This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” (n = 1). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” ( Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects a person's interaction, communication, and learning skills. Autism was first characterized by Leo Kanner in 1943, who tried to understand the relationship between autism and sociodemographic factors such as social class, sex, maternal education, age, and race . RecentlASD has no single known cause. Given the complexity of the disorder and the fact that symptoms and severity vary, there are probably many causes. Both genetics and environmental factors may play important roles. Many different genes are involved in ASD for genetic reasons, such as Rett syndrome or fragile X syndrome . In addiOn the other hand, environmental factors such as viral infections, medications, or complications during pregnancy play a role in triggering ASD. Also, they may be caused by familial or sociodemographic characteristics, such as race, gender, and age, or they may be behavioral factors implying a person's behavior  . In addiScientific studies, equipment, and test tools contributing to the early ASD diagnosis will also provide excellent support for controlling and treating this disease. At present and after the significant development in computer science and information technology, ML has been used to diagnose and analyze various conditions such as heart disease, lung cancer, diabetes, and COVID-19, which effectively classify and predict various medical fields. In these contexts, diagnosis of ASD used many methods for ML such as k-nearest neighbors (KNN), naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) and AI techniques (deep learning) such as convolutional neural network (CNN) and Recurrent neural network (RNN). Evaluate algorithms by metrics vary from one researcher to another.Several trends have emerged in identifying, diagnosing, and analyzing autism using AI and ML. First, magnetic resonance imaging (MRI) is a cross-sectional image of the brain and a medical imaging method to clarify pathological changes in living tissues , 26. MRIIn conclusion, each diagnostic direction has limitations regarding the utilized diagnosis features. Each approach alone does not provide an accurate diagnosis procedure. These directions could not have been realized the proper diagnosis process using specific features and neglecting others. Yet no new path brings them together in an integrated diagnosis by AI and ML methods. Therefore, the question arises about “What is the expected research direction to improve the diagnosis of ASD?”When the integration between these paths is introduced, it strengthens and supports the diagnostic process. According to the literature, there is no integration between these paths of medical features and sociodemographic features in the context of AI techniques, ML, and deep learning algorithms, which motivates this study. This aspect reinforces the importance of medical characteristics, especially their integration with sociodemographic factors. Although, in the literature, there is no integration of medical features with sociodemographic features. Medical aspects give accurate results such as examining vitamins  that cause affect autistic children. As was not covered based on medical and laboratory due to the lack of laboratory or medical tests (such as a blood test or others), ASD diagnosis may be challenging. Thus, an extensive investigation of the diagnosis of ASD towards hybrid multimedical test features and sociodemographic characteristics using AI and ML techniques as a systematic review needs to be presented.This study used a style of systematic reviews and meta-analyses guidelines illustrated in Studies in English journals or conference papersStudies related to various contexts such as diagnosis, detection, and prediction for autism use different methods, algorithms, and AI techniques to optimize ASD processes and enhance diagnosis decision-makingThe above contexts were conducted based on medical laboratory testing, social demography, family characteristic features, and criteria based on questionnairesArticles that monitor ASD based on play therapyArticles that monitor ASD using smart sensors and roboticsArticles that diagnosis based on medical image processing using MRIArticles that diagnosis based on EEG signal processingAfter duplicate papers are removed, the next step includes scanning papers' titles and abstracts according to the inclusion/exclusion criteria. A full-text reading is achieved for potentially related articles. The surveyed works and a running classification for all the articles allow us to create the proposed taxonomy. In the current study, various data were collected and extracted in articles related to autism, which in turn led to a deep perception in the researchers' narrative of things related to the purpose of the article and the motivation and challenges faced by the researcher, also the limitations or recommendations that we will address later in the subsequent sections, in addition, the number of features and algorithms used and the method in which the researcher decided to diagnose or identify autism and assessment matrices, which in turn serve to determine the efficiency and quality of the algorithm used.Five comprehensive analysis processes have been presented based on medical/laboratory features and sociodemographic features in this section for the articles collected from the four databases based on the objectivity of the papers based on:This section discusses the articles' distribution on ASD in single or multiple publications in each country, in this regard, as can also aim to observe networking and collaboration between countries. Besides, in A word cloud describes the focus of detail of these studies and the types of data used in the articles. In addition, it highlights the essential subjects associated with a specific topic. Word clouds are a way of visually representing a set of words. In addition, word cloud summarized many keywords of studies in the form of an easy and understandable ASD picture. It creates an image of a given set of frequently used words. Most studies focused on ASD patients' characteristics . Words are highlighted by making them more central in the picture or increasing their font size. The proposal represents a chronological network map, a historiography map for the most relevant direct citations resulting from article collection. The citation network technique provides the scholar with a new work method that may significantly affect future historiography, as shown in The factorial analysis of the survey can be presented in n = 198 papers were removed, resulting in a total of n = 746 articles. Then, the screening was meant for the title and abstract scanning, which resulted in a total of n = 393 articles. The following filtration was conducted through a full-text reading for the identified articles and resulted in a total of n = 75. The articles (n = 40) were screened and determined as relevant to the review based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” (n = 39). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques which we will address later. The second category consists of “diagnosis ASD based on medical and family characteristic features” (n = 1). The papers were divided and designed based on overlap and content in a solid taxonomy, as shown in The purpose of using taxonomy in the ASD field is to understand the philosophy of the final set of articles and determine their analytical dimension to link them theoretically and intellectually on a systematic basis, which makes it easier for the medical staff to visualize to understand the ASD research in a smooth way away from the complexity and overlap in the understanding of AI theories in general. Accordingly, the collecting articles begin with 944 papers from the four databases. Upon the completion of duplication screening, a total of This category contains 39/40) articles and includes three sections. A questionnaire assesses the diagnosis, and treatment is one of the standard diagnostic methods a doctor and medical staff carry out in diagnosing children with autism based on a set of questions. The collection of questions given to parents to answer includes inquiries related to race, age, family history, etc., based on family and social characteristics  that play a role in diagnosing and exploring the