Effect Of Rehabilitation Training Research Articles

Effect of Rehabilitation Training for Pregnancy Outcomes and Postpartum Quality of Life in Thyroid Cancer Survivors: A Propensity Score-Matched Cohort Study

Effect of Rehabilitation Training for Pregnancy Outcomes and Postpartum Quality of Life in Thyroid Cancer Survivors: A Propensity Score-Matched Cohort Study

Design of Lumbar Rehabilitation Training System Based on Virtual Reality

A virtual reality-based lumbar rehabilitation training system is designed to address the increasing number of patients with low back pain (LBP) year by year. Attitude sensors are used to track lower back movement. In order to improve the effect of rehabilitation training, several virtual rehabilitation training games and assessment scenes are designed based on the Unity3D engine to complete different tasks from simple to complex. The goal is to increase patients’ interest in rehabilitation training. The experimental results verify the accuracy of rehabilitation data acquisition, real-time interactive communication, and the smooth operation of rehabilitation scenes.

Electrical stimulation of the cuneiform nucleus enhances the effects of rehabilitative training on locomotor recovery after incomplete spinal cord injury.

Most human spinal cord injuries are anatomically incomplete, leaving some fibers still connecting the brain with the sublesional spinal cord. Spared descending fibers of the brainstem motor control system can be activated by deep brain stimulation (DBS) of the cuneiform nucleus (CnF), a subnucleus of the mesencephalic locomotor region (MLR). The MLR is an evolutionarily highly conserved structure which initiates and controls locomotion in all vertebrates. Acute electrical stimulation experiments in female adult rats with incomplete spinal cord injury conducted in our lab showed that CnF-DBS was able to re-establish a high degree of locomotion five weeks after injury, even in animals with initially very severe functional deficits and white matter lesions up to 80-95%. Here, we analyzed whether CnF-DBS can be used to support medium-intensity locomotor training and long-term recovery in rats with large but incomplete spinal cord injuries. Rats underwent rehabilitative training sessions three times per week in an enriched environment, either with or without CnF-DBS supported hindlimb stepping. After 4 weeks, animals that trained under CnF-DBS showed a higher level of locomotor performance than rats that trained comparable distances under non-stimulated conditions. The MLR does not project to the spinal cord directly; one of its main output targets is the gigantocellular reticular nucleus in the medulla oblongata. Long-term electrical stimulation of spared reticulospinal fibers after incomplete spinal cord injury via the CnF could enhance reticulospinal anatomical rearrangement and in this way lead to persistent improvement of motor function. By analyzing the spared, BDA-labeled giganto-spinal fibers we found that their gray matter arborization density after discontinuation of CnF-DBS enhanced training was lower in the lumbar L2 and L5 spinal cord in stimulated as compared to unstimulated animals, suggesting improved pruning with stimulation-enhanced training. An on-going clinical study in chronic paraplegic patients investigates the effects of CnF-DBS on locomotor capacity.

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.

Low-Dose LPS Modulates Microglia/Macrophages Phenotypic Transformation to Amplify Rehabilitation Effects in Chronic Spinal Cord Injured (CSCI) Mice.

Spinal cord injury (SCI) results in stalled motor function recovery under the chronic phase. One of the reasons due to the presence of ongoing inflammation. Therefore, regulating the status of immune cells may help reopen the window for neural repair, which represents a potential therapeutic target. In this study, we aimed to investigate whether this could be achieved in mice with cervical 5 crush CSCI (4W) by utilizing a concentration of 0.5mg/kg of lipopolysaccharide (LPS) to stimulate microglia/macrophages. Additionally, the mice underwent rehabilitation training for another 6 weeks. Our results showed that systemic injection of LPS enhanced the effects of forelimb rehabilitation training, as evaluated through single pellet grasping (SPG). Electrophysiological studies revealed the restoration of cortical drive to the injured side's forelimb muscles in the training combined with LPS group. Tract tracing studies demonstrated the reconstruction of cortical innervation to the cervical spinal cord. Furthermore, the levels of pro-inflammatory phenotype markers, such as inducible nitric oxide synthase (INOS) and CD68, decreased, while the expression of anti-inflammatory phenotype markers, including arginase 1 (ARG-1) and CD206, increased. Importantly, this phenotypic switch in microglia/macrophages was accompanied by an increase in phagocytic activity markers as indicated by BODIPY + IBA1 + staining. Collectively, our data suggests that low-dose LPS improves the effects of rehabilitation training by regulating the phenotypic transformation of microglia/macrophages in CSCI.This study provides a fresh perspective and intervention direction for the clinical treatment of chronic spinal cord injuries.

