback without error modulation; the visual amplification haptic reduction group received visual error amplification combined with haptic error reduction, and the visual reduction haptic amplification (VRHA) group received visual error reduction combined with haptic error amplification. Each subject implemented a trajectory-tracking task with an upper limb rehabilitation robot. They went through baseline, training, assessment, and generalization tests and completed 340 consecutive tracking movements. To evaluate motor learning and motivation, the average tracking error, the root mean square (RMS) of surface electromyography (sEMG) signals, and the intrinsic motivation inventory scale were all examined. In the assessment tests, the average tracking error was significantly decreased in all three groups. In particular, the VRHA group had a larger reduction in average tracking error in the generalization test, lower RMS of sEMG signals both in the assessment and generalization tests and higher perceived competence in the assessment tests. The VRHA fusion strategy significantly improved the subjects’ motor learning and transfer ability, decreased muscle activation, and increased motor learning motivation. These findings may provide some new insights for multisensory feedback fusion technology in the application of rehabilitation robots.

Collision avoidance analysis of human-robot physical interaction based on null-space impedance control of a dynamic reference arm plane.

When the terminal upper limb rehabilitation robot is used for motion-assisted training, collisions between the manipulator links and the human upper limb may occur due to the null-space self-motion of the redundant manipulator. A null-space impedance control method based on a dynamic reference arm plane is proposed to realize collision avoidance during human-robot physical interaction motion for the collision problem between the manipulator links and the human upper limb. Firstly, a dynamic model and a Cartesian impedance controller of the manipulator are established. Then, the null-space impedance controller of the redundant manipulator is established based on the dynamic reference plane, which manages the null-space self-motion of the redundant manipulator to prevent collision between the manipulator links and the human upper limb. Finally, it is experimentally verified that the method proposed in this paper can effectively manage the null-space self-motion of the redundant manipulator, and thus achieve collision avoidance during the human-robot physical interaction motion. This research has significant potential in improving the safety and feasibility of motion-assisted training with rehabilitation robots.

Proposing a Graphic Simulator for an Upper Limb Exoskeleton Robot

In this study, a graphic simulator that is used to simulate problems related to kinematics and dynamics for an exoskeleton robot arm with 5 degrees of freedom (DoF) was presented. The graphic simulator utilized the advantages of design software SolidWorks, Catia, and the computing and simulation power of SimMechanics Toolbox in Matlab. The core of the proposed graphic simulator is algorithm to solve the kinematics and dynamic problems of a developing upper-limb rehabilitation robot. The study used the proposed optimization-based algorithm to solve the inverse kinematics (IK) problem for the redundant robot model. Endpoint trajectories were imported from measurement data. The joints variable solutions obtained before entering the dynamics problem were smoothed to ensure feasibility in the later calculation process. A process to solve the inverse dynamics problem using physical model by combining the power of two software SolidWorks and SimMechanics was also proposed. This process ensured that the Robot’s design could be changed and updated to the kinematics calculation fast and easily. To evaluate this procedure, we also compared these dynamics results with results when applying the Lagrange–Euler formulation. All these calculation and simulation processes have been integrated into the graphic simulator software to show efficiency and user-friendliness.

Research on configuration design and intelligent compliance control of reconfigurable modular flexible upper limb rehabilitation robot

The upper limb exoskeleton rehabilitation robot can realize the partial functional compensation of upper limb and complete the various types of rehabilitation training for each joint of upper limb. However, the existing upper limb exoskeleton rehabilitation robots are lack of flexible reconfigurability, which are difficult to meet the diversified patient objects and rehabilitation needs, and have some problems, such as insufficient motion compliance, poor portability, and wearing comfort. To effectively solve the above problems and improve the effect of upper limb rehabilitation training, this project plans to carry out the following research: Firstly, analyze the structural characteristic and movement mechanism of upper limb, clarify the configuration theory of the modular flexible upper limb exoskeleton rehabilitation robot with reconfigurable, and design and optimize the mechanism form and structural parameters of the reconfigurable modular flexible upper limb exoskeleton rehabilitation robot. Secondly, based on the perspective of rigid–flexible coupling integration of bone–muscle–robot, the integrated equivalent mechanism model is constructed and the integrated dynamics model is established to plan compliance motion and develop intelligent compliance control strategy. Finally, the simulation experimental platform is built for simulation experimental demonstration of upper limb rehabilitation training. The implementation of this study will provide new idea and method for realizing the effect of flexible, compliance, light, and comfortable of upper limb rehabilitation training.

