Upper-limb Rehabilitation Training Research Articles

Design and torque control base on neural network PID of a variable stiffness joint for rehabilitation robot.

Variable stiffness joints have been gradually applied in rehabilitation robots because of their intrinsic compliance and greater ability to adjust mechanical stiffness. This paper designs a variable stiffness joint for upper limb rehabilitation training. The joint adopts the variable stiffness principle based special curved surface. The trapezoidal lead screw in the variable stiffness module has a self-locking function, and the stiffness can be maintained without the continuous output torque of the motor. In the aspect of control, back propagation (BP) neural network PID control strategy is used to control the torque of variable stiffness joint. Experiments show that this control method can effectively improve the torque control performance of variable stiffness joints in the case of low stiffness, and the isotonic centripetal resistance training can be realized by using the joints and control methods designed in this paper.

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.

Improving Haptic Response for Contextual Human Robot Interaction.

For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task.

Structural design of upper limb rehabilitation training device based on ergonomics and nanomaterials

Structural design of upper limb rehabilitation training device based on ergonomics and nanomaterials

Assist-As-Needed Control Strategy of Bilateral Upper Limb Rehabilitation Robot Based on GMM

Robotic-assisted rehabilitation therapy has been shown to be effective in improving upper limb motor function and the daily behavior of patients with motor dysfunction. At present, the majority of upper limb rehabilitation robots can only move in the two-dimensional plane, and cannot adjust the assistance mode in real-time according to the patient’s rehabilitation needs. In this paper, according to the shortcomings of the current rehabilitation robot only moving in the two-dimensional plane, a type of bilateral mirror upper limb rehabilitation robot structure with the healthy side assisting the affected side is proposed. This can move in three-dimensional space. Additionally, an assist-as-needed (AAN) control strategy for upper limb rehabilitation training is proposed based on the bilateral upper limb rehabilitation robot. The control strategy adopts Gaussian Mixture Model (GMM) and impedance controller to maximize the patient’s rehabilitation effect. In the task’s design, there is no need to rely on the assistance of the therapist, only the patients who completed the task independently. GMM guides the rehabilitation robot to provide different assistance for the patients at different task stages and induces the patients to complete the rehabilitation training independently by judging the extent to which the patients can complete the task. Furthermore, in this paper, the effectiveness of the proposed control strategy was verified by three volunteers participating in a two-dimensional task. The experimental results show that the proposed AAN control strategy can effectively provide appropriate assistance according to the classification stage of the interaction between the patients and the rehabilitation robot, and thus, patients can better achieve the rehabilitation effect during the rehabilitation task as much as possible.

Research on Fractional Order Fuzzy PID Control of the Pneumatic-hydraulic Upper Limb Rehabilitation Training System Based on PSO

Research on Fractional Order Fuzzy PID Control of the Pneumatic-hydraulic Upper Limb Rehabilitation Training System Based on PSO

Design of a virtual reality rehabilitation system for upper limbs that inhibits compensatory movement

Stroke is currently the main cause of death and disability among the elderly in our country. According to medical research, a considerable number of patients with physical disabilities can be recovered through rehabilitation training. However, traditional motor rehabilitation is a continuous, repeated and slow process, and patients tend to feel bored and lose motivation for training.In this paper, we developed a virtual reality rehabilitation system based on Kinect, which is a vision capture sensor, for patients with movement disorders who are at or above Brunnstrom Stage III and have certain motor ability. Through the management platform of the system, physician can obtain the patient's personal information, formulate and adjust the training plan. Patients can control the role in the virtual scene through Kinect sensor, and complete the training action according to the guidance. The system collects the user's motion data in real time and detect the compensation. The system will adaptively evolve to guide the patient to self-correct the compensatory patterns. After the training, the system will evaluate the patients based on the their training performance. Two experiments are also carried out to verify the accuracy of the range of motion and the effectiveness of virtual guidance. It is proved that the virtual reality upper limb rehabilitation training system studied in this paper is reliable, stable, and can guide users to complete the training action and improve the rehabilitation effect.

The effect of clinical application of transcranial direct current stimulation combined with non-immersive virtual reality rehabilitation in stroke patients.

