Rehabilitation Training Process Research Articles

An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model

In the rehabilitation training process for stroke patients, the level of excitement in the patient’s physiological state has a positive impact on the efficacy of the training. In order to improve patients’ initiative during training and prevent dependence on assistive systems, this study proposes an assist-as-needed control strategy based on a subjective intention decline model. The strategy primarily consists of two modules: a subjective intention decline control module and a limb movement assessment module. The subjective intention decline module collects surface electromyography (sEMG) data during patient training and optimizes support vector machine (SVM) using quantum particle swarm optimization (QPSO) algorithms to establish a subjective intention decline model. The limb movement assessment module collects information such as interaction force and position error during training and proposes a method for evaluating the motion state of the affected limb. This model combines traditional impedance control with a method for assessing limb movement and subjective status, automatically adjusting the level of assistive force on the affected limb in real time to enhance its active participation in tasks. Finally, we performed two verification experiments to assess the patient’s initiative in participating in the training. The experimental results show that the proposed method effectively reduced the average assist force by 65.66% for the traditional impedance control training system and effectively the average assist force by 35.2% for the control training system using only the assist force module based on force position information. At the same time, the accuracy of the subjective intention attenuation module established in the experiment to identify the fatigue level of the subjects reached 93.41%. Therefore, the proposed method effectively improves the initiative of trainers and also prevents patients from relying on the assist-as-needed control training system.

Rehabilitation Training after Spinal Cord Injury Affects Brain Structure and Function: From Mechanisms to Methods.

Spinal cord injury (SCI) is a serious neurological insult that disrupts the ascending and descending neural pathways between the peripheral nerves and the brain, leading to not only functional deficits in the injured area and below the level of the lesion but also morphological, structural, and functional reorganization of the brain. These changes introduce new challenges and uncertainties into the treatment of SCI. Rehabilitation training, a clinical intervention designed to promote functional recovery after spinal cord and brain injuries, has been reported to promote activation and functional reorganization of the cerebral cortex through multiple physiological mechanisms. In this review, we evaluate the potential mechanisms of exercise that affect the brain structure and function, as well as the rehabilitation training process for the brain after SCI. Additionally, we compare and discuss the principles, effects, and future directions of several rehabilitation training methods that facilitate cerebral cortex activation and recovery after SCI. Understanding the regulatory role of rehabilitation training at the supraspinal center is of great significance for clinicians to develop SCI treatment strategies and optimize rehabilitation plans.

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

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

Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology.

Traditional respiratory rehabilitation training fails to achieve visualization and quantification of respiratory data in improving problems such as decreased lung function and dyspnea in people with respiratory disorders, and the respiratory rehabilitation training process is simple and boring. Therefore, this article designs a biofeedback respiratory rehabilitation training system based on virtual reality technology. It collects respiratory data through a respiratory sensor and preprocesses it. At the same time, it combines the biofeedback respiratory rehabilitation training virtual scene to realize the interaction between respiratory data and virtual scenes. This drives changes in the virtual scene, and finally the respiratory data are fed back to the patient in a visual form to evaluate the improvement of the patient's lung function. This paper conducted an experiment with 10 participants to evaluate the system from two aspects: training effectiveness and user experience. The results show that this system has significantly improved the patient's lung function. Compared with traditional training methods, the respiratory data are quantified and visualized, the rehabilitation training effect is better, and the training process is more active and interesting.

Study on the rehabilitation training effect of exoskeletal diseases based on the skeletal muscle geometry modeling method

Abstract Plantar pressure is the main mechanical parameter to understand human lower limb movement, which can fully show the health status of human spine, foot, leg and other regions.At present, researchers have carried out empirical analysis on the rehabilitation training process of lower limbs, but due to the defects of guidance methods and too vague evaluation indicators, it is impossible to conduct comprehensive monitoring and feedback on the rehabilitation process of patients. Therefore, on the basis of the modeling method based on the understanding of skeletal muscle set, the rest of the lower limbs dyskinesia patients rehabilitation training, puts forward the plantar pressure changes as the core of human lower limb rehabilitation training monitoring and feedback methods, at the same time using autoregressive model to construct the algorithm model of lower limb exoskeleton, updating method of the weighted coefficient of contrast research. The final results show that the least mean square error (LMS) algorithm can provide effective driving force for the rehabilitation training model of lower extremity exoskeleton disease, and feedback evaluation of the whole rehabilitation training process

