Field Of Rehabilitation Medicine Research Articles

Upper and Lower Limb Training Evaluation System Based on Virtual Reality Technology

Upper and lower limb rehabilitation training is essential for restoring patients’ physical movement ability and enhancing muscle strength and coordination. However, traditional rehabilitation training methods have limitations, such as high costs, low patient participation, and lack of real-time feedback. The purpose of this study is to design and implement a rehabilitation training evaluation system based on virtual reality to improve the quality of patients’ rehabilitation training. This paper proposes an upper and lower limb rehabilitation training evaluation system based on virtual reality technology, aiming to solve the problems existing in traditional rehabilitation training. The system provides patients with an immersive and interactive rehabilitation training environment through virtual reality technology, aiming to improve patients’ participation and rehabilitation effects. This study used Kinect 2.0 and Leap Motion sensors to capture patients’ motion data and transmit them to virtual training scenes. The system designed multiple virtual scenes specifically for different upper and lower limbs, with a focus on hand function training. Through these scenes, patients can perform various movement training, and the system will provide real-time feedback based on the accuracy of the patient’s movements. The experimental results show that patients using the system show higher participation and better rehabilitation training effects. Compared with patients receiving traditional rehabilitation training, patients using the virtual reality system have significantly improved movement accuracy and training participation. The virtual reality rehabilitation training evaluation system developed in this study improves the quality of patients’ rehabilitation and provides personalized treatment information to medical personnel through data collection and analysis, promoting the systematization and personalization of rehabilitation training. This system is innovative and has broad application potential in the field of rehabilitation medicine.

Expert consensus on the application of big data on rehabilitation medicine (2024 edition)

With the development and progress of technology, the transformation of biomedical models, and the application of digital technology, the field of rehabilitation medicine has also generated massive and diversified big data. However, the current level of utilization of rehabilitation medicine is relatively low. Empowering the development of rehabilitation medicine through big data is crucial. In order to better promote the standardized development of rehabilitation big data in China, the Rehabilitation Big Data Working Committee of the Chinese Rehabilitation Medical Association organized an expert plan on the concept, significance, and necessity of rehabilitation big data; Rehabilitation big data source; Priority areas for rehabilitation big data application research; Basic research methods for rehabilitation big data; The standards and principles for establishing a rehabilitation database; Consensus has been developed on six aspects of clinical application scenarios for rehabilitation big data, providing reference for healthcare workers and researchers related to rehabilitation big data.

The Application and Development of Music Therapy in Rehabilitation Medicine

Music therapy, as an ancient and continually evolving therapeutic approach, has demonstrated unique effects and extensive potential applications in the field of rehabilitation medicine. This paper first explores the physiological and psychological impacts of music and theoretical models of its therapeutic mechanisms. It further details the specific applications of music therapy in neurological rehabilitation, motor function recovery, psychological and emotional adjustment, and chronic disease and pain management. The article also investigates the prospects of integrating music therapy with modern technologies such as virtual reality and artificial intelligence and emphasizes the importance of interdisciplinary collaboration and policy support in advancing this field. Through comprehensive analysis, the paper identifies future development directions and research needs for music therapy in rehabilitation medicine.

Degradable, biocompatible, and flexible capacitive pressure sensor for intelligent gait recognition and rehabilitation training

Gait recognition and rehabilitation training based on pressure sensing technology have an important role in the field of rehabilitation medicine. However, existing pressure sensors that are used in this process are often fabricated from non-degradable or non-biocompatible source materials, which limits their application in long-term sustainable use. In this study, we propose a high-performance, degradable, biocompatible and flexible capacitive pressure sensor. Its dielectric layer is prepared using a polyvinyl alcohol/sodium alginate (PVA/SA) electrospun nanofiber membrane (ENM), while its upper and lower electrodes are fabricated from PVA@graphene screen-printed ENMs. Experimental results demonstrate the sensor's exceptional pressure sensing performance, characterized by its high sensitivity (0.54 kPa−1 at 0–10 kPa), low detection limit (∼2.2 Pa), rapid response time (26.6/29.9 ms), and excellent stability (5000 cycles). Furthermore, it exhibits outstanding biocompatibility, achieving a cell viability assay survival rate exceeding 97%, and remarkable degradability, completely degrading within one hour. A gait recognition and rehabilitation training system has been developed by integrating the sensors with a deep learning algorithm and wireless transmission technology. The system can effectively monitor and record dynamic parameters of lower limb swing during walking, exhibiting an impressive gait recognition accuracy of 98% over five normal and abnormal gaits. Moreover, it can guide patients in performing diverse rehabilitation actions as well as monitor their progress through action count recording. This innovative solution offers a sustainable approach to gait recognition and rehabilitation training within the field of rehabilitation medicine, thereby opening up new avenues for patient recovery.