Cortical activation in robot-assisted dynamic and static resistance training combining VR interaction: An fNIRS based pilot study.

There are few isometric training systems based on upper limb rehabilitation robots. Its efficacy and neural mechanism are not well understood. This study aims to investigate the cortex activation of dynamic resistance and static (isometric) training based on upper limb rehabilitation robot combined with virtual reality (VR) interaction by using functional near-infrared spectroscopy (fNIRS). Twenty subjects were included in this study. The experiment adopts the block paradigm design. Experiment in dynamic and static conditions consisted of three trials, each consisting of task (60 s)-rest (40 s). The neural activities of the sensorimotor cortex (SMC), premotor cortex (PMC) and prefrontal cortex (PFC) were measured. The cortex activation and functional connectivity (FC) were analyzed. Both the dynamic and static training can activate SMC, PMC, and PFC. In SMC and PMC, the activation of static training was stronger than dynamic training, there were significant differences between the two modes of each region of interest (ROI) (p < 0.05) (SMC: p = 0.022, ES = 0.72, PMC: p = 0.039, ES = 0.63). Besides, the FC between all ROIs of the static training was stronger than that of the dynamic training. The static training based on upper limb rehabilitation robot may better facilitate the cortical activation associated with motor control.

The effect of training using an upper limb rehabilitation robot (HEXO-UR30A) in chronic stroke patients: A randomized controlled trial.

Upper limb robotic rehabilitation can be beneficial to the patients when applied appropriately. HEXO-UR30A is a novel exoskeletal type upper limb rehabilitation robot that provides continuous passive motion to the shoulder joint. The purpose of this study is to evaluate the effectiveness of HEXO-UR30A on the patient's functional change, spasticity, and range of motion (ROM). We included stroke patients with upper limb hemiparesis of age > 19 years with spasticity grading of modified Ashworth scale < 3 and Brunnstrom recovery stage ≥ 4. The efficacy of the robot was investigated based on a rehabilitation program for 3 weeks. Patient's functions were compared before vs after treatment and between the HEXO group vs control. We conducted the Fugl-Meyer Assessment of the Upper Extremity, modified Barthel index, modified Ashworth scale, ROM, and Motricity Index upper limb. Patients' satisfaction was evaluated using a questionnaire after every 10 sessions of training. In the HEXO group, the Fugl-Meyer assessment for shoulder improved significantly (P value = .006*) compared with the control group (P value = .075). Both groups showed significant improvement (P value < .05) in Motricity Index upper limb after treatment. There were some improvements in the passive and active ROM. Patients in the HEXO group reported high satisfaction with upper limb rehabilitation. These results show that HEXO-UR30A can improve functional ability in chronic stroke patients. Moreover, the high satisfaction in patients might promote active involvement in upper limb rehabilitation.

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.

Impedance learning adaptive super‐twisting control of a robotic exoskeleton for physical human‐robot interaction

AbstractThis study addresses two issues about the interaction of the upper limb rehabilitation robot with individuals who have disabilities. The first step is to estimate the human's target position (also known as TPH). The second step is to develop a robust adaptive impedance control mechanism. A novel Non‐singular Terminal Sliding Mode Control combined with an adaptive super‐twisting controller is being developed to achieve this goal. This combination's purpose is to provide high reliability, continuous performance tracking of the system's trajectories. The proposed adaptive control strategy reduces matched dynamic uncertainty while also lowering chattering, which is the sliding mode's most glaring issue. The proposed TPH is coupled with adaptive impedance control with the use of a Radial Basis Function Neural Network, which allows a robotic exoskeleton to simply track the desired impedance model. To validate the approach in real‐time, an exoskeleton robot was deployed in controlled experimental circumstances. A comparison study has been set up to show how the adaptive impedance approach proposed is better than other traditional controllers.

Design and control of a cable-driven rehabilitation robot for upper and lower limbs – ERRATUM

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Cognitive and motor cortex activation during robot-assisted multi-sensory interactive motor rehabilitation training: An fNIRS based pilot study.