disease. In addition, Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) are questionnaires related to the behavior and action of the child and asked the parents to be analyzed and studied [/40 articThis section consists of 5/39) articles. Early detection of ASD disease  is the primary step in treating the autism patient to be recovered. The early diagnosis of ASD through AI techniques focuses on detecting or predicting autism patients as early as possible to have the best chance for successful treatment. In addition, recent research has proven that early detection of autism leads to the furry assistance provided by doctors, making it easier for the patient to recover faster. In , the stu/39 articThis section consists of 32/39) articles. Recently, ML has been extensively applied in many fields of life and medical domains –42. ML c articlesThis section consists of (2/39) articles. The concept of resampling techniques and data imbalance have been covered in this section. One of the methods commands utilized in ML is the balancing of data based on the target class, which in turn achieves high accuracy in the classification process and gives a perception closer to reality . It has This category contains 1/40 obtained by searching the four trusted databases regarding the medical and familial characteristics of individuals with autism. This is considered a gap that should be addressed and extensively covered. The authors of  dealt wiThis section targets to highlight and debate three basic concepts after extracting information from the collected articles: (1) the motives, benefits, and the importance of the topics that made researchers highlight and try to provide solutions to solve problems; (2) the challenges and what current and former researchers face in the cases and obstacles that have been reported; and (3) the recommendations recommended by the authors and what is the future work to be applied later in the ASD research path.The significant increase in ASD prompted researchers to find ways to diagnose, predict, and improve treatment for this disease. Motives fall into four categories, as indicated in The diagnosis of ASD is essential, and without a diagnosis, this can make so many areas of life complex, distressing and bewildering for the undiagnosed person. This sort of autism spectrum syndrome is detected a great deal later than is conceivable . DetectiA classifier is an ML algorithm used to assign a class label to data input. A classifier utilizes some training data to understand how given input variables relate to the class. Regardless of increased performance in diagnosing ASD applied to different ML algorithms of available clinical tests , this leVarious risk factors play a significant role in individuals with ASD, such as family and patient medical history. In addition, as can be to check gender differences, the disparity in ASD diagnosis between males and females suggests differences in a big underlying etiology of autism . One steDue to the increasing number of patients, the authors recommend that there are serious demands to implement simple, effective, and more accurate prediction models , 51. FurIn this study, the challenges that researchers faced in classifying or predicting ASD have been addressed. They were divided into five clusters, and each cluster contained similar articles as can be seen in Many mental disorders that are similar to ASD become a challenge as there are many other mental disorders whose few displays are very similar to those with ASD symptoms, making prediction a challenging task . In addiPeople with ASD experience various types of symptoms, for example, difficulty interacting with others, repetitive behaviors, and difficulty functioning correctly in other areas of day-to-day life. These symptoms generally occur in early childhood. Most people are not well aware of the syndrome; therefore, they do not know whether a person is suffering from the disorder or not . Many paAutism poses an emotional lifetime struggle for numerous families because of the nature of the disorder . In compASD causes deficits in cognition, communication, and social skills. ASD is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and the presence of restricted, repetitive behaviors , 61. ThrThe quick and continuous increase in information inflation and information technology in world databases is doubling every year. This caused making processing operations on this amount of data a challenge . The troIn the study , the autThe main recommendation , extensiFive particular pathways in treating ASD patients have been presented in . The autCollecting as many possible data from different sources can boost the accuracy performance , 5, 13. In the article , utiliziAs can collect more samples to decrease the impact of special samples on neural network training, the introduction of many factors affecting autism on the networks increases the accuracy of diagnosis and prediction. Use some ways to discover and eliminate noise samples Because of the efficiency of the Multivariate Curve Resolution (MCR) model, a better autism detecting model can be given to society as a potentially very effective automated international recommender system. This can detect autism at an earlier stage for the proper medical guidance and fast recovery. Besides, it can be extended for behavioral and sociodemographic data analysis on other datasets with implementation Deep learning algorithms can be utilized, and helping medical staff makes more accurate decisions on autism The ML classification can be affected by data imbalance on unstructured and more complex acoustic data features of autism Investigate differences in more specific clinical comparison groups  and specific gender groups . In addiImprovement of multilayer fuzzy cognitive map MFCM-ASD through introducing new layers to evaluate ASD can represent new dimensions of symptoms to be included in the diagnostic process This section is aimed at discussing recommendations linked to the diagnosis of ASD. The guidance and future works recommended by the authors are listed in bullet points:Three complex and important section analyses associated with the current topic of this study are stated in this section.In this section, the insertion data from the articles were included in five important columns and presented in All of these data mentioned in AI science and ML algorithms are implemented with multidisciplinary studies that aid in building more precise results. Thus, several articles on autism utilized ML methods have been presented in It can be seen in Furthermore, the below Selecting features remains an essential task before developing a predictive model for ASD classification. As shown in This work is aimed at comprehensively reviewing the literature on autism, which can help the most significant number of researchers and people in this field, especially discovering that ASD diagnosis is a high cost and consumes time and effort. This study provided detailed descriptive information concerning the detection and prediction of ASD that interact with the disease classification covered in the literature in the context of AI techniques and ML algorithms contributions. We comprehensively analyzed some studies to highlight the benefits, challenges, and recommendations related to ASD and found specific gaps. In this systematic review, the focus has been dedicated to the AI techniques and ML and how they can integrate autism diagnosis into the concept of using multimedical tests and sociodemographic characteristics. Several feature selection approaches and ML algorithms have been used in the literature without suggesting the hybridization among both. The feature selection approaches of multimedical test features and sociodemographic characteristics based on reliable guidelines can be an essential method to be applied in ASD classification models to obtain a quick and specific diagnosis. Classification models based on feature selection approaches are remarkable for early diagnosis and treatment, in these contexts, the hybridization among feature selection approaches and ML classification algorithms resulting several ML models. However, no intersection process can be leveraged to diagnose ASD patients based on hybridization models. In addition, the selection process for the best hybrid model based on multievaluation metrics simultaneously is a challenging task that falls under the multicriteria decision-making theory. Therefore, this study suggested using a new framework that can handle methodology in the context of used multimedical test features and sociodemographic characteristic features.