A decision-making system based on case-based reasoning for predicting stroke rehabilitation demands in heterogeneous information environment

Recently, the increasing number of stroke survivors has created a greater demand for rehabilitation services. How to predict rehabilitation demands is important for policy makers in various countries. The demand prediction is essentially a multi-attribute decision-making problem. The diverse forms of information or data are often neglected or simplified in previous studies of decision-making problems. In this paper, heterogeneous multi-attributes including crisp numbers, interval numbers, and linguistic variables are defined to represent decision-relevant information concerning the patient's condition in cases when applied to real-life scenarios. Moreover, a deviation minimization optimization model combining CRITIC method and H2TLWMSM operator is proposed to assign the attribute weights with dual information. The WHMACBR system can be used to predict stroke rehabilitation demands for making the diagnosis and treatment decisions. The proposed decision-making system based on the CBR approach simulates the operations of human memory and reasoning, thus assisting physicians in addressing the ever-increasing demand for rehabilitation services of stroke survivors. Finally, the effectiveness and applicability of the case retrieval process of the presented CBR model are demonstrated through a stroke rehabilitation case study (top 3 cases ranked by similarity to the target case are C6, C2, and C3) and comparison analysis with relevant existing CBR models. This indicates that the proposed decision-making system, by considering heterogeneous multi-attributes and their weights, is capable of providing more comprehensive and rational decision-related information in the field of rehabilitation medicine.

The applicability of noncontact sensors in the field of rehabilitation medicine.

A noncontact sensor field is an innovative device that can detect, measure, or monitor physical properties or conditions without direct physical contact with the subject or object under examination. These sensors use a variety of methods, including electromagnetic, optical, and acoustic technique, to collect information about the target without physical interaction. Noncontact sensors find wide-ranging applications in various fields such as manufacturing, robotics, automobiles, security, environmental monitoring, space industry, agriculture, and entertainment. In particular, they are used in the medical field, where they provide continuous monitoring of patient conditions and offer opportunities in rehabilitation medicine. This article introduces the potential of noncontact sensors in the field of rehabilitation medicine.

EEG-Based Multi-Frequency Multilayer Network for Exploring the Brain State Evolution Underlying Motor Imagery

Motor imagery (MI)-based brain-computer interface (BCI) provides a promising solution for the limb rehabilitation of stroke patients. However, due to the inadequate cognition of brain state during MI, the further application of BCI in the field of rehabilitation medicine is limited. In this paper, we focus on the MI tasks of four commonly used upper limb rehabilitation actions. And a complex network based topological analysis method is developed to characterize the brain state evolution mechanism over time. In detail, we first carry out the MI experiments and acquire the EEG signals associated with imagining left/right fist clenching and left/right wrist dorsiflexion. Then a multi-frequency multilayer (MFML) network is constructed, which maps the topological features of four MI-related frequency bands. The brain electrodes are defined as network nodes, and each frequency band corresponds to one single layer. Global clustering coefficient ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GC</i> ) is introduced to conduct quantitative topological analysis. Experimental results show that the topological structures of brain tend to be more energy consuming over time, suggesting that the cognitive demands of subjects gradually increase during MI. At the same time, it is found that continuous MI experiments improve the mind control ability of subjects, and the distinguishability of MI signals also gradually improves. Our research quantitatively explores the evolution of brain states behind MI, which is expected to guide the follow-up application of MI-BCI in clinical rehabilitation medicine.

A BERT-Span model for Chinese named entity recognition in rehabilitation medicine.