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.

Ai-based next-generation sensors for enhanced rehabilitation monitoring and analysis

With advancements in technology and healthcare, the increasing aging population necessitates effective solutions to restore autonomy in elderly individuals suffering from conditions like stroke, cerebral hemorrhage, and muscle atrophy. In order to solve this problem this article is proposing AI-based next-generation sensors for enhanced rehabilitation monitoring and analysis along with other AI based automated tools. By employing precise motion recognition algorithms based on machine intelligence, these sensors accurately capture gait data from the elderly and allows the customization of individualized rehabilitation plans based on their unique needs. They offer a range of visual and auditory stimuli within virtual rehabilitative environments in order to stimulate interest and foster active engagement for enhancing the effectiveness of rehabilitation training in elderly people. Experimental results demonstrate that the rehabilitative effects achieved within virtual environments are comparable to real-world settings and exhibit successful outcomes. With the aid of this technology, adjustable lower limb exoskeleton medical equipment is designed to accommodate individual differences and rehabilitation progress. By seamlessly integrating virtual spaces and next generation smart sensors, the proposed research offer a dynamic and tangible rehabilitation solution. PGWBI score, balance ability score, gait improvement, blood pressure, heart rate and statistical metrics such as R2 score, mean value and standard deviation and confidence intervals are considered for performance evaluation of the proposed rehabilitation monitoring and analysis method. The proposed research is also addressing the challenges posed by an aging society while aligning with the goals of the information and digital era. The research findings hold significant theoretical and practical value in advancing rehabilitation practices.

Research on interactive force acquisition and active–passive mode determination of rehabilitation robot

Lower limb muscle strength is an important indicator of the effectiveness of rehabilitation training for patients with stroke and hemiplegia. When patients are trained with a lower limb rehabilitation robot, real-time human–machine interaction force acquisition can not only grasp the muscle strength of patients' lower limbs in time but also serve as an important reference for active/passive training mode. To accurately collect the human–machine interaction force to identify the patient's movement intention and determine the active/passive mode, a method is proposed to collect the human–machine interaction force based on a resistive strain gauge leg pressure sensor. The method can be applied to a variety of rehabilitation platforms to collect human–machine interaction force quickly and accurately and to identify and classify behaviors through probabilistic neural networks to determine patients' active/passive training patterns. By analyzing the active/passive walking of rehabilitation patients, the motor status of patients in that period is derived. The validity and correctness of the designed sensor and determination method were verified.

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

Retracted: Effect of Rehabilitation Training Based on Automatic Extraction Algorithm on Knee Anterior Cruciate Ligament Injury Caused by Exercise.

[This retracts the article DOI: 10.1155/2022/8304071.].

Ultra-Tough Waterborne Polyurethane-Based Graft-Copolymerized Piezoresistive Composite Designed for Rehabilitation Training Monitoring Pressure Sensors.

Effective training is crucial for patients who need rehabilitation for achieving optimal recovery and reducing complications. Herein, a wireless rehabilitation training monitoring band with a highly sensitive pressure sensor is proposed and designed. It utilizes polyaniline@waterborne polyurethane (PANI@WPU) as a piezoresistive composite material, which is prepared via the in situ grafting polymerization of PANI on the WPU surface. WPU is designed and synthesized with tunable glass transition temperatures ranging from -60 to 0 °C. Dipentaerythritol (Di-PE) and ureidopyrimidinone (UPy) groups are introduced, endowing the material with good tensile strength (14.2MPa), toughness (62 MJ-1 m-3 ), and great elasticity (low permanent deformation: 2%). Di-PE and UPy enhance the mechanical properties of WPU by increasing the cross-linking density and crystallinity. Combining the toughness of WPU and the high-density microstructure derived by hot embossing technology, the pressure sensor exhibits high sensitivity (168.1 kPa-1 ), fast response time (32ms), and excellent stability (10 000 cycles with 3.5% decay). In addition, the rehabilitation training monitoring band is equipped with a wireless Bluetooth module, which can be easily applied to monitor the rehabilitation training effect of patients using an applet. Therefore, this work has the potential to significantly broaden the application of WPU-based pressure sensors for rehabilitation monitoring.

Application of a specialist nurse-led multidisciplinary team model in the perioperative care of patients undergoing simultaneous pancreas and kidney transplantation: randomized controlled trial.