The survey was based on two previously conducted interview studies and included the Student-Instructor Relationship Scale (SIRS), the Mistake Rumination Scale (MRS), the Trauma History Screen, and self-constructed questions about the received music education, early success, and personal experiences. To identify potential risk factors, independent samples t-tests were conducted and found that there are significant differences in musicians with and without MFD in terms of mistake rumination, early success, and the received music education. A logistic regression showed that six factors contributed to the construct to various extents; we observed a significant model , which predicted 71.2% of the cases correctly. This exploratory study shows that psychological and psychosocial factors might play a role in the development of MFD. Understanding these in more detail could inform preventative strategies and complement the current therapeutic approaches to support this vulnerable population better.Musicians’ Focal Dystonia (MFD) is a task-specific neurological movement disorder, affecting 1–2% of highly skilled musicians. The condition can impair motor function by creating involuntary movements, predominantly in the upper extremities or the embouchure. The pathophysiology of the disorder is not fully understood, and complete recovery is extremely rare. While most of the literature views the condition through a neurological lens, a handful of recent studies point out certain psychological traits and the presence of adverse playing-related experiences and preceding trauma as possible contributors to the onset. The nature and the frequency of these factors, however, are under-researched. The present quasi-experimental study aimed to compare musicians with and without MFD in terms of the frequency of various adverse psychosocial and psychological factors to explore their contribution to the onset of the condition. Professional musicians with MFD ( In the past decades, there is a growing awareness of the occupational problems and disorders of performing musicians . Among tWhile our understanding evolved greatly of the genetic predisposition of the sufferers  and the To clarify the origins and the multifactorial network of triggering factors, researchers broadened the field of inquiry by exploring the individual characteristics of the sufferers and found that certain psychological traits, cognitive patterns, and practice behaviours might be contributing factors in the onset . A strinAnxiety, perfectionism, and phobias are closely associated with maladaptive cognitive strategies, such as overfocusing and reinvestment, which were also suggested as triggering factors , and areAll of these possible psychological, cognitive, and behavioural contributing factors are usually discussed in the literature as highly individual traits and behaviours; however, they do not develop in a vacuum. The social and cultural context, in which the individual is embedded plays an important role in the development and cultivation of the personality and behaviours , therefoAs an example, there is a rich literature on how social expectations can increase maladaptive perfectionism , especiaThe frequently mentioned over-involvement in practice which often results in overuse and motor fatigue in this population , can be The idea of external factors influencing the onset of the condition is further reinforced by the fact that the onset often follows a traumatic experience . To the Yet, the majority of the literature considering psychological and behavioural factors in the onset largely neglects how these characteristics evolved in musicians. These features are examined in isolation, with little regard for the highly specialised environment in which they developed. This leads not only to a limited understanding of the condition but also, perhaps unknowingly, results in a narrative that holds the sufferers at least partially responsible for their condition, by highlighting their maladaptive psychological and behavioural traits. To explore this under-researched area, we initially approached the topic with an open and qualitative methodology  to gatheGiven that the symptoms of MFD evolve and are experienced in a professional setting, most often affecting only the movements associated with performance , it was The aim of the present study is to examine the topic further by testing the hypotheses generated through the two qualitative interview studies  in a larThe questionnaire study is the final stage of a larger mixed-method study. The previously conducted interview studies with MDF sufferers  and pracThe questionnaire was administered online, and apart from identifying as a musician with or without MFD, we set no exclusion criteria. Participants were recruited from online support groups and social media platforms, and various organisations and practitioners were asked to share the information and the links in their networks. A total of 240 musicians  answered the survey. After omitting unfinished and partial responses, 175 cases were considered for further analysis. To allow for a more precise comparison, a pairwise matching between participants with and without MFD was conducted based on gender, age, and instrument group, which resulted in a subset of 88 musicians. Information about the mean age, gender ratio and instruments played in the sample and the subset is presented in We collected sociodemographic information , and questions were administered about the participants’ experience with the received music education, performance-related mistake rumination, lifetime prevalence of trauma, and other stress-inducing experiences. Additionally, we inquired about the location of the symptoms of MFD and the received diagnosis in the MFD group.The first main topic of the investigation was the experience with the received music education. In the absence of a measurement tool specifically developed for this construct, we choose to use the Student-Instructor Relationship Inventory (SIRS)  and askeWe also inquired about performance-related mistake rumination using the Mistake Rumination Scale (MRS) , which wData was collected about the experienced trauma over the lifetime with the Trauma History Screen , and addAfter the participants received a link to the questionnaire, they were presented with a downloadable information sheet and were asked to give consent to the research team to use their anonymised data. Without consent, they could not proceed to the survey questions. The survey was open for 2 months.For data analysis, SPSS and R software were used. First, responses with more than three data points missing were omitted manually. In the following analyses, the remaining missing values were replaced by means. The open questions regarding the instruments played were dummy coded twice, firstly, all different instruments were assigned a different number, and secondly, the instruments were grouped into six categories  with one assigned number for each category. The location of the MFD symptoms in the group with MFD was also coded into three categories . Following this, descriptive tests were run to explore the sociodemographic variables.In the second stage of the analysis, the instruments were inspected: Exploratory Factor Analyses (EFAs) and Confirmatory Factor Analyses (CFAs) were run on the scales to evaluate them and to test their internal consistency.t-tests on each variable and tested the relationship between them with a Pearson r correlation. In order to create a comprehensive model of the variables, a logistic regression was used using the presence of MFD as a dependent variable. A detailed description of the tests and the procedure are presented in the result section.To map the differences in risk factors between the groups, we conducted Musicians who suffer from dystonia are vulnerable to psychological distress when asked to recall details about the condition which endangers both their livelihood and artistic outlet. The information sheet and accompanying documents were phrased with this in mind, avoiding negative language, and providing resources of support at the conclusion of the survey. Ethical approval was obtained from the Arts and Humanities Ethics Committee at the University of York, United Kingdom.N = 105) was 44.46 years (SD = 14.29) ranging from 21- to 83-year-old, and the average age of the onset was 35.57 years (SD = 16.743). They started playing the instrument at 13.09 years of age (SD = 7.14), although this number is skewed by some amateur players, who started playing between the ages of 35–50. After removing these outliers, the average age of starting instrumental education was 12.20 years. The participants suffered from the onset of MFD after 21.13 years of playing on average (SD = 13.491). 