Due to various factors such as the increasing aging of the population and the upgrading of people's health consumption needs, the demand group for rehabilitation medical care is expanding. Currently, China's rehabilitation medical care encounters several challenges, such as inadequate awareness and a scarcity of skilled professionals. Enhancing public awareness about rehabilitation and improving the quality of rehabilitation services are particularly crucial. Named entity recognition is an essential first step in information processing as it enables the automated extraction of rehabilitation medical entities. These entities play a crucial role in subsequent tasks, including information decision systems and the construction of medical knowledge graphs. In order to accomplish this objective, we construct the BERT-Span model to complete the Chinese rehabilitation medicine named entity recognition task. First, we collect rehabilitation information from multiple sources to build a corpus in the field of rehabilitation medicine, and fine-tune Bidirectional Encoder Representation from Transformers (BERT) with the rehabilitation medicine corpus. For the rehabilitation medicine corpus, we use BERT to extract the feature vectors of rehabilitation medicine entities in the text, and use the span model to complete the annotation of rehabilitation medicine entities. Compared to existing baseline models, our model achieved the highest F1 value for the named entity recognition task in the rehabilitation medicine corpus. The experimental results demonstrate that our method outperforms in recognizing both long medical entities and nested medical entities in rehabilitation medical texts. The BERT-Span model can effectively identify and extract entity knowledge in the field of rehabilitation medicine in China, which supports the construction of the knowledge graph of rehabilitation medicine and the development of the decision-making system of rehabilitation medicine.

Neural mechanisms of temporal and rhythmic structure processing in non-musicians.

Music is increasingly being used as a therapeutic tool in the field of rehabilitation medicine and psychophysiology. One of the main key components of music is its temporal organization. The characteristics of neurocognitive processes during music perception of meter in different tempo variations technique have been studied by using the event-related potentials technique. The study involved 20 volunteers (6 men, the median age of the participants was 23 years). The participants were asked to listen to 4 experimental series that differed in tempo (fast vs. slow) and meter (duple vs. triple). Each series consisted of 625 audio stimuli, 85% of which were organized with a standard metric structure (standard stimulus) while 15% included unexpected accents (deviant stimulus). The results revealed that the type of metric structure influences the detection of the change in stimuli. The analysis showed that the N200 wave occurred significantly faster for stimuli with duple meter and fast tempo and was the slowest for stimuli with triple meter and fast pace.

Bioinspired skin towards next-generation rehabilitation medicine.

The rapid progress of interdisciplinary researches from materials science, biotechnologies, biomedical engineering, and medicine, have resulted in the emerging of bioinspired skins for various fantasticating applications. Bioinspired skin is highly promising in the application of rehabilitation medicine owing to their advantages, including personalization, excellent biocompatibility, multi-functionality, easy maintainability and wearability, and mass production. Therefore, this review presents the recent progress of bioinspired skin towards next-generation rehabilitation medicine. The classification is first briefly introduced. Then, various applications of bioinspired skins in the field of rehabilitation medicine at home and abroad are discussed in detail. Last, we provide the challenges we are facing now, and propose the next research directions.

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Health monitoring by functional indicators with the help of sensors at the phase of patient rehabilitation in today's conditions

Nowadays, people's need for fast and effective rehabilitation processes is growing significantly. Sensory devices are available for people with functional disabilities, which are used for rehabilitation to help a person's health and return to an appropriate standard of living. Scientists in the field of rehabilitation medicine are actively studying the method of remote monitoring of the physiological indicators of the human body. The last decade has been marked by the intensive development of research in the field of sensor devices. For example, mechanical, robotic systems and exoskeletons, which enable people with limited physical capabilities to move their bodies, occupy an important place among the technical means for restoring the condition of the human locomotor system. Despite the existence of various technical systems and means for rehabilitation after injuries and diseases of the spine and lower limbs, the latest modern exoskeletons of various types have not yet been used.

Effects of Electrical Stimulation on the Signal Transduction-Related Proteins, c-Src and Focal Adhesion Kinase, in Fibroblasts.

