Upper-limb Rehabilitation Training Research Articles

Force-Field based assisted control for upper-limb rehabilitation robots

Robot-assisted training with an assist-as-needed (AAN) control strategy has been used in clinical investigations to effectively enhance the subject’s active effort and promote the rehabilitation of individuals with upper-limb motor impairment. In this paper, a force field-based AAN control scheme is proposed for robot-assisted upper limb rehabilitation training and then implemented in a robot-assisted rehabilitation training system (RaRTS). A spatial varying assistance force field is constructed around the predetermined trajectory in this scheme, and then a varying force field coefficient based on the subject’s motor performance is adopted to deliver the AAN features with temporal freedom. The normal force field, tangential force field, and viscous force field are the three components of the constructed spatial varying assistance force field. Furthermore, the proposed scheme contains three training modes that are switched according to the subject’s trajectory tracking error. Finally, task-oriented continuous training experiments with different force field factors are conducted on able-bodied subjects to evaluate and validate the performance and feasibility of the proposed scheme. Preliminary experimental results and statistics analysis suggest that the subjects were able to keep much closer to the predetermined trajectory through an appropriate selection of force-field assistance factors. Furthermore, the robot would apply an assistance force as needed as the motor performance deteriorate, which could prevent the subject from losing confidence due to poor performance.

Impact of task-oriented training based on acupuncture rehabilitation on upper extremity function and quality of life of patients with early stroke.

Eighty percent of stroke patients develop upper limb dysfunction, especially hand dysfunction, which has a very slow recovery, resulting in economic burden to families and society. To investigate the impact of task-oriented training based on acupuncture therapy on upper extremity function in patients with early stroke. Patients with early stroke hemiplegia who visited our hospital between January 2021 and October 2022 were divided into a control group and an observation group, each with 50 cases. The control group underwent head acupuncture plus routine upper limb rehabilitation training (acupuncture therapy). In addition to acupuncture and rehabilitation, the observation group underwent upper limb task-oriented training (30 min). Each group underwent treatment 5 d/wk for 4 wk. Upper extremity function was assessed in both groups using the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Wolf Motor Function Rating Scale (WMFT), modified Barthel Index (MBI), and Canadian Occupational Performance Measure (COPM). Quality of life was evaluated using the Short-Form 36-Item Health Survey (SF-36). Clinical efficacy of the interventions was also evaluated. Before intervention, no significant differences were observed in the FMA-UE, MBI, and WMFT scores between the two groups (P > 0.05). After intervention, the FMA-UE, WMFT, MBI, COPM-Functional Mobility and Satisfaction, and SF-36 scores increased in both groups (P < 0.05), with even higher scores in the observation group (P < 0.05). The observation group also obtained a higher total effective rate than the control group (P < 0.05). Task-oriented training based on acupuncture rehabilitation significantly enhanced upper extremity mobility, quality of life, and clinical efficacy in patients with early stroke.

Effect of task-oriented training assisted by force feedback hand rehabilitation robot on finger grasping function in stroke patients with hemiplegia: a randomised controlled trial

