Upper-limb Rehabilitation Robot Research Articles

Development of a force-field-based control strategy for an upper-limb rehabilitation robot

Abstract. Robot-assisted rehabilitation has proven to be effective for improving the motor performance of patients with neuromuscular injuries. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy training. This paper presents an end-effector upper-limb rehabilitation robot for the functional recovery training of disabled patients. A force-field-based rehabilitation control strategy is then developed to induce active patient participation during training tasks. The proposed control strategy divides the 3D space around the rehabilitation training path into a human-dominated area and a robot-dominated area. It encodes the space around the training path and endows the corresponding normal and tangential force; the tangential component assists with movement along the target path, and the normal component pushes the patient's hand towards the target path using a real-time adjustable controller. Compared with a common force-field controller, the human–robot interaction in this strategy is easy and can be quickly adjusted by changing the force field's range or the variation characteristics of two forces, and the intervention in two directions can change continuously and smoothly despite the patient's hand crossing the two areas. Visual guidance based on the Unity-3D environment is introduced to provide visual training instructions. Finally, the feasibility of the proposed control scheme is validated via training experiments using five healthy subjects.

Design and Analysis of 6-DoFs Upper Limb Assistant Rehabilitation Robot

This paper presents an assisted upper limb rehabilitation robot (ULRR) for patients who have experienced stroke who are in the middle and late stages of rehabilitation and have certain muscle strength. The ULRR can complete adduction and abduction motion of the shoulder joint (SJ) in the frontal plane in one step, which can save time and improve the efficiency of rehabilitation training. Based on the principles of ergonomics and rehabilitation medicine, the freedom degree of the upper limb and the motion range of each joint are determined, and the structure of the shoulder, the elbow, and the wrist joint of ULRR are designed. The kinematics model of the robot is established, and the kinematics equations are derived. Meanwhile, the simulation analysis and the workspace analysis of the robot are carried out, and the different movement forms of SJ adduction and abduction are compared and analyzed. Then, the trajectory of the robot is planned to complete the act of drinking water. Finally, an experimental platform is built to complete the ULRR to help participants complete the experiments of drinking water and active training. The experiments verify that the robot is suitable for rehabilitation tasks.

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Patient-Specific Exercises with the Development of an End-Effector Type Upper Limb Rehabilitation Robot.

End-effector type upper limb rehabilitation robots (ULRRs) are connected to patients at one distal point, making them have simple structures and less complex control algorithms, and they can avoid abnormal motion and posture of the target anatomical joints and specific muscles. Given that the end-effector type ULRR focuses more on the rehabilitation of the combined motion of upper limb chain, assisting the patient to perform collaborative tasks, and its intervention has some advantages than the exoskeleton type ULRR, we developed a novel three-degree-of-freedom (DOF) end-effector type ULRR. The advantage of the mechanical design is that the designed end-effector type ULRR can achieve three DOFs by using a four-bar mechanism and a lifting mechanism; we also developed the patient-specific exercises including patient-passive exercise and patient-cooperative exercise, and the advantage of the developed patient-cooperative exercise is that we simplified the human-robot coupling system model into a single spring system instead of the mass-spring-damp system, which efficiently improved the response speed of the control system. In terms of the organization structure of the work, we introduced the end-effector type ULRR's mechanical design, control system, inverse solution of positions, patient-passive exercise based on the inverse solution of positions and the linear position interpolation of servo drives, and patient-cooperative exercise based on the spring model, in sequence. Experiments with three healthy subjects have been conducted, with results showing good trajectory tracking performance in patient-passive exercise and showing effective, flexible, and good real-time interactive performance in patient-cooperative exercise.