64.7% of the musicians considered themselves professionals with a further 15.2% responding “maybe,” and 20% of the sample was amateurs. Responses to the open question added at this point suggested that many professional musicians reported “maybe” because they were unable to pursue their profession full time due to their symptoms at the time of their responses. 63.8% of the musicians were diagnosed by a neurologist, further 9.5% by another practitioner, 4.7% by fellow sufferer or musician, and 21.9% were self-diagnosed. There were 31 participants (29.5%) with embouchure problems in the sample with the rest reporting problems with their upper extremities, most often their fingers.The average age of musicians with MFD (2(190) = 1199.250, p < 0.001]. All items correlated at least with one other (>0.3), showing that each item had some level of common variance with the other items, which suggested acceptable factorability. N = 175); the higher number of responses provided more data to establish the factors.20 items were examined for factorability. Half of the items were worded to express the opposite of the targeted construct to avoid participant bias; the scores for these items were reversed for the analysis. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.788, above the commonly recommended value of 0.6, and Bartlett’s test of sphericity was significant , and the three factors showed acceptable internal consistency . This CFA model was used to calculate the factor scores for each factor and each participant, and these scores were used in the subsequent analysis. The items in the three factors are presented in Appendix B  = 248.514, p < 0.001] showed that the items met the requirements for factor analysis. The EFA was run on the full sample (N = 175), with the use of parallel analysis and Promax rotation, and with the sample size and degrees of freedom in mind, the loading values below 0.40 were suppressed. The analysis and the following CFA showed one underlying factor clearly accounting for 65.4% of the variance with good internal consistency (α = 0.818) and was dubbed as “Early success.” The loading table and the correlation table are presented in A second factor analysis was conducted on the items relating to early success on the instrument. The items were moderately correlating with each other 0.453–0.666) showing common variance, but uniqueness. Kaiser-Meyer-Olkin measure of sampling adequacy (0.767) and the Bartlett’s test of sphericity , and one underlying factor in the Mistake Rumination Scale (α = 0.897) . In both scales, means were used as scores for each participant and each factor in the subsequent analysis.Two further CFAs were run on the selected items of the SIRS and the modified Mistake Rumination Scale. The CFAs reinforced the two underlying factors in the SIRS: anxiety around the tutor (α = 0.913) and connectedness to the tutor (α = 0.922) , Nagelkerke R2 = 0.306), which predicted 71.2% of the cases correctly. The coefficient table is presented in The results indicate that there are two highly influential, significant risk factor variables contributing to the construct: Mistake Rumination and Early success, two variables showing non-significant trends and two non-significant variables where the estimates show the predicted direction. With the inclusion of these risk factor variables, we observed a significant model . Assigning difficult materials to students might be due to misinterpreted levels of capability from the teachers’ part, but it is also possible that these young students showed great aptitude. This idea is further reinforced by the fact that musicians affected by the condition are often soloists  or playiAlso, the rapid advancement in playing the instrument might also be linked to the age these musicians began their instrumental studies: the average starting age for our participants with dystonia was 12.20 years (after removing the amateur outliers who started over 35). Children’s learning strategies undergo certain changes as their metacognition and verbal working memory develop. Very young children learn based on mimicking and repeating: the process is mostly unconscious and is not accompanied by cognitive verbal processes . This isWhat is clear, however, is that there are neurological consequences of the late start: the networks underpinning the complex movements needed for playing are developing more optimally if the individual starts playing before the age of seven , therefoIn summary, playing challenging materials without fully mastering the required motor skills is a risk factor in developing MFD. The reasons for choosing such a repertoire might be manifold: late start, which can be associated with different learning strategies and possibly peer pressure, or simply, the aptitude of these students. Nevertheless, both a late start and the mismatch between the established motor skills and the requirements of the repertoire seem to put musicians at risk of developing MFD.Apart from how and what is being learned in the educational context, the atmosphere and the teaching style also seem very influential. Items relating to the received encouragement, and positive feedback were found to be significantly different between the groups with musicians with dystonia reporting a less favourable and less supportive environment. Moreover, the SIRS’s subscale, “anxiety around the tutor” approached statistical significance, with the dystonic participants reporting more anxiety around their teachers, which also points toward an adverse experience with the individual tuition. The relationship between motor learning and feedback has been examined in sports settings: This unfavourable environment was also underpinned by a prescriptive and authoritative teaching style and high demands. Strict constraints on the performance are an inevitable part of playing classical music: many aspects of the performance are subscribed to the player, especially in group settings, such as orchestral or chamber music work, leaving little room for spontaneous changes. These musical constraints make classical musicians more vulnerable to developing MFD than jazz or pop musicians who have more freedom to modify the played material .As inevitable as some restrictions are, there are aspects where the musician should be able to make their own decisions, especially in an educational setting, in order to explore their individual interpretation of the pieces and their technical capabilities . The autIf the general restrictions of classical music are furthered by the teacher’s expectations, and the students must play in a prescribed way, the required precision also increases, and with it, the risk for developing MFD. The more highly specialized a skill’s neural representations are, the less ability they have to adapt to different task requirements . MoreoveThere are established links between socially prescribed perfectionism, anxiety, and rumination , and theWe were not able to show any significant differences between the samples, in spite of the fact that preceding traumatic events or significant change have been repeatedly mentioned in the literature , and alsIn conclusion, while there is a growing amount of literature on the psychological and behavioural triggering factors in MFD, these have not yet been explored in a social and playing specific context. While this study has not been able to show very strong and conclusive evidence, it still seems important to examine the developmental trajectories of these risk factors. This approach can not only enhance music educational practices in a broader sense but can specifically inform preventative strategies. It also has the added benefit of changing the narrative around the sufferers of the condition and shifting it toward a more compassionate and holistic understanding of the personal experience of the musicians.The most obvious limitation of this study is