BackgroundOver 80% of patients with stroke experience finger grasping dysfunction, affecting independence in activities of daily living and quality of life. In routine training, task-oriented training is usually used for functional hand training, which may improve finger grasping performance after stroke, while augmented therapy may lead to a better treatment outcome. As a new technology-supported training, the hand rehabilitation robot provides opportunities to improve the therapeutic effect by increasing the training intensity. However, most hand rehabilitation robots commonly applied in clinics are based on a passive training mode and lack the sensory feedback function of fingers, which is not conducive to patients completing more accurate grasping movements. A force feedback hand rehabilitation robot can compensate for these defects. However, its clinical efficacy in patients with stroke remains unknown. This study aimed to investigate the effectiveness and added value of a force feedback hand rehabilitation robot combined with task-oriented training in stroke patients with hemiplegia.MethodsIn this single-blinded randomised controlled trial, 44 stroke patients with hemiplegia were randomly divided into experimental (n = 22) and control (n = 22) groups. Both groups received 40 min/day of conventional upper limb rehabilitation training. The experimental group received 20 min/day of task-oriented training assisted by a force feedback rehabilitation robot, and the control group received 20 min/day of task-oriented training assisted by therapists. Training was provided for 4 weeks, 5 times/week. The Fugl-Meyer motor function assessment of the hand part (FMA-Hand), Action Research Arm Test (ARAT), grip strength, Modified Ashworth scale (MAS), range of motion (ROM), Brunnstrom recovery stages of the hand (BRS-H), and Barthel index (BI) were used to evaluate the effect of two groups before and after treatment.ResultsIntra-group comparison: In both groups, the FMA-Hand, ARAT, grip strength, AROM, BRS-H, and BI scores after 4 weeks of treatment were significantly higher than those before treatment (p < 0.05), whereas there was no significant difference in finger flexor MAS scores before and after treatment (p > 0.05). Inter-group comparison: After 4 weeks of treatment, the experimental group’s FMA-Hand total score, ARAT, grip strength, and AROM were significantly better than those of the control group (p < 0.05). However, there were no statistically significant differences in the scores of each sub-item of the FMA-Hand after Bonferroni correction (p > 0.007). In addition, there were no statistically significant differences in MAS, BRS-H, and BI scores (p > 0.05).ConclusionHand performance improved in patients with stroke after 4 weeks of task-oriented training. The use of a force feedback hand rehabilitation robot to support task-oriented training showed additional value over conventional task-oriented training in stroke patients with hand dysfunction.Clinical trial registration informationNCT05841108

Adaptive hybrid-mode assist-as-needed control of upper limb exoskeleton for rehabilitation training

In this paper, a novel adaptive hybrid-mode assist-as-needed (AHMAAN) control algorithm is designed for the exoskeleton-assisted upper limb rehabilitation training. The overall control framework includes an outer control loop to calculate the required interaction force, and an inner control loop to drive the exoskeleton track subject’s motion, and provide specific target interaction force obtained from the outer control loop. In the outer control loop, a hybrid control mode is proposed, which consists of resistive mode and assistive mode. In this regard, a virtual tunnel is firstly established around the defined training task path, and the training mode is switched according to the deviation between the subject’s position and the boundary of the virtual tunnel. Furthermore, for tuning the strength of the resistance or assistance to the subjects with different motor capabilities, two adjustable gain factors are designed, whose values are adaptively adjusted according to the subject’s training performance by using a fuzzy logic. Then, for the inner control loop, a barrier Lyapunov function-based controller is designed to constrain exoskeleton tracking errors within the defined boundary. Meanwhile, time delay estimation (TDE) technology is used to estimate the uncertain terms of the system, and a robust adaption law is developed to compensate TDE error. Experimental tests have been performed on an upper limb exoskeleton with three healthy subjects to evaluate the effectiveness of the developed method. The results show that the proposed control scheme can be effectively applied in a variety of rehabilitation requirements and achieves better training performance than classical hybrid-mode assist-as-needed control method.

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.

Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN.

Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe's action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient's upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method's recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient's rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.

Effect of Virtual Reality Upper Limb Rehabilitation Training on Older Adults

Virtual reality has gained more attention in the physical training field, but few studies focus on the effects of VR on older adults. Based on existing study we suggest that VR-based upper limb training might be more effective for older adults and used functional near inferred spectrum and movement analysis to evaluate the effects of VR-based training on older adults. 20 older and 20 youth adults were recruited to perform VR training by extending their upper limb to reaching the objects, and non-VR training as a contrast. Both age-related and task-related differences were found in cortical activation, showing that the VR training has aroused more cortical activation. The older groups have more intensive movement but perform worse in terms of task completion. Both groups performed better in VR, and the difference in the older group was higher.

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

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

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System.