지적장애인의 직장 내 대인관계 경험에 관한 현상학적 연구

The purpose of this study is to reveal the content and structure of interpersonal experiences in the workplace of people with intellectual disabilities. To achieve this purpose, 10 workers with intellectual disabilities in their 20s and 30s were selected as research participants by applying a phenomenological research method, and data collected through in-depth interviews were analyzed based on Giorgi's five-step procedure. First, 7 components and 22 sub-components were derived from the interpersonal experience of intellectually disabled people, and 7 components were 'interpersonal relationship with non-disabled people', 'interpersonal relationship formation at work', 'resolution of interpersonal conflicts at work', and 'wish for the future'. Second, the interpersonal experience structure of people with intellectual disabilities in the workplace was identified as the structure of the experience according to the relationship object and the temporal and situational context, focusing on seven components. Accordingly, this study is meaningful in that it revealed the interpersonal experience of people with intellectual disabilities at work and derived the desire for interpersonal relationships of people with intellectual disabilities. Based on these results, it is possible to build a work structure that considers the psychological characteristics of people with intellectual disabilities in the work place in the field of vocational rehabilitation. In addition, it is expected to contribute to overall interpersonal relationships in the vocational rehabilitation training process for people with intellectual disabilities, such as using them as basic data for developing interpersonal relationship programs at counseling sites.

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.

Проект Хаддад: реинтродукция перелётных соколов в Кыргызской Республике

The Hadad international project in Kyrgyzstan is implementing in accordance with the program of the Kingdom of Saudi Arabia dedicated to 'brighter future for the environmental efforts and conservation projects'. This project is pursuing balanced and smooth reintroduction process of migratory falcons in order to restore sustainable biodiversity in Central Asian region. Through the joint efforts of the two teams, we established a base for transporting of 115 falcons from Saudi Arabia to the Kyrgyz Republic. Upon arrival, the birds are going to be released into the wild under a specially developed reintroduction protocol. Preliminary expeditions to the nesting habitats of falcons and studies of different factors such as feeding base and anthropogenic threats have been conducted, and the preffered landscape for the Saker Falcon (Falco cherrug) was described to make the release process as successful as possible. Along with technical activities, scientific tasks are carried out as well, both accomplished and planned for the future. Some of them are listed below: 1) reintroduction approaches and methods, 2) DNA analysis, 3) study of migration routs of the released falcons (to be analyzed by means of satellite tracking), 4) local falcons population distribution, 5) development of soft rehabilitation period and training process methodology, 6) using innovative GIS computer technologies to store and present observation data.

FRACTIONAL ORDER PIλDμ FOR TRACKING CONTROL OF A NOVEL REHABILITATION ROBOT BASED ON IIMO-BP NEURAL NETWORK ALGORITHM

In this study, we develop a novel multi-posture lower limb rehabilitation robot with three postures, which can provide different amplitudes and frequencies of rehabilitation training for hip, knee and ankle joints, respectively. The kinematic and dynamic analyses of the robot are carried out to solve the kinematic forward and backward solutions and the Lagrangian dynamics equations of the lower limbs. The angle, angular velocity and angular acceleration ideal trajectory curves of the rehabilitation motion are derived by using a quintic polynomial trajectory planning scheme. An improved ions motion optimization (IIMO) algorithm is proposed and applied to optimize the initial weight of back propagation (BP) neural network, and algorithm is used to adjust five parameters of fractional order [Formula: see text] ([Formula: see text]) control in controller design. The passive training experiment results of prototype show that the designed controller has the largest average error of angle and angular velocity of hip, knee and ankle joints in high amplitude and high frequency movement mode, which are 1.091∘, 0.716∘, 0.412∘, 1.551∘/s, 1.394∘/s, 1.498∘/s, respectively. At low amplitude and low frequency, the maximum average errors are the smallest, which are 0.351∘, 0.341∘, 0.167∘; 0.833∘/s, 0.842∘/s, 0.398∘/s, respectively. The actual trajectory curve fits well with the designed one. The highest accuracy of angle and angular velocity can reach 99.165% and 99.116% through comprehensive comparison of all motion modes. Therefore, the overall error is small. The stability of rehabilitation training process is ensured, and the rationality and effectiveness of trajectory planning and control design are verified.

Adaptive cooperative control of a soft elbow rehabilitation exoskeleton based on improved joint torque estimation

A current challenge with robot-assisted rehabilitation is to combine the active involvement and voluntary participation of patients into the rehabilitation training process to enhance therapy outcome. This paper presents a soft elbow exoskeleton for the rehabilitation training of disabled patients. An adaptive cooperative control strategy is investigated to promote active participation based on an improved joint torque estimation method and a time-delay sliding mode control scheme. The surface electromyography signals from biceps and triceps are input into a Hill-type musculoskeletal model and a Gaussian radial basis functional network to estimate human elbow joint torque and motion intention. The joint torque estimation experiment, intention-based trajectory following experiment and patient-active free-motion training experiment are carried out by five healthy volunteers and five stroke patients to evaluate the performance of proposed control strategy. The results indicate that the proposed scheme can guarantee the joint torque estimation accuracy and position control accuracy. Moreover, it allow patients to actively dominate the training path based on their motion intention with different training intensity.