Simultaneous pancreas and kidney transplantation (SPKT) is an effective treatment option for individuals who suffer from both diabetes mellitus and renal failure. However, experiments exploring nurse-led multidisciplinary team management during the perioperative management of patients undergoing SPKT are currently limited. This study aims to explore the clinical performance of a transplant nurse-led multidisciplinary team (MDT) in the perioperative management of SPKT patients. A total of 218 patients who underwent SPKT were randomly assigned to either a control group (n=116) receiving conventional care or an intervention group (n=102) managed through a transplant nurse-led MDT approach. The incidence of postoperative complications, hospital stay, total hospitalization cost, readmission rate, and postoperative nursing quality were compared between these 2 groups. The intervention and control groups showed no significant differences in age, gender, and body mass index. Compared with the control group, the intervention group had a significantly lower incidence of postoperative pulmonary infection and gastrointestinal (GI) bleeding (27.6% vs. 14.7% and 31.0% vs. 15.7%, respectively, both P<0.05). Compared to the control group, the intervention group had significantly lower hospitalization costs, length of hospital stay, and readmission rate 30 days after discharge (32.98±9.10 vs. 36.78±15.36, 26.47±13.4 vs. 31.03±11.61 and 31.4% vs. 50.0%, respectively, all P<0.05). Additionally, the intervention group had significantly better quality of postoperative nursing care than the control group (11.61±0.69 vs. 9.64±1.42, P<0.01), the availability of infection control and prevention measures (11.74±0.61 vs. 10.53±1.11, P<0.01), the effectiveness of health education (11.73±0.61 vs. 10.41±1.06, P<0.01), the effectiveness of rehabilitation training (11.77±0.54 vs. 10.37±0.96, P<0.01), and the patient satisfaction with nursing care (11.83±0.42 vs. 10.81±1.08, P<0.01). The nurse-led MDT model for transplant patients can reduce complications, shorten hospital stays, and save costs. It also provides clear guidelines for nurses, improving care quality and aiding patient recovery. Chinese Clinical Trial Registry ChiCTR1900026543.

Study on brain mechanism of rehabilitation training of articulation disorder in cleft lip and palate patients based on functional magnetic resonance imaging

The cleft lip and palate (CLP) is one of the most common craniofacial malformations in humans. We collected functional magnetic resonance data of 23 CLP patients before rehabilitation training (Bclp) and 23 CLP patients after rehabilitation training (Aclp), who were performing Chinese character pronunciation tasks, and performed brain activation analysis to explore the changes of brain mechanism in CLP patients after articulation disorder rehabilitation training. The study found that Aclp group had significant activation in the motor cortex, Broca area, Wernicke area and cerebellum. While the Bclp group had weak activation in the motor cortex with a small activation range. By comparing the differences and co-activated brain regions between the two groups, we found that rehabilitation training increased the activity level of negatively activated brain areas (cerebellum, left motor area, Wernicke area, etc.) to a positive level. At the same time, the activity level of weakly activated brain areas (right motor area, Broca area, etc.) was also increased. Rehabilitation training promoted the activity level of articulation-related brain regions. So that the activation intensity of articulation-related brain regions can be used as a quantifiable objective evaluation index to evaluate the effect of rehabilitation training, which is of great significance for the formulation of rehabilitation training programs.

The identification of interacting brain networks during robot-assisted training with multimodal stimulation

Objective. Robot-assisted rehabilitation training is an effective way to assist rehabilitation therapy. So far, various robotic devices have been developed for automatic training of central nervous system following injury. Multimodal stimulation such as visual and auditory stimulus and even virtual reality technology were usually introduced in these robotic devices to improve the effect of rehabilitation training. This may need to be explained from a neurological perspective, but there are few relevant studies. Approach. In this study, ten participants performed right arm rehabilitation training tasks using an upper limb rehabilitation robotic device. The tasks were completed under four different feedback conditions including multiple combinations of visual and auditory components: auditory feedback; visual feedback; visual and auditory feedback (VAF); non-feedback. The functional near-infrared spectroscopy devices record blood oxygen signals in bilateral motor, visual and auditory areas. Using hemoglobin concentration as an indicator of cortical activation, the effective connectivity of these regions was then calculated through Granger causality. Main results. We found that overall stronger activation and effective connectivity between related brain regions were associated with VAF. When participants completed the training task without VAF, the trends in activation and connectivity were diminished. Significance. This study revealed cerebral cortex activation and interacting networks of brain regions in robot-assisted rehabilitation training with multimodal stimulation, which is expected to provide indicators for further evaluation of the effect of rehabilitation training, and promote further exploration of the interaction network in the brain during a variety of external stimuli, and to explore the best sensory combination.