사용자 안전성을 고려한 상지 재활치료 로봇의 병렬 안전 제어 시스템

This study proposes a parallel safety control system for upper-limb rehabilitation robots to ensure patient safety. The mechanical and electrical safety devices to ensure patient safety are designed and developed to operate in several stages. The mechanical safety device is configured in a way to set the limit, and the electrical safety is constructed considering electrical insulation and failure. The safety switch could directly and immediately stop the robot

Design and kinematical performance analysis of the 7-DOF upper-limb exoskeleton toward improving human-robot interface in active and passive movement training

Upper-limb rehabilitation robots have become an important piece of equipment in stroke rehabilitation. The design of exoskeleton mechanisms plays a key role to improve human-robot interface in the upper-limb movements under passive and active rehabilitation training. This paper proposes a novel of the 7-DOF (RR-RR-PRR) under-actuated exoskeleton mechanism based on the characteristics of the upper-limb movements in both of active and passive training. This aim of the proposed work is to improve human-robot interface in rehabilitation training with robots. Firstly, the characteristics of active and passive movement training are analyzed depending on the human upper-limb model. Then, a novel 7-DOF (RR-RR-PRR) exoskeleton mechanism is proposed based on the analyzed characteristics. After that, kinematical performances of the proposed exoskeleton are analyzed on the workspace, manipulability and manipulability ellipsoid by compared with the common exoskeleton configuration of the 7 DOFs (RRR-R-PRR) mechanism. In the end, the prototype is manufactured and tested by undergoing the experiments of single-joint passive movement training and multi-joint active movement training. The human-robot interface of the proposed exoskeleton is demonstrated by root mean square error, Pearson correlation coefficient, and the time-delay difference. The results of the kinematical performance show that the effective workspace and the flexibility of the exoskeleton with the proposed configuration are increased by 10.44% and 1.7%. In the single-joint passive movement training experiment, the root mean square errors are 6.986, 7.568, 5.846, and Pearson correlation coefficients are 0.989, 0.984, 0.988 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.1%. In the multi-joint active movement training experiment, the root mean square errors are 9.312 and 7.677, and Pearson correlation coefficients are 0.906 and 0.968 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.28%. The proposed 7 DOFs exoskeleton mechanism shows uniformity with that of the common exoskeleton on the same rehabilitation trajectory which is effective to improve human-robot interface under passive and active rehabilitation training.

Experiences of patients who had a stroke and rehabilitation professionals with upper limb rehabilitation robots: a qualitative systematic review protocol

IntroductionEmerging evidence suggests that robotic devices for upper limb rehabilitation after a stroke may improve upper limb function. For robotic upper limb rehabilitation in stroke to be successful, patients’ experiences...

TU-173. The effect of upper limb rehabilitation robot training on the motor function and neuroelectrophysiology of stroke patients

TU-173. The effect of upper limb rehabilitation robot training on the motor function and neuroelectrophysiology of stroke patients

Two-Dof Upper Limb Rehabilitation Robot Driven by Straight Fibers Pneumatic Muscles.

In this paper, the design of a 2-dof (degrees of freedom) rehabilitation robot for upper limbs driven by pneumatic muscle actuators is presented. This paper includes the different aspects of the mechanical design and the control system and the results of the first experimental tests. The robot prototype is constructed and at this preliminary step a position and trajectory control by fuzzy logic is implemented. The pneumatic muscle actuators used in this arm are designed and constructed by the authors’ research group.

Electromyogram-based motion compensation control for the upper limb rehabilitation robot in active training

Abstract. Active participation in training is very important for improving the rehabilitation effect for patients with upper limb dysfunction. However, traditional upper limb rehabilitation robots cannot drive the patients' arms by following their varying motion intents during active training. This control strategy can weaken the patients' active participation. This paper proposes a novel center-driven upper limb rehabilitation robot and an electromyogram (EMG)-based motion compensation control method for the upper limb rehabilitation robot in active training in order to improve the patients' active participation. In addition, the trajectory planning equations for the proposed robot manipulator are analyzed and built in order to provide the reference trajectory in active training. In the end, two experiments are carried out to verify the proposed control method. The EMG compensation experiments show that the maximum error between the theoretical and experimental motor rotating speeds is no more than 1.3 %. The active training control experiment results show that the proposed robot can implement the reference trajectory in real time. The control method can implement the positive relationship between the rotating speed and the intensity of EMG emerging during upper limb training. It shows that the proposed rehabilitation robot can provide auxiliary force according to the patients' motion intents. The proposed rehabilitation robot can guide the patients in implementing the reference task in active training.

Testing the Limit Range of Motion Safety Function of Upper Limb Rehabilitation Robots with an Anthropometrically Adjustable and Sensorized Dummy Limb.