that it uses to some degree self-constructed, previously not tested scales. We had to proceed with this method because of the lack of scales measuring the constructs highlighted in the interviews. Therefore, the development and validation of scales measuring the quality of the received music education are suggested to create a more reliable and valid understanding of the phenomenon.Only 63.8% of the musicians with MFD were diagnosed by a medical professional, therefore, we cannot state with complete confidence that all the participants who reported having MFD are indeed experiencing symptoms of the disorder and not some other form of performance-related problem. However, as more and more information is available describing the condition’s unique and peculiar symptoms, we decided to include all participants in the analysis who claim to have MFD.Also, musicians with MFD were asked to recall personal memories, feelings, and psychological states prior to the onset. This was necessary because we were interested in contributing factors, but obviously, it might not be fully reliable and carries possible personal bias. Moreover, there might be some other underlying psychological constructs that are contributing to these differences; systematic and holistic research including all these factors could offer further clarification. Nevertheless, this exploratory study highlights the importance of researching not only currently present traits in sufferers but also the developmental trajectory of these.In conclusion, mistake rumination and playing challenging materials prematurely possibly contribute to the onset of the disorder. Moreover, we found significant differences between the education of musicians with MFD and healthy musicians: it seems that those who developed the disorder learned less about healthy technique, were encouraged less, more demands were placed on them, and the received tuition followed a more authoritative style.The musicians’ lives who are struggling with the condition have to be viewed as complex, continuously evolving systems that are influenced by the environment. This can help us to clarify the aetiology of the condition and can inform preventative strategies.This is only an early exploration, the first step toward these goals. We need more refined methodologies, to assess various contributing factors and more research to explore how these might impact the individual musicians. Also, as our second interview study  shows, tThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Arts and Humanities Ethics Committee, University of York, United Kingdom. The patients/participants provided their written informed consent to participate in this study.AD recruited the participants, collected the data, and conducted the initial analysis and wrote the first draft of the manuscript which was then amended following HE’s feedback and suggestions. HE was overseeing the final analysis. Both authors contributed to the study design and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The capability of smart materials to adapt themselves to the environment and to the various system conditions are fundamental in the future. Moreover, the possibility of using control signals to modify the properties of the materials provides the real possibility to design innovative systems with a wide range of applications.The future of engineering systems is based on the capability of integrating sensors, actuators, control systems and materials. Engineering products, in fact, are nowadays innovative only if they ensure safety, reliability and good quality of life in all parts of the world. New materials, therefore, must be as much as possible All these innovative concepts living in smart materials rely on having accurate mathematical models. Moreover, the design of materials requires appropriate software tools and advanced sustainable technological processes to produce them. In order to have both reliable and smart materials, more validation experiments are required for their complex characterization.The topic of smart materials is linked to a strongly interdisciplinary area of research. Moreover, the global smart materials market size has been about 60 billion USD in 2021 and it is expected to register an increase of more than The Editorial is organized as follows. At first, a glossary summarizing the particular material types is reported. A short description of the 15 selected papers will follow. The Editorial will be concluded with a summary Table indicating the various correlations among the 15 papers and with a final consideration.Glossary:Magnetorheological Fluids (MRF) are considered smart fluids. The application of a magnetic field allows controlling their viscosity. Made by a carrier oil and magnetic particles are different from ferro-fluidics where the particles are of smaller size.Shape Memory Alloy (SMA) materials, such as TiNi composite, have thermoelastic martensive transportation characteristics upon heating. The shape recovery occurs at a transformation temperature called austenitization temperature.Composite Materials (CO) are made by two or more materials. The material could have different properties from chemistry-physical characteristics; moreover, the composite has different peculiarities with respect to its components.Elastometers are polymers with elastic properties including components with weak or strong links.Metamaterials are artificially structured materials that have the capabilities to control electromagnetic waves, acoustic waves and other types of waves, and can realize negative refraction indices.In the paper, , Wang anThe contribution presented by Zhang et al. in  is relatIn the research presented in  Yoon andThe survey presented in the paper by Vermes and Czigany  reviews The SMA materials have been considered one of the more appealing classes of smart materials. Even if the various proposed applications and the various characterization approaches had a wide diffusion for SMA material, efficient and reliable methods to define sensitivity models and uncertainty have not yet been satisfactorily studied. In the contribution  Islam anThe importance of composite materials and devices based on either classical components or on SMA is remarked in the contribution  by HamanThe behavior of SMAs is strongly related to martensite transformation. Ju and co-authors  considerThe property of smart materials can also be obtained by modifying flat textiles with hydrogels (hydrophilic polymers that are not dissolved by water). The property of hydrogels is very interesting as regards both the mechanical adaptability and the ease of use in biomedical applications, like contact lenses. The study presented by Sa̧siadek in  is referSMAs with a sustainable structure can be also conceived for micro and nanoscale applications. Bhale et al.  characteThe mathematical model of innovative materials represents a fundamental step for the validation of their smart properties. It is considered outstanding, in this context, the contribution by Wang et al. . The autThe contribution proposed in  by Ma etSmart materials are also involved in optical and radiofrequency applications. In the contribution by Mou et al. , the autThe contribution presented by Rogóż et al. in  addresseFuture advanced applications of smart materials related to MEMS and Microsystems, like microstents or microgrippers need further investigations. Jiao et al. in  investigThe contribution by Muhazeli et al. in  presentsIn In addition, the Editorial aims at remarking some details of further papers, in order to reinforce some items on the subject.In particular, in the papers by Roupec et al. , and MuhThese last items allow the reader to have a complete overview of recent papers on smart materials published in the journal Materials, further outlining the multidisciplinarity of the topic and the multi-technological challenges in different system scales, from nanoscales to the very large integration scales.