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the potential to meet these demands by providing engaging and motivating therapy options. The digital occupational training system is a VR application that utilizes cutting-edge technologies, including multi-touch screens, virtual reality, and human-computer interaction, to offer diverse training techniques for advanced cognitive capacity and hand-eye coordination abilities. The objective of this study was to assess the effectiveness of this program in enhancing cognitive function and upper extremity rehabilitation in stroke patients. The training and assessment consist of five cognitive modules covering perception, attention, memory, logical reasoning, and calculation, along with hand-eye coordination training. This research indicates that after eight weeks of training, the digital occupational training system can significantly improve cognitive function, daily living skills, attention, and self-care abilities in stroke patients. This software can be employed as a time-saving and clinically effective rehabilitation aid to complement traditional one-on-one occupational therapy sessions. In summary, the digital occupational training system shows promise and offers potential financial benefits as a tool to support the functional recovery of stroke patients.

A novel robotic system enabling multiple bilateral upper limb rehabilitation training via an admittance controller and force field

Patients with hemiplegia are usually restricted to performing general bilateral activities of daily life (gbADLs). Bilateral training has been verified to contribute to the rehabilitation of physical functions. Although robotic systems are gradually being employed in the field of rehabilitation, few studies have performed simulations with regards to gbADLs for training. Therefore, a novel end-effector bilateral rehabilitation robotic system (EBReRS) for the upper limb is developed in this article for the task rendering of gbADLs, in which the gbADL-corresponding workspace is obtained via modularly designed bilateral parallelogram mechanisms. In addition, the interaction rendering of multiple bimanual modes (uncoupled, trans-soft-coupled, trans-semi-coupled, and rotation-coupled) is achieved by implementing the admittance model, the inner force field between robotic end-effectors, and the outer force field distributed around. Experiments of the proposed four rehabilitation training modes were carried out on the healthy subject, with the results showing a feasible method of the EBReRS in the simulation of multiple bimanual coordinated rehabilitation training tasks. In the future, the constructed EBReRS is expected to be exploited for home rehabilitation as a coordinated training device.

Clinical efficacy of ultrasound-guided stellate ganglion block combined with extracorporeal shock wave therapy on limb spasticity in patients with ischemic stroke

IntroductionTo observe the clinical efficacy of ultrasound-guided stellate ganglion block (SGB) + extracorporeal shock wave therapy (ESWT) for limb spasticity in patients with ischemic stroke.MethodsA total of 60 patients with post-stroke limb spasticity in our hospital were selected and randomly divided into four groups (n = 15). In the control group, patients received routine rehabilitation training. Based on routine rehabilitation training, SGB group patients underwent ultrasound-guided SGB, ESWT group patients received ESWT, and SGB + ESWT group patients received ultrasound-guided SGB combined with ESWT. The total treatment course was one month. The Modified Barthel Index (MBI) score, Fugl-Meyer Assessment and upper limb rehabilitation training system were applied to evaluate the activities of daily living, upper limb motor function and upper limb performance before and after treatment. Finally, the improvement after treatment was compared among different groups.ResultsAfter treatment, compared with the control group, the MBI score and the upper limb score based on Fugl-Meyer Assessment in the SGB, ESWT, and SGB + ESWT groups were significantly increased (P < 0.05). Furthermore, compared with the SGB and ESWT groups, SGB + ESWT exhibited a higher upper limb function score (P < 0.05), while the MBI score was not significantly different (P > 0.05). In terms of upper limb performance ability, patients in the SGB, ESWT and SGB + ESWT groups had better fitting degree, participation and exertion of exercise than those in the control group, and the SGB + ESWT group patients had the same movement trajectory as robots.ConclusionUltrasound-guided SGB and ESWT can reduce the muscle tension of patients, alleviate spasticity, promote the motor function of the upper limb, and improve the working performance of patients. However, the effect of SGB combined with ESWT is better.

Adaptive Learning based Upper-Limb Rehabilitation Training System with Collaborative Robot.

