Field Of Medical Rehabilitation Research Articles

How IT technologies are transforming the world of sports for people with disabilities

IT technologies in medical rehabilitation play a key role in opening new perspectives for people with physical disabilities. Sensor devices, virtual reality and specialized software solutions make it possible to create innovative training programs adapted for people with disabilities. Thanks to biometric sensors and monitoring systems, doctors and trainers can more effectively track progress and adjust treatment plans. IT technologies are becoming a powerful tool in the field of medical rehabilitation, opening up new opportunities for a full and active life.

Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion.

Brain-computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model's input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model's overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models.

Assessment of Integrative Therapeutic Methods for Improving the Quality of Life and Functioning in Cancer Patients-A Systematic Review.

Background: Cancer rehabilitation represents a series of measures adopted for the recovery of psychological, emotional, social, and financial functioning in the case of cancer patients. The purpose of this study is to identify the main elements of therapeutic management in the field of medical rehabilitation, as well as integrative, complementary medicine and holistic approaches that can be performed on the oncological patient. Methods: This systematic literature review follows the methodology outlined in the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" ("PRISMA") statement, which is an internationally recognized and widely accepted standard. Results: Active rehabilitative therapies offer therapeutic options for improving the functioning and quality of life of oncological patients; these therapies comprehensively address both the physical and psychological aspects of the disease. This review also includes the latest novelties and nanotechnologies applied in oncological rehabilitation, for example, drugs (or supplements) inspired by nature. Conclusions: Physical and rehabilitation medicine, mostly using stimulating therapeutic methods, was recently added to the list of contraindications in the management of oncological patients, both as an approach to the pathological concept itself and as an approach to the main clinical consequences and functional aspects of oncological therapies. Integrative, complementary medicine presents an important therapeutic resource in the case of oncological patients. Advanced studies are needed in the future to further ascertain the role of these therapies.

Training program for primary care physicians in medical rehabilitation at the Pacifi c Medical University

This article describes the experience of training primary care physicians in the field of medical rehabilitation. The existing procedure for the arrangement of medical rehabilitation prescribes its rules and order of implementation in medical primary health care organizations both in outpatient and day patient settings. The Pacific State Medical University (PSMU) runs training programs for primary care physicians within the 31.05.01 educational program “General Medicine”, direction “02 Healthcare”. In accordance with the curriculum, a training program “Medical Rehabilitation” that includes both lectures and training sessions was developed. This program is aimed at forming the necessary competencies of future primary care physicians in implementing and monitoring the effectiveness of the patient’s medical rehabilitation. Training is carried out at the facilities of the PSMU Institute of Clinical Neurology and Rehabilitation Medicine by specialists having experience in medical rehabilitation and having completed the program “Modern aspects of specialist training in the field of physical and rehabilitation medicine”.

Humidity Adaptive Antifreeze Hydrogel Sensor for Intelligent Control and Human-Computer Interaction.

Conductive hydrogels have emerged as ideal candidate materials for strain sensors due to their signal transduction capability and tissue-like flexibility, resembling human tissues. However, due to the presence of water molecules, hydrogels can experience dehydration and low-temperature freezing, which greatly limits the application scope as sensors. In this study, an ionic co-hybrid hydrogel called PBLL is proposed, which utilizes the amphoteric ion betaine hydrochloride (BH) in conjunction with hydrated lithium chloride (LiCl) thereby achieving the function of humidity adaptive. PBLL hydrogel retains water at low humidity (<50%) and absorbs water from air at high humidity (>50%) over the 17 days of testing. Remarkably, the PBLL hydrogel also exhibits strong anti-freezing properties (-80°C), high conductivity (8.18 Sm-1 at room temperature, 1.9 Sm-1 at -80°C), high gauge factor (GF approaching 5.1). Additionally, PBLL hydrogels exhibit strong inhibitory effects against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), as well as biocompatibility. By synergistically integrating PBLL hydrogel with wireless transmission and Internet of Things (IoT) technologies, this study has accomplished real-time human-computer interaction systems for sports training and rehabilitation evaluation. PBLL hydrogel exhibits significant potential in the fields of medical rehabilitation, artificial intelligence (AI), and the Internet of Things (IoT).