The ability to manipulate the upper limbs and fingers of stroke patients is very important for independent daily life. Among the latest approaches for upper limb rehabilitation training, transcranial direct current stimulation (tDCS) is a non-invasive stimulation method that stimulates the cranial nerves by attaching electrodes to the scalp. In addition, virtual reality (VR) is an intervention method that provides an environment similar to reality and can help restore function by performing body movements as if playing a game. In addition, VR is an intervention method that provides an environment similar to reality and helps to recover functions by performing body movements as if playing a game. This study was conducted to investigate the effect of anodal tDCS applied to the ipsilateral primary motor cortex (M1) during VR training on the upper limb function, cognition, and executive function of stroke patients. After 20 patients were randomly assigned to the experimental group and the control group, the experimental group received tDCS and VR, and the control group received sham tDCS and VR for 20 minutes a day, 5 days a week, for a total of 4 weeks. Participants were evaluated for upper limb function using Box and Block Test (BBT) and Jebsen-Taylor Hand Function Test (JTHFT), and cognitive and executive function using the Stroop Test (ST) and Trail Making Test (TMT). In the experimental group, significant differences were found in the pre- and post-test for the all variance (p< 0.05). Control group is significant differences were found in the pre- and post-test for BBT, ST, TMT (p< 0.05). There were significant differences between the two groups in the post test of BBT and ST (p< 0.05). The results of this study suggest that anodal tDCS applied to the ipsilateral M1 during VR training is effective for upper limb function, cognitive function, and executive function in stroke patients.

Application and comparison of deep learning approaches for upper limb functionality evaluation based on multi-modal inertial data

Nowadays, stroke survivors usually can regain their limb function through rehabilitation training prescribed by physicians. With the development of technologies such as the Internet of Things, artificial intelligence and the rapid increase in research in the field of digital health, a growing number of cutting-edge research focuses on sustaining medical services with digital technologies. In addition, some new approaches have been widely taken in rehabilitation. In the process of upper limb rehabilitation training for stroke survivors, this paper collects a series of inertial sensing time series data through the data collection mechanism. This paper utilizes Dynamic Time Warping-K-NearestNeighbor (DTW-KNN), Long Short-Term Memory (LSTM) neural network and Gated Recurrent Unit (GRU) neural network to process and analyze the time series data of rehabilitation actions, and finally realize the classification and evaluation of different completion situations of various upper limb rehabilitation actions. The experimental results show that the classification accuracy of DTW-KNN under different completion situations of the three upper limb rehabilitation actions are 72.2%, 47.2%, and 69.4%, respectively, while the corresponding classification accuracy of LSTM is 97.2%, 94.4% and 91.7%. LSTM also has significant advantages over DTW-KNN in terms of classification time. In addition, GRU not only has a high classification accuracy rate similar to LSTM, but also has a slight advantage over LSTM in classification time.

Design and Evaluation of a Parallel Cable-Driven Shoulder Mechanism With Series Springs

Abstract Upper limb paralysis and movement disorders resulting from neurologic injuries can be treated with an upper limb exoskeleton robot that assists with movement retraining. Cable-driven exoskeletons have been widely studied because of their lightness, compact structure, and low cost. However, the problems of shoulder squeeze force and system stability have not been solved. In this article, we present a prototype parallel cable-driven shoulder mechanism with series springs. The theoretical analysis suggests that the stability of the mechanism is improved compared with that of the previous mechanism, and the effects of stiffness, upper limb weight, and mechanism parameters on the shoulder joint extrusion pressure are analyzed by simulation and experimental results. The results show that this mechanism plays an important role in reducing or eliminating the shoulder squeeze pressure and improving the stability of the mechanism. Moreover, the mechanism has good portability and can be combined with other exoskeletons to facilitate various robot-assisted upper limb rehabilitation training.

Clinical Effect of Virtual Reality Technology on Rehabilitation Training of Sports Injury.

In order to make most patients recover most of their limb functions after rehabilitation training, virtual reality technology is an emerging human-computer interaction technology, which uses the computer and the corresponding application software to build the virtual reality environment. Completing the training tasks in the virtual environment attracts the patients to conduct repeated training in the game and task-based training mode and gradually realizes the rehabilitation training goals. For the rehabilitation population with certain exercise ability, the kinematics of human upper limbs is mainly analyzed, and the virtual reality system based on HTC VIVE is developed. The feasibility and work efficiency of the upper limb rehabilitation training system were verified by experiments. Adult volunteers who are healthy and need rehabilitation training to participate in the experiment were recruited, and experimental data were recorded. The virtual reality upper limb rehabilitation system was a questionnaire. By extracting the motion data, the system application effect is analyzed and evaluated by the simulation diagram. Follow-up results of rehabilitation training showed that the average score of healthy subjects was more than 4 points and 3.8 points per question. Therefore, it is feasible to perform upper limb rehabilitation training using the HTC VIVE virtual reality rehabilitation system.