This study aimed to evaluate the effects of multiple virtual reality (VR) interaction modalities based on force-haptic feedback combined with visual or auditory feedback in different ways on cerebral cortical activation by functional near-infrared spectroscopy (fNIRS). Methods: A modular multi-sensory VR interaction system based on a planar upper-limb rehabilitation robot was developed. Twenty healthy participants completed active elbow flexion and extension training in four VR interaction patterns, including haptic (H), haptic + auditory (HA), haptic + visual (HV), and haptic + visual + auditory (HVA). Cortical activation changes in the sensorimotor cortex (SMC), premotor cortex (PMC), and prefrontal cortex (PFC) were measured. Four interaction patterns all had significant activation effects on the motor and cognitive regions of the cerebral cortex (p < 0.05). Among them, in the HVA interaction mode, the cortical activation of each ROI was the strongest, followed by HV, HA, and H. The connectivity between channels of SMC and bilateral PFC, as well as the connectivity between channels in PMC, was the strongest under HVA and HV conditions. Besides, the two-way ANOVA of visual and auditory feedback showed that it was difficult for auditory feedback to have a strong impact on activation without visual feedback. In addition, under the condition of visual feedback, the effect of fusion auditory feedback on the activation degree was significantly higher than that of no auditory feedback. The interaction mode of visual, auditory, and haptic multi-sensory integration is conducive to stronger cortical activation and cognitive control. Besides, there is an interaction effect between visual and auditory feedback, thus improving the cortical activation level. This research enriches the research on activation and connectivity of cognitive and motor cortex in the process of modular multi-sensory interaction training of rehabilitation robots. These conclusions provide a theoretical basis for the optimal design of the interaction mode of the rehabilitation robot and the possible scheme of clinical VR rehabilitation.

Trajectory tracking control of upper limb rehabilitation robot based on optimal discrete sliding mode control

In this paper, an optimal sliding mode control method for trajectory tracking of discrete-time systems based on linear quadratic regulator is proposed to improve the trajectory tracking control accuracy and robustness of upper limb rehabilitation robot under the condition of highly nonlinearities, external disturbances, and unmodeled dynamics. Firstly, considering the uncertainty of the mass and moment of inertia of the connecting rod arm of the upper limb rehabilitation robot and the uncertainty of external interference, the dynamic model of the upper limb rehabilitation robot is established by using the Euler Lagrange method, and the linear time-varying state equation of the rehabilitation robot system under the influence of both nonlinear and uncertain factors is derived. Secondly, directing at the chattering problem in sliding mode control, a sliding mode control method based on a new discrete time reaching law is designed to reduce the amplitude of chattering in the control input signal of the upper limb rehabilitation robot system and improve the tracking speed. Furthermore, combined with linear quadratic optimal control, the optimal discrete integral sliding mode control law (LQRSMC) is finally obtained. Meanwhile, for the sake of reducing the influence of the uncertain signal on the system, a robust control law is adopted to estimate and compensate the uncertain interference. The stability of the upper limb rehabilitation robot system is verified by the sliding mode approach condition of the discrete system. Finally, the genetic algorithm is used to further optimize the weighted value, and MATLAB/Simulink is used to simulate the state trajectory of the upper limb rehabilitation robot under various weighted values. The control strategy can not only effectively weaken the trajectory tracking oscillation problem of the upper limb rehabilitation robot, but also overcome the external disturbance and modeling uncertainty, while ensuring the robustness of the rehabilitation robot system.

Control Strategy for Power Assist Upper Limb Rehabilitation Robot with the Therapist’s Motion Intention Prediction