At present, the academic education of Chinese students is basically public education, but the quality training is mainly handed over to the market for training. Therefore, China’s online education and training institutions have gradually developed under this demand. With the improvement of people’s living standards, families have higher and higher requirements for children’s education, expecting that children can be well improved in physical, mental and psychological aspects, and hoping that they will have their own advantages in the future competition. Therefore, this paper studies the influence of parents’ perception on the brand recognition of online education and training. Through the analysis of dance online training institutions, the research shows that among the three categories, teachers’ Graduation schools account for the highest proportion, with an average proportion of 47.07%, followed by teachers’ grade certificates, with a proportion of 32.29%, and teachers’ competition scores of 25.57%. Therefore, in the process of operation, online dance training institutions should meet the needs of parents to understand the professional level of teachers. Improve the service system of training institutions, improve the parent brand recognition and the number of customers of training institutions, further improve online education and training institutions, and provide them with improvement suggestions and measures for reference. With the continuous improvement of the quality of education in China, students’ parents are paying more attention to education, so their requirements for education are becoming higher and higher. At present, the academic education of Chinese students is basically public education, but the quality training is mainly handed over to the market for training. Therefore, China’s online education and training institutions have gradually developed under this demand . PromotiPeople’s consumption concept and demand have also changed greatly with the development of economy. With the improvement of living standards, families have higher and higher requirements for children’s education, expecting that children can be well improved in physical, mental and psychological aspects, and hoping that they will have their own advantages in the future competition. Therefore, this paper studies the online education and training brand from the perspective of parents’ perception. Choosing a high-quality dance online education institution is a primary factor for parents to consider in the process of cultivating their children’s comprehensive quality education . The desWith the increase of the number of trainees, enterprises have exposed such problems as the lack of standards for service products, the difficulty in ensuring the strength and level of teachers, and the difficulty in meeting the needs of consumers, which make the further growth of these enterprises slow. Through the data analysis of the survey results on the brand recognition degree of the teaching parents of children’s dance online education and training institutions, this paper puts forward strategies for the teaching of children’s sports dance training institutions in terms of low satisfaction, and finally achieves the goal of improving the brand recognition degree of the teaching parents. For example, there is no research on the online education and training institutions from the perspective of parents’ brand recognition degree . TherefoThe innovative contribution of this paper lies in the construction of an online dance teaching system. Brand design is more about improving emotional ability. Through design, children’s self cognition ability is improved to a certain extent, and a long-term emotional bond is established between children and brands. Therefore, brand design should not only meet the basic functions and aesthetic needs, but also meet the emotional needs of users. How to effectively use the rich brand teaching resources on the Internet is the key problem to be solved at present. The construction of online dance teaching system can enable students to view learning content at any time, make full use of Internet resources and scattered time around the computer environment, and improve learning efficiency. Consolidate learning achievements, expand learning influence, lay a solid foundation for better serving the society, and develop and carry forward dance in the future. Combined with curriculum learning, it is conducive to students’ autonomous learning potential, and also provides a shortcut for inter school communication and curriculum resource sharing.The overall structure of this paper consists of five parts.The first chapter introduces the background and significance of online education and training brand, and then introduces the main work of this paper. The second chapter mainly introduces the related work of online education and training brand and the research content of online education and training brand based on the method proposed in this paper. The third chapter introduces the research of parents’ perception on online education and training, and puts forward the teaching mode of dance online training institutions and the teacher-student interaction subsystem. The fourth chapter analyzes the experimental part. The fifth chapter is a summary of the full text.In terms of the elements of business model, most online education and training institutions do not have the ability to master key resources, and they are faced with the problems of high pressure of marketing funds, low liquidity of users, low renewal rate of users, and low stickiness of users, so they cannot achieve long-term profits. It is precisely because the business model of online educational institutions is not clear, and when choosing the business model, it doesn’t aim at creating value for customers, so that the goods provided by them can’t meet the needs of users, which leads to the above problems. Therefore, many scholars have carried out research on this issue.Teachers lacked basic training and physical training in the process of online physical education dance teaching for children, and often ignored teaching rules, which led to the best training effect . In the Drawing on the results of previous studies, this paper studies the impact of online education and training brand recognition from the perspective of parents’ perception, and uses the theory of parents’ recognition to design the evaluation standards, models and systems of online education and training. Provide suggestions for the healthy development of industry norms by investigating the recognition degree and improving the service quality of online education and training institutions. Parents’ recognition of online education and training brands can not only reflect the operation quality of online education and training institutions, but also have a good feeling and predict the future development trend. For online education and training institutions, a better degree of parental recognition can bring better operational feedback and support the long-term development of online education and training institutions. As the service port of the online education and training service industry, online education and training institutions should not only carry out education from the perspective of educating people, but also implement the principle of service first to improve the economic value of their own industry. At the same time, in combination with local conditions, they should implement standardized service models to promote parents’ positive evaluation of their own institutions and improve their recognition. Only in this way can they get higher market recognition and development prospects.Network education has also absorbed the advantages of traditional education in the tortuous process. The original ideal mode has been abandoned in the aspects of examination organization, courseware design, teaching interaction and so on. More and more close to traditional education. In addition, online education researchers are more and more deeply aware of the importance of emotional communication in traditional education. And try to integrate teachers and learners into a virtual classroom through various technical means to make up for the lack of emotional education and humanistic education. In the training, parents have increased their knowledge through the platform of network. Increased awareness and strengthened awareness. It is agreed that the opening of parent classes is of great significance to the growth of children. Under the external conditions of the market economy, many online education and training institutions’ school running ideas and courses are more in line with the market demand, with richer cultural connotation, more comfortable hardware facilities and environmental atmosphere, showing a development trend of flexible school running and management, distinctive characteristics, and