Arm type or exoskeleton type rehabilitation robots move the patient's upper limb through one or more, either free or restrained connection points. The rehabilitation robot is unsafe if it moves the patient's upper limb beyond the limits of the anatomical joint ranges. A validation toolkit was developed to assess the risks of "limit anatomical joint range of movement" and "limit anatomical joint overreaching" during the regular operation of a rehabilitation robot. The validation toolkit includes an anthropometrically adjustable and sensorised dummy limb attached to the RACA (rehabilitation, assessment, compensation, or alleviation) rehabilitation robot; and a software tool for off-line risk assessment and reporting.

A Practical Post-Stroke Elbow Spasticity Assessment Using an Upper Limb Rehabilitation Robot: A Validation Study.

Spasticity is a motor disorder characterised by a velocity-dependent increase in muscle tone, which is critical in neurorehabilitation given its high prevalence and potential negative influence among the post-stroke population. Accurate measurement of spasticity is important as it guides the strategy of spasticity treatment and evaluates the effectiveness of spasticity management. However, spasticity is commonly measured using clinical scales which may lack objectivity and reliability. Although many technology-assisted measures have been developed, showing their potential as accurate and reliable alternatives to standard clinical scales, they have not been widely adopted in clinical practice due to their low usability and feasibility. This paper thus introduces an easy-to-use robotic based measure of elbow spasticity and its evaluation protocol. Preliminary results collected with one post-stroke patient and one healthy control subject are presented and demonstrate the feasibility of the approach.

Cross-Validation of Machine Learning Models for the Functional Outcome Prediction after Post-Stroke Robot-Assisted Rehabilitation.

The state of the art is still lacking an extensive analysis of which clinical characteristics are leading to better outcomes after robot-assisted rehabilitation on post-stroke patients. Prognostic machine learning-based models could promote the identification of predictive factors and be exploited as Clinical Decision Support Systems (CDSS). For this reason, the aim of this work was to set the first steps toward the development of a CDSS, by the development of machine learning models for the functional outcome prediction of post-stroke patients after upper-limb robotic rehabilitation. Four different regression algorithms were trained and cross-validated using a nested 5×10-fold cross-validation. The performances of each model on the test set were provided through the Median Average Error (MAE) and interquartile range. Additionally, interpretability analyses were performed, to evaluate the contribution of the features to the prediction. The results on the two best performing models showed a MAE of 13.6 [13.4] and 13.3 [14.8] on the Modified Barthel Index score (MBI). The interpretability analyses highlighted the Fugl-Meyer Assessment, MBI, and age as the most relevant features for the prediction of the outcome. This work showed promising results in terms of outcome prognosis after robot-assisted treatment. Further research should be planned for the development, validation and translation into clinical practice of CDSS in rehabilitation. Clinical relevance- This work establishes the premises for the development of data-driven tools able to support the clinical decision for the selection and optimisation of the robotic rehabilitation treatment.

A novel end-effector upper limb rehabilitation robot: Kinematics modeling based on dual quaternion and low-speed spiral motion tracking control

For patients with upper limb dysfunction after stroke, robot-assisted rehabilitation training plays an important role in functional recovery. The existing upper limb rehabilitation robots have some problems, such as complex mechanisms, insufficient compliance, and can only realize the rehabilitation training of shoulder and elbow joints in the horizontal plane. This research proposes a novel end-effector upper limb rehabilitation robot with three degrees of freedom. Two horizontal rotation freedoms are driven by motors and one vertical translation freedom is driven by a pneumatic cylinder. So it can realize the spatial rehabilitation training of shoulder and elbow joints. The rotation and translation transformation of the robot can be represented by a dual quaternion, which is concise in form and clear in the physical meaning. Therefore, this article adopts dual quaternions to complete the robot’s kinematics modeling, inverse kinematics calculation, and terminal spiral motion trajectory planning. To improve the low-speed moving performance of the spiral motion, a sliding mode control strategy plus feedforward compensation is employed to control the displacement of the cylinder. Experiments show that the robot can realize proximal joints training and has good position tracking accuracy (tracking error is within 2 mm) with smoothness under the proposed control strategy, which can guarantee the accuracy and comfort of passive rehabilitation training, contributing to restoring the function of the impairment upper limbs.