Types and role of public diplomacy in the implementation of the foreign policy of the state and its special features in the field of medical rehabilitation

Types and role of public diplomacy in the implementation of the foreign policy of the state and its special features in the field of medical rehabilitation

A novel recognition and classification approach for motor imagery based on spatio-temporal features.

Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity.

Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance its development has become increasingly necessary to enable efficient detection and response to a wide range of movements. Current recognition methods rely on calculating changes in joint distance to classify activity patterns. Therefore, a different approach is required to identify the direction of movement to distinguish activities exhibiting similar joint distance changes but differing motion directions, such as sitting and standing. The research conducted in this study focused on determining the direction of movement using an innovative joint angle shift approach. By analyzing the joint angle shift value between specific joints and reference points in the sequence of activity frames, the research enabled the detection of variations in activity direction. The joint angle shift method was combined with a Deep Convolutional Neural Network (DCNN) model to classify 3D datasets encompassing spatial-temporal information from RGB-D video image data. Model performance was evaluated using the confusion matrix. The results show that the model successfully classified nine activities in the Florence 3D Actions dataset, including sitting and standing, obtaining an accuracy of (96.72 ± 0.83)%. In addition, to evaluate its robustness, this model was tested on the UTKinect Action3D dataset, obtaining an accuracy of 97.44%, proving that state-of-the-art performance has been achieved.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches.

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

Актуальные вопросы организации профессиональной переподготовки специалистов по физической реабилитации (кинезиоспециалистов) в условиях новой модели медицинской реабилитации.

The system of medical rehabilitation in Russia undergoes significant transformation which is reflected in the Federal Program «Optimal for health restoration medical rehabilitation in the Russian Federation». In such situation the issues of personnel training in the field of medical rehabilitation are becoming extremely relevant. The difficulties that arise along this path are associated with the interdisciplinarity of medical rehabilitation and the necessity to train specialists with medical education (doctors and nurses) as well as with higher non-medical education: physical therapists, ergotherapists, medical psychologists and medical speech therapists (according to the Order No. 788n of 31.07.2020). These allied health specialties are new for Russia but they are responsible for the main practical activity in the implementation of an individual medical rehabilitation plan. The difficulties in training non-medical personnel in rehabilitation largely arise from the requirement for them to have both medical and pedagogical competencies and to have the ability to work independently as well as in a rehabilitation team. This article summarizes the three-year experience of training specialists in medical rehabilitation with higher non-medical education at the National Research Nizhny Novgorod State University named after N. I. Lobachevsky. The article presents the main issues of the professional retraining program «Physical rehabilitation (physical therapy)» with a duration of 1020 hours, defines professional competencies, difficult issues of training, formation of practical skills and assessment of professional competencies in this category of students.

Aloe Inspired Special Structure Hydrogel Pressure Sensor for Real‐Time Human‐Computer Interaction and Muscle Rehabilitation System

AbstractIn recent years, significant progress has been made in the research and development of conventional hydrogel sensors. However, the development of hydrogels with special structures, achieved through specific designs or fabrication strategies, remains relatively scarce. Inspired by aloe, a specially structured hydrogel (named Skin‐Polyvinyl alcohol‐Polyaniline‐AgNWs, S‐PPA) is successfully prepared with skin. Innovatively utilizing the hydrogen bonding interaction between ions and water molecules, the surface of the hydrogel is treated to create a protective skin. The S‐PPA hydrogel with a protective skin demonstrates strong resistance to damage (with a tensile strength of 5 MPa, &gt;11 times higher compared to hydrogel without skin) and exhibits dual conductivity (0.8 S m−1 for the inner skin and 0.33 S m−1 for the outer skin). In addition, the S‐PPA hydrogel also possesses water retention capabilities, antibacterial properties (89.4% inhibition rate against Staphylococcus aureus (S. aureus)), and minimal corrosion to metal electrodes. Based on the S‐PPA hydrogel and combined with wireless Bluetooth technology and Python programming, intelligent applications are developed such as multi‐gradient intelligent control and finger muscle condition evaluation, achieving real‐time human‐ computer interaction (HCI). The intelligent pressure‐sensitive hydrogel proposed by this study shows great potential in the fields of medical rehabilitation, artificial intelligence, and the Internet of Things.

Rehabilitation exoskeleton torque control based on PSO-RBFNN optimization.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.