continuous improvement of internal quality and quality. In the Internet era, the dance training industry is inevitably affected. Where and how big the impact is, and what are the advantages and disadvantages of the industry, deserve our in-depth study. Therefore, from the perspective of parents’ perception, this paper studies dance online education and training.In the process of preschool education, the design of professional dance courses should be comprehensively explored from the perspective of combining online and offline. This kind of blended teaching has more advantages, can improve the overall teaching quality, and can better integrate students’ learning time . There iDance is a skill-based course. In the process of learning, every movement and every look needs teachers’ demonstration and guidance. Students begin with basic imitation learning, and through teachers’ constant correction and guidance, they complete their own flesh-and-blood memory, and gradually master the key points and skills of every movement, thus realizing their learning goals . As the In online teaching, teachers can not only upload the teaching videos in dance and music classes to various network platforms for students to watch, think and practice, but also make an appointment with parents to conduct live teaching on the network live broadcasting platform, providing a broader stage for students to learn dance and music independently . The finThe first term of the loss function represents the multi task discrimination loss: The second term is the loss of feature matching. Feature matching is performed on the feature maps of all layers in the discriminator network. Feature extraction and matching consists of three steps: key point detection, key point feature description, and key point matching. A feature is a piece of information related to solving a computing task related to an application. Features may be specific structures in the image, such as points, edges, or objects. The key descriptor is a vector used to describe the image patch value around the key. The description methods include the methods that compare the original pixel values and more complex methods, such as the histogram of gradient direction. The specific loss expression is shown in the formulak represents the discriminator network, g represents the g layer in the discriminator network, and D represents the picture waiting to be generated.Where Update the discriminator parameters to maximize the following functionsUpdate the generator parameters to minimize the following functionsx and the generated image w is as followsThus, the calculation between the real dance image d2 is the tracing operation, that is, summing all diagonal elements. In particular, a lower value means better dance image quality and diversity.Where Next, we will introduce the part of loss value decreasing during training. In the process of online teaching, teachers can’t find the problems existing in students’ practice in time, nor can they guide and demonstrate hand in hand. Even in online live courses, the interaction between teachers and students is blocked by the screen, which can’t achieve the effect of face-to-face correction, thus limiting the development of online teaching .Through positive motivation, general administrators can actively let teachers and squad leaders have an impact on students, and teachers and squad leaders further affect students, which is called “positive impact.” The impact of general teachers or squad leaders on administrators, or students’ impact on teachers and squad leaders, or students’ impact on administrators is called “negative feedback.” General negative feedback can provide a certain positive impact on the normal development of their work. In the classroom, teachers can also use different applications to complete online interaction such as asking questions, and guide students’ initiative and self-consciousness in class, so as to better complete classroom teaching. In the process of classroom teaching, teachers can also divide different teaching contents into different links, set different checkpoints, enhance students’ interest in learning, and help students improve their ability to solve difficulties. The sharing of curriculum resources among teachers in the school enables students to enjoy more high-quality teaching resources. The sharing of possible contents between teachers and students, including problem discussion, mid-term and final exams, and homework mutual evaluation, is integrated with curriculum learning, which is conducive to students’ autonomous learning potential, and also provides a shortcut for inter school exchange and sharing of curriculum resources. The distribution of the relationship among teachers, administrators, monitor and students is shown in Similarly, for teachers, if they feel that uploading video is inefficient, they can upload action images first, and then upload the whole relevant video, so as to maximize the interactivity of the website and between teachers and students. Basically, from the administrator to each link, there is corresponding feedback adjustment, and each time it is propagated downward, the lower level goal will carry out negative feedback adjustment on the sub goal, which will play a certain incentive role for both teaching and management. The teacher should first demonstrate to the students, explain the basic essentials of the action to the students, and try to be more detailed to facilitate the students’ understanding. Then, more time will be allocated to the students themselves to summarize the exercises and improve their own abilities. The calculation process of forward propagation isThe calculation formula is as followsThe expression of the active function isThe gradient vanishing problem may occur when the back propagation relies on the gradient to update the network parametersHow much previous state to keep and how much information to discardht–1 of the previous time and the input xt of the current time t, it is transformed into a number between 0 and 1 through the sigmoid activation function.By reading the output Ct is calculated and added to the cell stateA new candidate value vector Because each student has a different perception of dance, and some students have been exposed to dance in the original period, while some students are a blank sheet of paper and know nothing. The differences between students are uneven. At present, the online teaching design of dance courses for most preschool education majors is carried out under the guidance of the teaching concept of “flipped classroom,” which mainly reflects the progressiveness and scientificity of the design from the following aspects. Teachers can assign homework in the application. Students need to complete the after-school exercise according to the teacher’s homework requirements, and upload the video of the exercise to the program. Teachers can check the completion of students’ dance movements through the video and give one-to-one guidance. The module of managing and uploading teaching courseware includes viewing and uploading teaching courseware, viewing the published teaching courseware resources and operating the database of teaching courseware. Upload and manage the teaching video module, including viewing the uploaded teaching video, viewing all uploaded video information, shooting time, shooting location, shooting camera model, number of shooting works, video quality, and teaching video database processing.The teaching goal is the first clear problem in lesson preparation. After all, it determines the teaching content of a class. Teaching methods and the form of teaching organization play a guiding role. Studying teaching materials carefully is the primary basis for determining teaching objectives. Classroom teaching should take the least teaching time to obtain the best teaching effect. We should ensure students’ acquisition of knowledge and development of thinking ability within limited teaching time. This requires teachers to choose the most reasonable teaching methods when preparing lessons, and organize teaching from students’ life experience and existing knowledge background. The factors that parents allow their children to participate in the learning of training institutions are more diverse. Through face-to-face interviews with teachers, parents and managers of training institutions, and telephone interviews with experts, important factors