Development of a Wearable Upper Limb Rehabilitation Robot Based on Reinforced Soft Pneumatic Actuators

Dyskinesia of the upper limbs caused by stroke, sports injury, or traffic accidents limits the ability to perform the activities of daily living. Besides the necessary medical treatment, correct and scientific rehabilitation training for the injured joint is an important auxiliary means during the treatment of the effected upper limb. Conventional upper-limb rehabilitation robots have some disadvantages, such as a complex structure, poor compliance, high cost, and poor portability. In this study, a novel soft wearable upper limb rehabilitation robot (SWULRR) with reinforced soft pneumatic actuators (RSPAs) that can withstand high pressure and featuring excellent loading characteristics was developed. Driven by RSPAs, this portable SWULRR can perform rehabilitation training of the wrist and elbow joints. In this study, the kinematics of an SWULRR were analyzed, and the force and motion characteristics of RSPA were studied experimentally. The results provide a reference for the development and application of wearable upper limb rehabilitation robots. An experimental study on the rotation angle of the wrist and the pressure of the RSPA was conducted to test the effect of the rehabilitation training and verify the rationality of the theoretical model. The process of wrist rehabilitation training was tested and evaluated, indicating that SWULRR with RSPAs will enhance the flexibility, comfort, and safety of rehabilitation training. This work is expected to promote the development of wearable upper-limb rehabilitation robots based on modular reinforced soft pneumatic actuators.

Proposing a Method to Solve Inverse Kinematics and Dynamics of a Human Upper Limb Rehabilitation Robot

A rehabilitation robot is a device that has been proving its positive effectiveness in the process of helping patients recover quickly after a stroke. Researching, designing, and manufacturing robot models in general and upper limb rehabilitation robots in particular are very practical. In this study, we proposed to combine the use of an algorithm and a physical modeling method to shorten the calculation process and design an upper limb rehabilitation robot. First, an exoskeleton upper limb rehabilitation robot model (UExosVN) was briefly described. Next, in turn, all the important problems including inverse kinematics, inverse dynamics for this robot model were proposed and solved by using optimization algorithms and physical modeling methods. The model was evaluated in the critical movement of daily operations. The results after the testing process have proven the accuracy and effectiveness of the proposed methods.

Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot

<span>Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation robot. Although the OMBAC is an adaptive and model-based controller, some of its parameters need to be determined precisely. In this paper, these parameters are determined by the grasshopper optimization algorithm (GOA). The Lyapunov method is used to analyze the stability assurance of controlled rehabilitation. The results of the simulation for two tested trajectories (linear and nonlinear trajectories) demonstrate the efficiency of the suggested OMBAC with fast settling time, minimum error steady state, and very small overshoot.</span>

上肢ロボットリハビリテーションの現状と課題

上肢の訓練支援ロボットは，上肢自体の複雑さゆえに下肢に比べてやや遅れて進化している．末梢効果器型が主流であったが，最近は手指型の訓練支援ロボットも開発され，脳卒中をはじめ麻痺や可動域制限を生じている疾患に用いられている．効果のエビデンスも構築され，本邦でも保険が適応となり少しずつ普及が進んでいるが，汎用性や操作性にも配慮が必要であり，まだまだ発展途上である．また，医療もICT化が進みスマートフォンなどのタッチパネル操作が日常生活活動や社会参加に不可欠となっており，操作に必要な機能評価や，訓練，操作能力の改善評価ツールの開発もあわせて期待されている．

Effect of Rehabilitation Robot Combined with Upper Limb Rehabilitation Training on Hemiplegic Patients in Recovery Stage of Stroke