Control strategy based on improved fuzzy algorithm for energy control of wrist rehabilitation robot

With the complex changes in social and demographic structure, the problem of aging is becoming increasingly serious, and the demand for equipment in the field of medical rehabilitation is also increasing. Given the above situation, various countries have proposed using rehabilitation robots to assist patients in treatment, and have achieved certain results. The rehabilitation robot helps the injured area gradually recover autonomous movement through repeated auxiliary actions, thereby achieving the goal of physical rehabilitation training. However, most robot configurations face insufficient energy and power, as well as bulky and inflexible equipment. Based on the above issues, this article adopts an improved fuzzy algorithm control strategy to optimize the energy storage device and applies the energy system to the wrist rehabilitation training robot device. Firstly, a fuzzy adaptive algorithm is added to the traditional energy controller to combine system control with operational status, achieving precise control and dynamic adjustment of energy allocation. The research has added filter optimization design, which improves the accuracy and effectiveness of fuzzy algorithms. Finally, this article establishes an upper limb motion model of the rehabilitation robot and configures relevant hardware systems. At the same time, this article also uses manufacturing materials that meet functional requirements to construct auxiliary equipment. The results indicate that the improved fuzzy algorithm can improve the auxiliary effect in energy control of wrist rehabilitation robots. It has obvious advantages in the process of wrist rehabilitation training.

Wearable Optical Fiber Sensors for Biomechanical Measurement in Medical Rehabilitation: A Review

With the increasing demand for rehabilitation of the elderly population and patients with limb injuries, it is imperative to develop new wearable health monitoring system and assistive rehabilitation equipment. Optical fiber sensing has the advantages of light-weight, anti-electromagnetic interference, higher strain and elastic limit, safety and dependableness. It has been significantly utilized in engineering prospecting, building structure monitoring, and medical and health fields. At present, the application of optical fiber sensing in medical rehabilitation has been developed rapidly, however, the discussion and summary of its key theories and technologies are insufficient. This review details different types of optical fiber sensing mechanisms and devices, as well as the measurement of physical parameters using optical fiber sensors and application examples in the field of medical rehabilitation. This review systematically analyzes the advance of the sensitivity, flexibility, accuracy, and wearability of the optical fiber sensing in the monitoring of human body sign information. This review also discusses the challenges of making a new generation of optical fiber sensors that fit human joints, muscles, and rehabilitation equipment. Finally, this review provides a research direction for the realization of flexible, comfortable, safe, stable monitoring of human biomechanical parameters and real-time precise control of rehabilitation equipment in the future.

The role of clinical psychologist in modern healthcare system

The article analyses current understanding of clinical psychology as a field of professional activity of psychologists in medicine and public health. It reveals the main traditional and new areas of activity of a medical psychologist in clinical and preventive medicine. Main directions and forms of clinical psychologist’ work in healthcare system is reviewed. The role of clinical psychologist as part of a multidisciplinary team is outlined and the main models of multidisciplinary teams in the field of medical rehabilitation are analyzed. We identified the main problems and the goals in this area, which solution is necessary to create an effective system for the provision of medical, psychological and social assistance to the population. The article shows that clinical psychology plays a key role within the multidisciplinary approach to assistance of patients and their relatives in specialized psychiatric or addiction treatment, psychotherapy, as well as in general medical practice. One of the main tasks of clinical psychology is considered humanization and personalization of the entire process of medical care, protection from inconvenient impact of technization of medicine on patient and doctor, narrow specialization of health services. Some of the organizational and legal barriers hindering full integration of medical psychologist into the healthcare system and effective realization of the goals of medical and psychological activities were highlighted. The prospects for the development of clinical psychology in healthcare in the theoretical, methodological, substantive and organizational-legal aspects are determined. It is emphasized that in order to realize the potential of clinical psychology, constant improvement of theory and methodology, the development of effective medical and psychological technologies for diagnosis, correction, and rehabilitation are necessary. And at the same time, it is important to create organizational and legal conditions for the full cooperation of clinical psychologist with specialists in other areas.

A Multi-Label Based Physical Activity Recognition via Cascade Classifier.