Study on upper limb joint angle prediction method based on sEMG

Aiming at the problems of insufficient human-computer interaction and human-machine coupling in the rehabilitation training process, a prediction model of upper limb joint angle is proposed and verified by experiments. Firstly, a mixture vector that can well represent the motion intention of the upper limbs is obtained based on sEMG; secondly, the signal preprocessing, feature optimization and extraction of temporal eigenvalues are completed; finally, for the problems of unsatisfactory prediction accuracy and slow prediction speed of the current models in the field of motion control, the least square method (LSM) is adopted. The upper limb joint angle prediction is realized by multiplying the support vector machine (LSSVM) first. The experimental results show that the prediction model proposed in this paper can well predict the motion trajectory of the upper limb joints of the human body according to the sEMG and attitude information, effectively reduce the prediction time delay and error, and has certain advantages.

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.

Real-Time Hand Gesture Tracking for Human–Computer Interface Based on Multi-Sensor Data Fusion

Cerebral palsy is one of the main factors leading to children’s disability. A large number of such children have hand motor dysfunction, such as limited range of motion, abnormal gestures, etc. Our goal is to design a prototype of wearable gesture training equipment for such children. For this purpose, this paper presents the development of a wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application and most of them can only provide one degree of freedom for finger flexion and extension. In this paper, a data glove integrated with nine degree of freedom inertial sensors in conjunction with complex multi-sensor data fusion is developed. In our method, a single/dual state measurement update switching extended Kalman filter is proposed to estimate the spatial attitude of each finger segment. Through traversing the kinematic chain, a tree type hand dynamic model based on joint constraints is established to realize the real-time gesture tracking. Furthermore, a visual interface is developed to display the tracking effect of gestures, and the reliability of our algorithm is verified by optical contrast experiments, the repeatability of joint angle is also evaluated by kinematic analysis. In general, all the experimental results demonstrated that our proposed framework can accurately track the 3-D hand motion. This glove will help quantify joint stiffness and monitor patient progression during the rehabilitation training process.

Realisation of a Virtual Reality based Remedial Module for Cognition and Hand Function Rehabilitation

Hand function disorders of an individual hinders them from performing activities of daily living with ease and sometimes limit their cognitive growth. As a remedial treatment, nowadays virtual reality (VR) emerged as a gaming platform that has been employed for the rehabilitation of such individuals. User motivation and engagement has risen with the development of VR technology. The developed remedial module is based on a VR platform using two games for upper limb rehabilitation with a hand motion sensing device (HMSD) to provide a solution for sensing hands and fingers movements. The movement was measured with the development of the games based on bilateral hand coordination and manual dexterity, the subset of Bruininks-Oseretsky Test (BOT2) of Motor Proficiency. However, the result of the system considered here is based on the system usability scale (SUS) for healthy individuals. Thus, the system is simple, economical, portable, user-friendly, and does not require intruding external hardware to wear. Hence, over the time these engaging VR rehabilitation games can provide an effective rehabilitation training process for upper limb hemiparesis children.

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.

Design and testing of a soft parallel robot based on pneumatic artificial muscles for wrist rehabilitation

Wrist rehabilitation is needed to help post-stroke and post-surgery patients recover from wrist fracture or injury. Traditional rehabilitation training is conducted by a therapist in a hospital, which hinders timely treatment due to the corresponding time and space constraints. This paper presents the design and implementation of a soft parallel robot for automated wrist rehabilitation. The presented wrist rehabilitation robot integrates the advantages of both soft robot and parallel robot structures. Unlike traditional rigid-body based rehabilitation robots, this soft parallel robot exhibits a compact structure, which is highly secure, adaptable, and flexible and thus a low-cost solution for personalized treatment. The proposed soft wrist-rehabilitation robot is driven by six evenly distributed linear actuators using pneumatic artificial muscles and one central linear electric motor. The introduced parallel-kinematic mechanism design enables the enhancement of the output stiffness of the soft robot for practical use. An electromyography sensor is adopted to provide feedback signals for evaluating the rehabilitation training process. A kinematic model of the designed robot is derived, and a prototype is fabricated for experimental testing. The results demonstrate that the developed soft rehabilitation robot can assist the wrist to realize all the required training motions, including abduction-adduction, flexion-extension, and supination-pronation. The compact and lightweight structure of this novel robot makes it convenient to use, and suitable rehabilitation training modes can be chosen for tailored rehabilitation at home or in a hospital.