<bold>Objective:</bold> To observe the effect of upper limb rehabilitation robot combined with upper limb rehabilitation training on motor function of upper extremity of hemiplegic patients in recovery stage of stroke. <bold>Methods</bold> A total of 40 hemiplegic patients in recovery stage of stroke who were treated in the Zhujiang Hospital of Southern Medical University from October 2018 to January 2020, which were randomly divided into the control group and the observation group according to the random number table method, with 20 cases in each group. Both groups received basic medication and conventional rehabilitation therapy (including good limb position, active and passive joint movement, sitting and standing balance training, walking function training, assistive device use training, speech and swallowing therapy, conventional physical therapy, etc.). The control group received upper limb rehabilitation training on the basis of basic medication and conventional rehabilitation therapy, 60 minutes a time, once a day, seven days a week, continuous treatment for two weeks. The observation group received upper limb rehabilitation robot training on the basis of the control group, upper limb rehabilitation training for 30 minutes a time, upper limb robot rehabilitation training for 30 minutes a time, once a day, seven days a week, continuous treatment for two weeks. Before and after treatment, the Fugl-Meyer assessment-upper limb (FMA-UL) was used to evaluate the motor function of upper extremity, including the FMA shoulder and elbow (FMA-SE) score and the FMA wrist and hand (FMA-WH) score; the Barthel index (BI) was used to evaluate activities of daily living; the root mean square (RMS) and integral electromyography (iEMG) values of the biceps, triceps, deltoid of anterior and middle fiber of the affected upper limbs of the subjects during maximal isometric contraction were measured by surface electromyography to assess muscle activation and synchronization of motor recruitment of the upper limbs. <bold>Results</bold> A total of 38 hemiplegia patients in the recovery stage of stroke completed the whole course of treatment. Compared with before treatment, FMA-UL score, FMA-SE score, FMA-WH score, BI score, RMS values of biceps, triceps, deltoid of anterior and middle fiber, and iEMG values of the both groups after treatment were significantly higher, with statistically significant differences (<italic>P</italic>&lt;0.05). Compared with the control group, the FMA-UL score, FMA-SE score, BI scores, triceps RMS value, deltoid of middle fiber RMS value and iEMG value of observation group were significantly higher, with statistically significant differences (<italic>P</italic>&lt;0.05). <bold>Conclusion</bold> Upper limb rehabilitation robot combined with upper limb rehabilitation training can effectively improve motor function of upper limb, muscle activation and motor unit recruitment synchronization of hemiplegic upper limb, and improve the activities of daily living of hemiplegic patients in recovery stage of stroke, which is worthy of clinical promotion.

Upper-Limb Rehabilitation Robot: A Measure of Intent Detection for Robot Aided Active Therapy Exercise

<h3>Research Objectives</h3> In robot-aided rehabilitation of post-stroke patients with upper limb impairment, robotic device must allow patient's effort in performing exercises. To estimate such effort, user's force exerted on robotic device can be used during active exercises. To validate use of force exerted by user/wearer by comparing with EMG signals as a measure of intent detection in robot-aided active therapy on rehabilitation robot, in this research, user's interactive force with robot was measured and compared with user's muscle activity (EMG) during elbow flexion-extension performed by an upper-limb rehabilitation robot (u-Rob). This physiological measure will help design rehab robot centering patient's need and level of impairment. Furthermore, it can give an indication of spastic behavior during rehab exercises. <h3>Design</h3> Experimental study of robot-aided therapy to observe force values with EMG values, leading to use force as a measure of intent detection. <h3>Setting</h3> Protocol: Elbow flexion/extension (0-120 deg) motion. Experiments (passive and active therapy exercise, five trials/exercise) were conducted with healthy subject in seated position wearing the robot. <h3>Participants</h3> 2 healthy males and 1 healthy female. <h3>Interventions</h3> Elbow flexion/extension (0-120 deg) motion. Experiments (passive and active therapy exercise, five trials) were conducted subject in seated position wearing the robot. <h3>Main Outcome Measures</h3> Force exerted by the participant and participant's EMG <h3>Results</h3> Significant amount of force is exerted during active exercise and significant amount of EMG activity is observed during active exercise. Besides, looking at the torque, it is seen that during active rehab exercise, robot has to contribute lesser torque than in passive exercise. <h3>Conclusions</h3> User-robot interactive forcesdetected by sensor can be used as a measure of intent detection during robot-aided active exercise. Moreover, measured forces can also be observed from muscle activity. It may give an idea of subject's spastic behavior. <h3>Author(s) Disclosures</h3> N/A.