Physical activity recognition is a field that infers human activities used in machine learning techniques through wearable devices and embedded inertial sensors of smartphones. It has gained much research significance and promising prospects in the fields of medical rehabilitation and fitness management. Generally, datasets with different wearable sensors and activity labels are used to train machine learning models, and most research has achieved satisfactory performance for these datasets. However, most of the methods are incapable of recognizing the complex physical activity of free living. To address the issue, we propose a cascade classifier structure for sensor-based physical activity recognition from a multi-dimensional perspective, with two types of labels that work together to represent an exact type of activity. This approach employed the cascade classifier structure based on a multi-label system (Cascade Classifier on Multi-label, CCM). The labels reflecting the activity intensity would be classified first. Then, the data flow is divided into the corresponding activity type classifier according to the output of the pre-layer prediction. The dataset of 110 participants has been collected for the experiment on PA recognition. Compared with the typical machine learning algorithms of Random Forest (RF), Sequential Minimal Optimization (SMO) and K Nearest Neighbors (KNN), the proposed method greatly improves the overall recognition accuracy of ten physical activities. The results show that the RF-CCM classifier has achieved 93.94% higher accuracy than the 87.93% obtained from the non-CCM system, which could obtain better generalization performance. The comparison results reveal that the novel CCM system proposed is more effective and stable in physical activity recognition than the conventional classification methods.

Digital twin rehabilitation system based on self-balancing lower limb exoskeleton.

The digital twin concept is the virtual model based on entity design measures, which is used in many enterprises' virtual workshop design models for workshop production scheduling and optimization. However, in the field of medical rehabilitation, the integration of digital twin technology started late compared to traditional industrial manufacturing. Many current digital models are not well suited for information interaction between patients and devices. In order to address the lack of interaction between patients and devices in the field of medical rehabilitation, this paper proposes an automatic gait data control system (AGDCS) for fully actuated lower limb exoskeleton digital twinning. This system improves the integration of digital twinning system with the medical rehabilitation field and analyzes the patient's gait data through simulation experiments. The digital twin system was designed in several steps. Firstly, the upper computer function module was designed and developed according to the rehabilitation treatment needs. After that, the combination of exoskeleton robot and software was carried out, and finally the real rehabilitation treatment environment of patients was simulated through experiments. The proposed system was very reliable in the experimental tests of the host computer and exoskeleton robot. In the upper computer test, the patient specific gait can be generated, and the motion of the exoskeleton robot can be observed in real-time. During the walking test of the exoskeleton robot, the exoskeleton robot completed the specified gait. The result verified the superiority and effectiveness of the digital twin system AGDCS in the field of rehabilitation. The digital twin system proposed in this paper improves the interaction between self-balancing exoskeleton robot and patients, and improves the autonomy and safety of patients in rehabilitation treatment.

The level of awareness of osteoporosis among physicians working in the field of medical rehabilitation and the effectiveness of the educational program

The level of awareness of osteoporosis among physicians working in the field of medical rehabilitation and the effectiveness of the educational program

Performance Comparison of Different Convolutional Neural Network Approaches for Facial Expression Recognition

Abstract: Facial expression is a non-verbal way of communication to express the human state of mind using facial muscles. Happiness, sadness, anger, surprise, disgust, fear, and neutral expressions are widely used in the field of medical rehabilitation, sentiment analysis, counseling, and so on inspiring researchers to develop effective models to classify the expressions effectively. LeNet5, AlexNet, Deep Model, Shallow Model, Deep CNN Model are some commonly used models that have been developed to recognize facial expressions using machine learning and deep learning. In this research, a new convolutional neural network model has been proposed and compared with the existing models. The FER-2013 dataset has been used to evaluate the performance using different metrics to find the efficiency of the models. The proposed model provides comparatively better accuracy than most of the existing models, which is 64.4%. Keywords: Facial Expression, Image Classification, Convolutional Neural Network, Deep Learning, Non-verbal communication.

Personal‐specific gait recognition based on latent orthogonal feature space

Exoskeleton has been applied in the field of medical rehabilitation and assistance. However, there are still some problems in the interaction between human and exoskeleton, such as time delay, the existence of certain constraints on the human body, and the movement in time is hard to follow. A human motion pattern recognition model based on the long short-term memory (LSTM) is proposed, which can recognise the state of the human body. Meanwhile, the orthogonalisation method is integrated to make personal-specific disentangling, and it can effectively improve the generalisation ability of different groups of people, so as to improve the effective follower ability of the exoskeleton. Compared with some other traditional methods, this model has better performance and stronger generalisation ability, which has certain significance in the field of exoskeleton algorithm.