Rehabilitation training for patients with motor disabilities usually requires specialized devices in rehabilitation centers. Home-based multi-purpose training would significantly increase treatment accessibility and reduce medical costs. While it is unlikely to equip a set of rehabilitation robots at home, we investigate the feasibility to use the general-purpose collaborative robot for rehabilitation therapies. In this work, we developed a new system for multi-purpose upper-limb rehabilitation training using a generic robot arm with human motor feedback and preference. We integrated surface electromyography, force/torque sensors, RGB-D cameras, and robot controllers with the Robot Operating System to enable sensing, communication, and control of the system. Imitation learning methods were adopted to imitate expert-provided training trajectories which could adapt to subject capabilities to facilitate in-home training. Our rehabilitation system is able to perform gross motor function and fine motor skill training with a gripper-based end-effector. We simulated system control in Gazebo and training effects (muscle activation level) in Open-Sim and evaluated its real performance with human subjects. For all the subjects enrolled, our system achieved better training outcomes compared to specialist-assisted rehabilitation under the same conditions. Our work demonstrates the potential of utilizing collaborative robots for in-home motor rehabilitation training.Clinical relevance-The collaborative robot system is capable of providing safe and effective training comparable to specialized rehabilitation robots, enabling possibilities of convenient rehabilitation training at home.

Assistance level quantification-based human-robot interaction space reshaping for rehabilitation training.

Stroke has become a major disease that seriously threatens human health due to its high incidence and disability rates. Most patients undergo upper limb motor dysfunction after stroke, which significantly impairs the ability of stroke survivors in their activities of daily living (ADL). Robots provide an optional solution for stroke rehabilitation by attending therapy in the hospital and the community, however, the rehabilitation robot still has difficulty in providing needed assistance interactively like human clinicians in conventional therapy. For safe and rehabilitation training, a human-robot interaction space reshaping method was proposed based on the recovery states of patients. According to different recovery states, we designed seven experimental protocols suitable for distinguishing rehabilitation training sessions. To achieve assist-as-needed (AAN) control, a PSO-SVM classification model and an LSTM-KF regression model were introduced to recognize the motor ability of patients with electromyography (EMG) and kinematic data, and a region controller for interaction space shaping was studied. Ten groups of offline and online experiments and corresponding data processing were conducted, and the machine learning and AAN control results were presented, which ensured the effective and the safe upper limb rehabilitation training. To discuss the human-robot interaction in different training stages and sessions, we defined a quantified assistance level index that characterizes the rehabilitation needs by considering the engagement of the patients and had the potential to apply in clinical upper limb rehabilitation training.

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.

Human–machine interaction controller of upper limb based on iterative learning method with zeroing neural algorithm and disturbance observer

In this paper, an iterative learning controller with zeroing neural algorithm and disturbance observer is proposed to solve the conflicting of human–machine interaction and disturbances in system. This paper presents a theoretical framework, which is able to process rigorous stability analysis of human–machine interaction control. The iterative learning controller is suitable for repetitive upper limb rehabilitation training. Combining tracking error dependent weight vector with zeroing neural algorithm is aimed to reduce the conflicts in various operations of motions between upper limb and machine. The disturbance observer is used to deal with eliminating the disturbance. In addition, simulations of the controller can effectively and safety assist upper limb movement in different training stages.

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.

Multi-user upper limb rehabilitation training system integrating social interaction

COVID-19 causes persistent symptoms such as weakness and myasthenia in most patients. Due to the cross-infection of COVID-19, the traditional face-to-face rehabilitation services are risky for the elderly. To ensure that the elderly in urgent need of rehabilitation services receive training while minimizing the disturbance of the COVID-19 pandemic on their social activities. We have improved the existing virtual upper limb training system, and added a social factor to the system. Seniors with upper limb rehabilitation needs can use the system to compete or collaborate with others for training. In addition, a set of natural and scientific exclusive gestures have been designed under the direction of following the doctor’s advice. The experiment is conducted jointly with the chief physicians of the geriatrics department in the authoritative class-A hospitals of Class III. Our experiment, which lasted for two months, showed that the virtual training system with social factors added had the best rehabilitation effect and enhanced the initiative of patients. The system has value for popularization during the COVID-19 epidemic.