Control Strategy and Experimental Research of Cable-Driven Lower Limb Rehabilitation Robot

The passive training with fixed trajectory is more suitable for the initial rehabilitation training of patients with no muscle strength in the affected limb. In order to meet the needs of patients' rehabilitation training in different rehabilitation stages and improve the active participation and rehabilitation training effect of patients, a fuzzy sliding mode variable admittance (FSMVA) controller based on safety evaluation and supervision is proposed for the cable-driven lower limb rehabilitation robot (CDLR) in this paper. The FSMVA controller consists of an inner loop fuzzy sliding mode controller, an outer loop variable admittance controller, and a safety evaluation and supervision module. Through the safety evaluation index of the CDLR system and the comprehensive evaluation of patients' rehabilitation effect given by rehabilitation physiotherapists, a real-time adjustment algorithm of variable admittance controller parameters is designed, which can realize the real-time switching of training modes and adjustment of variable admittance controller parameters in active training mode and passive training mode. The trajectory tracking experiments with no admittance, fixed admittance, and variable admittance based on safety evaluation and supervision were carried out on the experimental platform. The experimental results show that the proposed FSMVA control strategy has high position control accuracy in the active training mode. In addition, the training intensity and the active participation of patients can be increased according to the patient's exercise ability. In the passive training mode, according to the training needs of patients, the amount of the trajectory adjustment can be increased to improve the comfort of the training process and the safety of patients. It lays a foundation for the further study of the evaluation system of the rehabilitation training process and the experiment of human-machine interaction compliance.

Control of a New Cycling Rehabilitation Robot Based on Fuzzy PID

For completing the standardized rehabilitation training of the affected limb based on the lower limb rehabilitation training robot system, it is designed that a lower limb rehabilitation robot control system based on fuzzy PID control method to realize stable and accurate control of the rehabilitation training process. First of all, this paper analyzes the ranges of motion of human hip and knees, and a single degree of freedom lower limb rehabilitation training robot system based on the belt transmission principle is designed. Then the fuzzy PID control is analyzed. And the Matlab is used to simulate the passive rehabilitation mode of the lower limb rehabilitation training robot system under different control strategies. The designed rehabilitation training test shows that the control system of the lower limb rehabilitation robot based on the fuzzy PID control method has a small speed overshoot, high control accuracy and smoother operation.

Quantitative Assessment of Motor Function for Patients with a Stroke by an End-Effector Upper Limb Rehabilitation Robot.

With the popularization of rehabilitation robots, it is necessary to develop quantitative motor function assessment methods for patients with a stroke. To make the assessment equipment easier to use in clinics and combine the assessment methods with the rehabilitation training process, this paper proposes an anthropomorphic rehabilitation robot based on the basic movement patterns of the upper limb, point-to-point reaching and circle drawing movement. This paper analyzes patients' movement characteristics in aspects of movement range, movement accuracy, and movement smoothness and the output force characteristics by involving 8 patients. Besides, a quantitative assessment method is also proposed based on multivariate fitting methods. It can be concluded that the area of the real trajectory and movement accuracy during circle drawing movement as well as the ratio of force along the sagittal axis in backward point-to-point movement are the unique parameters that are different remarkably between stroke patients and healthy subjects. The fitting function has a high goodness of fit with the Fugl-Meyer scores for the upper limb (R2 = 0.91, p = 0.015), which demonstrates that the fitting function can be used to assess patients' upper limb movement function. The indicators are recorded during training movement, and the fitting function can calculate the scores immediately, which makes the functional assessment quantitative and timely. Combining the training process and assessment, the quantitative assessment method will farther expand the application of rehabilitation robots.

Combination of wearable sensors and internet of things and its application in sports rehabilitation

Modern neurological rehabilitation medicine and its clinical research results show that through the scientific rehabilitation training, the damaged limb motor function can be restored to some extent. During the entire rehabilitation process, the physician needs to monitor and evaluate the patient’s physical condition and training effect in real time in order to develop or adjust the follow-up rehabilitation training program. Therefore, this paper takes the sports rehabilitation training as the background and based on the Internet of Things technology to develop a sports rehabilitation monitoring system based on wearable sensors and Internet of Things technology. Through the development of the sensory sensor monitoring sensor terminal node, based on the needs of physiological parameter monitoring during the rehabilitation training process, a monitoring system including electrocardiogram (ECG) signals, electromyography (EMG) signals, motion posture, body temperature and other physiological parameters was constructed based on the Internet of Things technology. The experimental results show that the system constructed in this paper can closely monitor the changes of vital signs of target users while providing real-time monitoring, and provide feedback. These physiological data can be analyzed to help doctors formulate effective rehabilitation training programs. Evaluate the recovery of exercise capacity and training participation, so as to achieve the purpose of improving training efficiency and achieving rehabilitation effects