Currently, fully automated rehabilitation robots can assist therapists in providing rehabilitation therapy, hence the patients could get hurt. On the other hand, manual treatment may cause less patient injury but it is tiresome, and there are not enough therapists in most countries. Power assist rehabilitation robots can support the therapists in conducting the treatment and may help to alleviate this problem. The goal of this study is to develop a control strategy for the robot to assist the therapist’s movement in a power assist upper limb rehabilitation treatment. The system combines the advantages of robotic and manual rehabilitation therapy. Torque and position sensors fitted on the power assist upper limb rehabilitation robot arm are used for motion intention estimation. The amount of angular velocity necessary to be delivered to the feedback controller will be determined by predicting the therapist‘s motion intention using the impedance control method. The resulting velocity from the motion intention estimator is incorporated into the Sliding Mode Control - Function Approximation Technique (SMC-FAT) based adaptive controller. The SMC-FAT based adaptive controller in the feedback loop, overcomes the uncertain parameters in the combination of the robot and the human arm. The motion intention estimator forecasts the movement of therapists. The proposed controller is used to regulate elbow flexion and extension motion on a power assist upper limb rehabilitation robot with one degree of freedom (DOF). The proposed control system has been tested using MATLAB simulation and hardware experimental tests. The outcomes demonstrate the effectiveness of the proposed controller in directing the rehabilitation robot to follow the desired trajectory based on the therapist's motion intention, with maximum errors of 0.002rad/sec, 0.005rad/sec and 0.02rad/sec for sinusoidal, constant torque values, and hardware experiment respectively. ABSTRAK: Pada masa ini, robot rehabilitasi automatik sepenuhnya dapat membantu ahli terapi dalam menyediakan terapi pemulihan, tetapi pesakit berkemungkinan tercedera. Sebaliknya, rawatan manual berkemungkinan mengurangkan kecederaan pesakit tetapi ia memenatkan, dan terdapat kurang ahli terapi yang mencukupi di kebanyakan negara. Robot pembantu rehabilitasi dapat membantu ahli terapi dalam menjalankan pemulihan dan mengurangkan masalah ini. Sistem ini menggabungkan kelebihan terapi pemulihan robotik dan manual. Alat pengesan tork dan kedudukan diletakkan pada anggota atas lengan robot rahabilitasi yang digunakan bagi mengesan anggaran jarak pergerakan ahli terapi. Anggaran halaju sudut diperlukan bagi kawalan gerak balas dan dapat diketahui melalui anggaran niat gerakan ahli terapi menggunakan kaedah kawalan impedans. Halaju yang terhasil daripada anggaran niat gerakan diadaptasi ke dalam pengawal adaptif berasaskan Kawalan Mod Gelongsor - Teknik Anggaran Fungsi (SMC-FAT). Pengawal penyesuaian berasaskan SMC-FAT dalam gelung maklum balas, mengatasi parameter yang tidak pasti dalam gabungan robot dan lengan manusia. Penganggar niat gerakan meramalkan gerakan ahli terapi. Pengawal yang dicadangkan digunakan bagi mengawal lenturan siku dan gerakan lanjutan pada robot rehabilitasi dengan satu darjah kebebasan (DOF). Sistem kawalan yang dicadangkan telah diuji menggunakan simulasi MATLAB dan ujian eksperimen perkakasan. Dapatan kajian menunjukkan keberkesanan pengawal yang dicadangkan dalam mengarahkan robot rehabilitasi mengikut trajektori yang dikehendaki berdasarkan niat gerakan ahli terapi, dengan ralat maksimum masing-masing 0.002rad/s dan 0.005rad/s bagi sinusoidal, nilai tork malar, dan eksperimen perkakasan masing-masing.

Design of a Rehabilitation Robotic System Based on Physiological Electrical Signals

This paper proposes the design of a rehabilitation robot for upper limb rehabilitation based on physiological electrical signals. The system uses surface electromyography (sEMG) and electroencephalography (EEG) signals to control the robot's movements. The rehabilitation robot has a modular structure consisting of a mechanical hand, a control system, and a data acquisition system. The control system uses a STM32 microcontroller and a Bluetooth module for real-time data transmission and control. The data acquisition system collects sEMG and EEG signals from the user's arm and head respectively, and processes the signals to extract features for controlling the robot. The rehabilitation robot also incorporates a virtual reality system to provide a more immersive and engaging rehabilitation experience.

Experiences of the Collaboration with Regional Hospitals on Upper Limb Rehabilitation Robots and Walking Aids

Experiences of the Collaboration with Regional Hospitals on Upper Limb Rehabilitation Robots and Walking Aids

Review on Upper-limb Rehabilitation Robots for Patients with Brain Injury and Clinical Applications

Review on Upper-limb Rehabilitation Robots for Patients with Brain Injury and Clinical Applications

Variable admittance time-delay control of an upper limb rehabilitation robot based on human stiffness estimation

This paper proposes a new variable admittance time-delay control strategy based on human stiffness estimation for improving the effectiveness of robot-assisted cooperative rehabilitation training. This control strategy is developed and implemented on a planar upper limb rehabilitation robot. Given the minimum-jerk-based desired trajectories of human hand position, in the developed control strategy, a time-delay approximator is utilized to estimate the external disturbances and modeling errors without exact knowledge of dynamics parameters, a sliding mode admittance controller is applied to obtained objective admittance characteristics, and an iterative optimization algorithm is used to estimate human arm stiffness and adjust human-robot interaction compliance. The closed-loop stability of the proposed control method is demonstrated via Lyapunov function theory. Experimental investigations involving ten subjects are conducted to validate the feasibility of the proposed control scheme. The experimental results show that the interaction compliance during cooperative rehabilitation training can be accurately adjusted based on selected admittance parameters and human arm stiffness, and it contributes to satisfying the specific training requirements of patients with different weakness levels and promoting the effectiveness of the robot-assisted training.