An active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface

Movement function rehabilitation of patients with craniocerebral injuries is an important issue facing neurorehabilitation science. The use of brain–computer interface technology in rehabilitation training systems can allow patients to actively participate in the rehabilitation training process and use the brain’s neuroplasticity to enhance the effects from rehabilitation training. At present, the brain–computer interface-based rehabilitation training system still has problems such as insufficient active participation of patients, resulting in slowed motor neural circuit repair or low action execution accuracy. In response to the above problems, this paper designed an active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface of steady-state visual evoked potentials (SSVEP) and movement-related cortical potentials (MRCPs). The system includes six parts: task setting and training guidance module, EEG signal acquisition module, EEG signal preprocessing and recognition module, rehabilitation training module, training completion evaluation module, and communication module. The system drives the rehabilitation robot to complete the training actions by identifying the participant’s SSVEP and evaluates the completion of the rehabilitation training based on the patient’s movement intention recognition results. In this study, 12 participants were recruited. In the online test, the system achieved an average action execution accuracy of 99.3%. The movement intention detection based on MRCPs reached an average accuracy of 82.7%. The participants’ average completion rate was 0.91. The experimental results show that the system can achieve a high rate of execution accuracy. In addition, it can evaluate the active participation level of patients in rehabilitation training based on the movement intention detection results, accelerate the reconstruction of motor neural circuits, improve the effects of training, and provide more effective ways of thinking for the study of upper limb rehabilitation training systems for patients with craniocerebral injuries.

모야모야병 아동의 로봇 보조 상지 훈련에 대한 효과: 사례 연구

The purpose of this study was to investigate the effect of robot-assisted upper limb rehabilitation training on hand and upper limb function in children with Moya-Moya disease.&#x0D; The intervention method used InMotion®RM, an upper limb-assisted rehabilitation robot, and the evaluation was conducted once each pre- and post-assessment, and the treatment was conducted 10 times for 30 minutes, a total of 12 sessions. The functional movement of the upper limb and hand was evaluated using the Jebsen-Taylor Hand Function Test and the program inherent in the InMotion®RM. The results were compared through a visual analysis using tables and graphs.&#x0D; After the training, the Jepson-Taylor Hand Function Test score improved in all areas, compared to the baseline. Evaluation using the program inherent in InMotion®RM showed improvement in the smoothness, accuracy, efficiency, and stability of upper limb movement.&#x0D; The results of this case study suggest that robot-assisted upper limb training is applicable to children with Moya-Moya disease and that it has a positive effect on improving the function and stability of the upper limb. In the future, more research subjects will be needed to study the method and effectiveness of robot-assisted upper limb training through group research.

Follow control of upper limb rehabilitation training based on Kinect and NAO robot

Gesture imitation is a common rehabilitation strategy in limb rehabilitation training. In traditional rehabilitation training, patients need to complete training actions under the guidance of rehabilitation physicians. However, due to the limited resources of the hospital, it cannot meet the training and guidance needs of all patients. In this paper, we proposed a following control method based on Kinect and NAO robot for the gesture imitation task in rehabilitation training. The method realized the joint angles mapping from Kinect coordination to NAO robot coordination through inverse kinematics algorithm. Aiming at the deflection angle estimation problem of the elbow joint, a virtual space plane was constructed and realized the accurate estimation of deflection angle. Finally, a comparative experiment for deflection angle of the elbow joint angle was conducted. The experimental results showed that the root mean square error of the angle estimation value of this method in right elbow transverse deflection and vertical deflection directions was 2.734° and 2.159°, respectively. It demonstrates that the method can follow the human movement in real time and stably using the NAO robot to show the rehabilitation training program for patients.