SSVEP-based active control of an upper limb exoskeleton using a low-cost brain–computer interface

PurposeFor the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode.Design/methodology/approachThis paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot.FindingsThrough a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system.Originality/valueAn EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.

Research on fNIRS Recognition Method of Upper Limb Movement Intention

This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance.

Application of Virtual Reality Technology (VR) in Practice Teaching of Sports Rehabilitation Major

Patients who can receive rehabilitation training provide two types of feedback. This is the biggest advantage of virtual reality technology for rehabilitation training, and at the same time can improve the patient’s perception of rehabilitation results. This article aims to study the application of virtual reality technology (vr) in sports rehabilitation professional practice teaching. This paper uses the research method of virtual scene position mapping. The virtual scene position mapping is based on the virtual realization technology of upper limb rehabilitation training, which is manipulated by the movement of the patient’s upper limbs. In the experiment, all the students in the first and second classes of a sports rehabilitation major in a local university were subjected to theoretical tests and practical ability assessments to all the students in the first and second classes of the sports rehabilitation major. Experimental data shows that the excellent rate and passing rate of the experimental class’s practical ability test are much higher than those of the control class. Therefore, it can be concluded that the application of virtual reality technology to sports rehabilitation teaching is beneficial to the improvement of learners’ practical ability. The experimental results show that the test excellence rate of the experimental class is 24.2% higher than that of the control class, and the passing rate is 12% higher. Therefore, applying virtual reality technology to sports rehabilitation education can stimulate people’s interest in learning. The virtual environment is very similar to the real world, and the motor skills learned in the virtual environment can be successfully transferred to the real world.

Evaluation of an Information Flow Gain Algorithm for Microsensor Information Flow in Limber Motor Rehabilitation

This paper conducts an evaluative study on the rehabilitation of limb motor function by using a microsensor information flow gain algorithm and investigates the surface electromyography (EMG) signals of the upper limb during rehabilitation training. The surface EMG signals contain a large amount of limb movement information. By analysing and processing the surface EMG signals, we can grasp the human muscle movement state and identify the human upper limb movement intention. The EMG signals were processed by the trap and filter combination denoising method and wavelet denoising method, respectively, the signal-to-noise ratio was used to evaluate the noise reduction effect, and finally, the wavelet denoising method with a better noise reduction effect was selected to process all the EMG signals. After the noise is removed, the signal is extracted in the time domain and frequency domain, and the root mean square (RMS), absolute mean, median frequency in the time domain, and average power frequency in the frequency domain are selected and input to the classifier for pattern recognition. The support vector machine is used to classify the myoelectric signals and optimize the parameters in the support vector machine using the grid search method and particle swarm optimization algorithm and classify the test samples using the trained support vector machine. Compared with the classification results of the grid search optimized support vector machine, the optimized vector machine has a 7% higher recognition rate, reaching 85%. The action recognition classification method of myoelectric signals is combined with an upper limb rehabilitation training platform to verify the feasibility of using myoelectric signals for rehabilitation training. After the classifier recognizes the upper limb movements, the upper computer sends movement commands to the controller to make the rehabilitation platform move according to the recognition results, and finally, the movement execution accuracy of the rehabilitation platform reaches 80% on average.

Additional Effects of Xbox Kinect Training on Upper Limb Function in Chronic Stroke Patients: A Randomized Control Trial.