Electrical stimulation of the skin and muscles, e.g., in the fields of rehabilitation medicine and acupuncture, is known to locally increase blood flow and metabolism, and thus have beneficial health effects. However, little is known about the changes in cellular morphology or regulation of the localization of specific proteins in response to electrical stimuli. The present study was performed to examine the effects of electrical stimulation on the cytoskeletal system of cultured fibroblasts. Following application of electrical stimulation to cultured fibroblastic cells for a period of about 2 h, the stress fibers in the cells became thicker and the cells showed a contracted appearance. Cells were subjected to periodic electrical stimulation for 0 (unstimulated control), 2, 5, or 20 h. The stress fibers showed an increase in thickness within 2 h, and became gradually thicker until 20 h. In addition, the focal adhesions and stress fibers were enlarged after 2 h of continuous stimulation, and both stress fibers and focal adhesions became larger and thicker after 20 h of periodic stimulation. Cells showed increased staining of focal adhesions with anti-phosphotyrosine antibody (PY-20) after electrical stimulation. Cells also showed increased staining of tyrosine-phosphorylated focal adhesion kinase (FAK) (pY397) and tyrosine-phosphorylated c-Src (pY418), indicating that electrical stimulation affected signal transduction-related proteins.

Topical issues of rehabilitation of patients with osteoporotic vertebral fractures

The literature review analyzed 20 Russian and 69 foreign publications on the rehabilitation of elderly patients with osteoporotic vertebral fractures. The article deals in detail with the prevalence, medical and social significance of pathological osteoporotic fractures, including vertebral deformities. The data confirming the importance of osteoporosis for physical and rehabilitation medicine specialists are presented. Changes in the quality of life, functional and gate disorders associated with osteoporotic vertebral fractures are described. Based on the available literature data, the principles of rehabilitation of patients with osteoporotic vertebral compression fractures are formulated, including the effectiveness of various methods of physical therapy, mechanotherapy and apparatus physiotherapy. From the standpoint of evidence-based medicine, the role of orthotics in the complex rehabilitation of such patients is described. Based on the analysis of literature data, it was concluded that the problem of osteoporosis is relevant for physicians working in the field of rehabilitation medicine; osteoporotic vertebral fractures are characterized by a high prevalence over the age of 50 years and are associated with a decrease in the quality of life, motor and functional limitations, and an increased risk of death, and well-planned medical rehabilitation programs including physical exercises, physiotherapy and orthotics can significantly improve patient functionality.

Conducting a Systematic Review and Meta-analysis in Rehabilitation.

Systematic reviews are reviews of the literature using a step-by-step approach in a systematic way. Meta-analyses are systematic reviews that use statistical methods to combine the included studies to generate an effect estimate. In this article, we summarize 10 steps for conducting systematic reviews and meta-analyses in the field of rehabilitation medicine: protocol, review team and funding, objectives and research question, literature search, study selection, risk of bias, data extraction, data analysis, reporting of results and conclusions, and publication and dissemination. There are currently 64,958 trials that contain the word "rehabilitation" in CENTRAL (the database of clinical trials in the Cochrane Library), only 1246 reviews, and 237 protocols. There is an urgent need for rehabilitation physicians to engage and conduct systematic reviews and meta-analysis of a variety of rehabilitation interventions. Systematic reviews have become the foundation of clinical practice guidelines, health technology assessments, formulary inclusion decisions and to guide funding additional research in that area.

Application of Robot in the Field of Rehabilitation Medicine

Application of Robot in the Field of Rehabilitation Medicine

Design and Application of Electronic Rehabilitation Medical Record (ERMR) Sharing Scheme Based on Blockchain Technology.

As the value of blockchain has been widely recognized, more and more industries are proposing their blockchain solutions, including the rehabilitation medical industry. Blockchain can play a powerful role in the field of rehabilitation medicine, bringing a new research idea to the management of rehabilitation medical data. The electronic rehabilitation medical record (ERMR) contains rich data dimensions, which can provide comprehensive and accurate information for assessing the health of patients, thereby enhancing the effect of rehabilitation treatment. This paper analyzed the data characteristics of ERMR and the application requirements of blockchain in rehabilitation medicine. Based on the basic principles of blockchain, the technical advantages of blockchain used in ERMR sharing have been studied. In addition, this paper designed a blockchain-based ERMR sharing scheme in detail, using the specific technologies of blockchain such as hybrid P2P network, block-chain data structure, asymmetric encryption algorithm, digital signature, and Raft consensus algorithm to achieve distributed storage, data security, privacy protection, data consistency, data traceability, and data ownership in the process of ERMR sharing. The research results of this paper have important practical significance for realizing the safe and efficient sharing of ERMR, and can provide important technical references for the management of rehabilitation medical data with broad application prospects

Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements.

Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on upper-limb motions is lost in the process of feature extraction for sEMG signals, and there exists the fact that only a small variety of rehabilitation movements can be distinguished, and the classification accuracy is easily affected. To solve these dilemmas, first, a multiscale time–frequency information fusion representation method (MTFIFR) is proposed to obtain the time–frequency features of multichannel sEMG signals. Then, this paper designs the multiple feature fusion network (MFFN), which aims at strengthening the ability of feature extraction. Finally, a deep belief network (DBN) was introduced as the classification model of the MFFN to boost the generalization performance for more types of upper-limb movements. In the experiments, 12 kinds of upper-limb rehabilitation actions were recognized utilizing four sEMG sensors. The maximum identification accuracy was 86.10% and the average classification accuracy of the proposed MFFN was 73.49%, indicating that the time–frequency representation approach combined with the MFFN is superior to the traditional machine learning and convolutional neural network.

Qualification in the field of osteoporosis treatment in doctors working in the field of physical and rehabilitation medicine

Introduction. The special rehabilitation programs are often required for patients with osteoporosis (OP). So physicians working in the field of physical and rehabilitation medicine should be well-versed in this problem.Aims. To study the awareness of doctors of rehabilitation medicine in the field of diagnosis and treatment of osteoporosis and their activity in providing medical care to patients with OP.Materials and methods. A questionnaire survey of 157 doctors (M-34, F-123) of 8 medical specialties working in 27 specialized medical institutions on the profile of “medical rehabilitation” was carried out. The questionnaire for doctors consisted of 21 items of special questions.Results and discussion. In the sample of rehabilitation doctors, 90.45% of the interviewed doctors believed that the problem of OP is relevant for their clinical activities, 100% of the respondents indicated that the presence of OP significantly affects the rehabilitation prognosis and 95.54% - on the degree of effectiveness of medical rehabilitation. According to the respondents, patients with OP make up on average 30.0% [20.0; 50.0] (0-90) of the total flow of patients. Endocrinologists (all surveyed doctors - 100%), obstetricians - gynecologists (66.67%) and therapists (60%) are mainly involved in the treatment of OP. Most often, specialists from rehabilitation institutions recommend zoledronic acid (23.57% of doctors indicated in this aspect), preparations of vitamin D (23.57%) and calcium (14.65%), various methods of physical therapy (14.65%) and parenteral form of ibandronic acid (12.74%).Conclusions. The presence of OP significantly affects the rehabilitation prognosis and the effectiveness of medical rehabilitation. 23.57% of specialists in the field of rehabilitation medicine prescribe treatment for OP to patients, giving preference to parenteral bisphosphonates, vitamin D, calcium and physical exercises.

Modelling a project for the development of a neurocomputer interface based on EMG

Currently, around the world, various neurocomputer interfaces (NCI) are being actively developed for controlling prostheses, exoskeletons, robots, for interacting with virtual reality, for rehabilitation medicine, and even for professional sports. Development and research in the field of rehabilitation medicine will help partially and fully restore the functional capabilities of people with amputation or congenital defects of the limbs. Thus, the purpose of this work is to review the methods of recording the control signals of bionic devices based on the analysis of physiological and nervous processes in humans and modeling of a project for developing a neurocomputer interface based on EMG using flexible methods of project management. In this regard, first of all, it is interesting to develop a sensor and controller for further use in systems of robotic prostheses. At the moment, the design of the project using flexible methods of project management has been made and the first prototype with five channels for connecting sensors and a USB interface for communication with a personal computer has been manufactured. Also was made the first prototype with 5 channels for connecting sensors and a USB interface for communication with a personal computer.