Retracted: Characteristics and Rehabilitation Training Effects of Shoulder Joint Dysfunction in Volleyball Players under the Background of Artificial Intelligence.

[This retracts the article DOI: 10.1155/2022/4512795.].

Human gait tracking for rehabilitation exoskeleton: adaptive fractional order sliding mode control approach

To improve the rehabilitation training effect of hemiplegic patients, in this paper, a discrete adaptive fractional order fast terminal sliding mode control approach is proposed for the lower limb exoskeleton system to implement high-precision human gait tracking tasks. Firstly, a discrete dynamic model is established based on the Lagrange system discretization criterion for the lower limb exoskeleton robot. Then, in order to design a discrete adaptive fractional order fast terminal sliding mode controller, the Grünwald–Letnikov fractional order operator is introduced to combine with fast terminal attractor to construct a fractional order fast terminal sliding surface. An adaptive parameter adjustment strategy is proposed for the reaching law of sliding mode control, which drives the sliding mode to the stable region dynamically. Moreover, the stability of the control system is proved in the sense of Lyapunov, and the guidelines for selecting the control parameters are given. Finally, the simulations are tested on the MATLAB-Opensim co-simulation platform. Compared with the conventional discrete sliding mode control and discrete fast terminal sliding mode control, the results verify the superiority of the proposed method in improving lower limb rehabilitation training.

Rigid-Body Guidance Synthesis of Noncircular Gear-Five-Bar Mechanisms and Its Application in a Knee Joint Rehabilitation Device

At present, the research objects of rigid-body guidance synthesis are mostly limited to pure linkages, and there is little research on the combined mechanisms of gears or cams and linkages. In order to expand the research objects of rigid-body guidance and improve the kinematic mapping theory, this paper proposes a rigid-body guidance method of noncircular gear-five-bar combined mechanisms. A noncircular gear-five-bar mechanism can be regarded as a combination of a 2R (two revolute joints) open chain, a 3R (three revolute joints) open chain and a pair of noncircular gears. Firstly, the circle point curves and circle center point curves of the 2R and 3R open chains are obtained by using kinematic mapping, and they are formed into a double crank five-bar linkage. Secondly, the B-spline curve is used to fit the rotation angle relationship of the gear pair to obtain the pitch curves of noncircular gears. Finally, aiming at correcting patients’ abnormal gait, a noncircular gear-five-bar exoskeleton knee joint rehabilitation device is designed based on four task poses. The prototype is developed and the wear test is carried out. The test results verify the correctness of the rigid-body guidance synthesis method and the effectiveness of rehabilitation training.

Humanoid control of lower limb exoskeleton robot based on human gait data with sliding mode neural network

Lower limb exoskeleton robots offer an effective treatment for patients with lower extremity dysfunction. In order to improve the rehabilitation training effect based on the human motion mechanism, this paper proposes a humanoid sliding mode neural network controller based on the human gait. A humanoid model is constructed based on the human mechanism, and the parameterised gait trajectory is used as target to design the humanoid control system for robots. Considering the imprecision of the robot dynamics model, the neural network is adopted to compensate for the uncertain part of the model and improve the model accuracy. Moreover, the sliding mode control in the system improves the response speed, tracking performance, and stability of the control system. The Lyapunov stability analysis proves the stability of the control system theoretically. Meanwhile, an evaluation method using the similarity function is improved based on joint angle, velocity, and acceleration to evaluate the comfort of humans in rehabilitation training more reasonably. Finally, to verify the effectiveness of the proposed method, simulations are carried out based on experimental data. The results show that the control system could accurately track the target trajectory, of which the robot is highly similar to the human.

Observation on Effect of Rehabilitation Training on Spinal Cord Injury with Neurogenic Bladder Dysfunction

Observation on Effect of Rehabilitation Training on Spinal Cord Injury with Neurogenic Bladder Dysfunction

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed. A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (IMU), fuzzy adaptive proportion differentiation (PD) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (sEMG) of the affected limb, and power compensation of the outer ring of the affected limb. The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb. The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive PD controller in the variable domain was 0.21 ± 1.35 ° . The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb. The calculated tracking error of the healthy limb trajectory was 0.39 ± 1.45 ° . It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.