Background: Xbox Kinect-based virtual reality, being a novel approach, has therapeutic benefits in rehabilitation and its use is encouraged in stroke rehabilitation of upper extremities. Objective: Primary aim of the current study is to investigate the additional effects of Xbox Kinect training in combination with routine physiotherapy exercises based on each component of Fugl-Meyer Assessment Scale for Upper Extremity (FMA-UE). Moreover, effect of upper limb rehabilitation on cognitive functions was also assessed. Methods: This study was a parallel arm randomized control trial. Fifty-six participants were recruited and randomly allocated to either an Xbox Kinect training group (XKGT) or exercise training group (ETG). Measures of concern were recorded using FMA-UE, Box and Block Test (BBT), and Montreal Cognitive Assessment (MOCA). Evaluation was conducted at baseline and after completion of intervention at the sixth week. Results: There were significant differences from pre- to post-intervention scores of FMA-UE and BBT (p < 0.001) in both groups, whereas no difference was observed for MOCA (XKTG p value 0.417, ETG p value 0.113). At six-week follow-up there were significant differences between both groups in FMA-UE total score (p < 0.001), volitional movement within synergies (p < 0.001), wrist (p = 0.021), hand (p = 0.047), grasp (p = 0.006) and coordination/speed (p = 0.004), favoring the Xbox Kinect training group. Conclusion: To conclude, results indicate repetitive use of the hemiparetic upper extremity by Xbox Kinect-based upper limb rehabilitation training in addition to conventional therapy has a promising potential to enhance upper limb motor function for stroke patients.

Comparison of Repetitive Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation on Upper Limb Recovery Among Patients With Recent Stroke.

ObjectiveTo compare the efficacy of repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) on upper limb function recovery among patients who recently had stroke.MethodsSubjects with recent stroke (within 1 month) were randomized to rTMS (n=25) and tDCS (n=26) applied over the non-lesioned hemisphere for three sessions per week, followed by tailored upper limb rehabilitation training for a total of 2 weeks. The primary outcomes were changes in the Motor Assessment Scale (MAS), Fugl-Meyer arm score test, Nine-Hole Peg Test (9HPT), hand grip strength, and modified Barthel Index at weeks 2 and 4. Both therapists responsible for training and assessment were blinded to the intervention allocated.ResultsThere was an improvement in all the motor performance scales among both groups (p<0.001). These improvements persisted at discharge. However, there was no significant difference in any of the assessment scales between the two groups. The rTMS group showed a statistically non-significant greater improvement in MAS, 9HPT, and handgrip strength than the tDCS group.ConclusionBoth interventions produce a statistically significant improvement in upper limb function. There was no statistically significant difference between the two intervention methods with respect to motor performance. It is suggested that a larger study may help to clarify the superiority of either methods.

Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation.

Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects' upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation (p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

Attention Enhancement and Motion Assistance for Virtual Reality-Mediated Upper-Limb Rehabilitation

Dysfunctions of upper limbs caused by diseases such as stroke result in difficulties in conducting day-to-day activities. Studies show that rehabilitation training using virtual reality games is helpful for patients to restore arm functions. It has been found that ensuring active patient participation and effort devoting in the process is very important to obtain better training results. This article introduces a method to help patients increase their engagement and provide motion assistance in virtual reality-mediated upper-limb rehabilitation training. Attention enhancement and motion assistance is achieved through an illusion of virtual forces created by altering the drag speed between the cursor and the object presented on a screen to the patient as the only feedback. We present two game forms using the proposed method, including a target-approaching game and a maze-following game. The results of evaluation experiments with human participants showed that the proposed method could provide path guidance that significantly improved path-following performance of users and required more involvement of the users when compared to playing the game without attention enhancement and motion assistance.

Kinematic analysis and simulation of human upper limb rehabilitation training mechanism

A rehabilitation training mechanism of human upper limb is proposed, it consists of the 3-RRS parallel mechanism and the crank-rocker mechanism. First, their degrees of freedom are analyzed. Then, their kinematics are analyzed by analytical method. Finally, the 3-RRS parallel mechanism and the crank-rocker mechanism are simulated with the software UG and SolidWorks respectively, the results of simulation show that the movement path of the handle on the moving platform of the 3-RRS parallel mechanism coincides with the trajectory of the human arm, it can be used for the rehabilitation training of the human shoulder joint. For the crank-rocker mechanism, the center angles of movement path of the rocker and the handle on the connecting-rod are within the range of motion angles of human elbow joint and wrist joint, it can be used for the rehabilitation training of the elbow and wrist joints. They will all produce soft impulses at the beginning and end points, and the soft impulses will increase as the rotating speed of the driving links increases, so their driving links must rotate at a lower speed.