that affect the teaching parents’ satisfaction of children’s sports dance training institutions are selected as comprehensively as possible, and unimportant factors are eliminated. In essence, the process of evaluating online education plans is the process of judging to what extent the courses and teaching plans have achieved the teaching objectives; The purpose of the evaluation is to see to what extent these educational goals have been achieved, and to point out the strengths and weaknesses of the curriculum plan through effective evaluation procedures. Because each indicator is influenced and related to each other. The current online teaching content needs more direct and specific projects, and can provide students with more self-study skills to improve their abilities. Most of the time, basic dance knowledge is an extra-curricular supplement for students. Teachers must pay attention to using more interesting content to attract students’ attention when explaining this knowledge. At ordinary times, teachers should communicate with students more, give some examples for students based on what they have encountered, feel the situation deeply reflected by students, and then use the identity of a person who has come to infect students. In this way, a good and harmonious relationship has been established between students and teachers. Students may also like the teacher and be willing to participate in the teacher’s courses. The measurement scale established in this paper also needs to adjust the correlation of measurement indicators according to the reality and characteristics of online dance training institutions. See (1)Teaching levelThe teaching provided by children’s sports dance training institutions is the content of sports dance, including: acquiring dance knowledge and using dance skills.(2)Teaching methods and meansTeachers’ teaching method and students’ learning method are the main contents of teaching, both of which are important bases. Without a certain content, the expected teaching goal cannot be achieved.(3)Teaching contentThe content of dance teaching is to convey the main information in the process of using teaching methods and means, including dance theory, dance skills, etc.(4)Teaching objectivesOn the basis of applying teaching level and teaching content, we can achieve teaching objectives. The plan, teaching methods, teaching means, and teaching contents made before the teaching content is delivered serve the completion of teaching objectives.(5)Teaching effectTeachers’ teaching level, what teaching methods and means are used, the main teaching contents transmitted, and whether the teaching objectives are finally achieved are the presentation of teaching effects.(6)Teaching environment and atmosphereChildren’s sports dance training institutions are training sports dance skills, and the training environment of children’s sports dance skills is also a crucial factor in the teaching process, such as the quality of the floor, the placement of mirrors, the number of dance props, and the ventilation of the venue.In order to integrate learning experiences to form a coherent teaching plan, the process of dividing learning experiences into units, courses and teaching plans. When compiling a set of effectively organized learning experiences, three standards should be met, namely, sequence, continuity, and integration. Customers who often attend training in first-tier cities will have higher expectations for teaching level, teaching content, teaching methods and means; However, a parent from a third-and fourth-tier city doesn’t have high expectations for the teaching methods of educational institutions, so this is also influenced by personal cognitive level. Besides, exaggerated advertising or publicity will also raise parents’ expectations of institutions. For example, the first-class teachers from China Sports Dance Association, etc., parents naturally have very high expectations for the teaching effect provided by them.The dominant indicator “service quality” is decomposed into three indicators: service attitude, parents’ complaint handling and parents’ rights and interests protection. Then, the decomposed three indicators are compared pairwise to obtain the comparison matrix of the three indicators, as shown in Then, each column of data is normalized, and a new eigenvector matrix is obtained by dividing the relative coefficient of each column by the sum of elements of each column, as shown in Finally, the data of each row is normalized, and the arithmetic mean of the sum of the data of each row is taken to obtain the weights of the three indicators, which are 0.632, 0.105, and 0.259, respectively.As can be seen from Online dance training institutions can enhance the ability of students in the training service, including physical ability, professional and technical ability and self-confidence ability, according to the different purposes of parents’ choice of projects. At the same time, it is also necessary to properly take into account the acceptability of students at different ages and provide them with interesting and effective training content. In addition, according to the needs of students and their parents’ professional path, training institutions should set up scientific and reasonable advanced learning classes accordingly. Therefore, this experiment adopts the following four dance categories to conduct an experimental investigation on the praise rate: Rumba, Cha Cha Cha, cowboy, and Samba. The experimental results are shown in Among the majors that children are studying, rumba accounts for the vast majority and is the most popular. The highest praise rate is 81.69%. Chaqia ranks second, with a highest praise rate of 58.35%. Cowboy ranks third, with a highest praise rate of 39.63%. Samba ranks last, with a highest praise rate of 25.03%. It can be seen that most parents prefer to let their children learn Latin dance. It is generally believed that learning standard dance is boring and requires high venues. Among the learned dances, rumba and Cha Cha Cha dances account for the majority, which indicates that they are the most popular dances to some extent. On the other hand, it also shows that the training time of each dance is uneven.As shown in In this experiment, the professional level of teachers is known through various channels, and the channels for parents to understand the professional level of teachers are studied. The following three categories are used for experimental comparison, namely, the grade certificate of teachers, teachers’ Graduation colleges, and teachers’ competition results. The experimental results are shown in Among the three categories, teachers’ Graduation schools account for the highest proportion, with an average proportion of 47.07%, followed by teachers’ grade certificates, with a proportion of 32.29%, and teachers’ competition scores of 25.57%. Therefore, in the process of operation, online dance training institutions can display teachers’ relevant professional displays through appropriate ways to meet parents’ needs for understanding teachers’ professional level.In order to observe this regulation more intuitively, this paper divides the samples into low parental expectation and high parental expectation according to different levels of parental expectation, and draws an interaction diagram of parental expectation, as shown in When the family’s expectations are low for a long time, with the increase of the number of examinations, the academic performance shows an upward trend. When the parents’ expectations are high, the parents pay high attention to the children and have a lot of communication with the teachers. At this time, the teachers’ expectations and parents’ expectations should promote each other and promote the children’s learning motivation; However, when the family’s long-term expectation is medium, it means that the child does not feel what the parents expect of him. The indifference of the parents’ attitude may make the child indifferent or even resistant to the expectations of the teachers, and then affect the academic performance.Today’s education for teenagers is a comprehensive education, and parents need to spend a lot of energy and money on their children. Therefore, online dance training institutions for teenagers should rely on the local economic situation to develop products with higher cost performance in future courses, activities and other related charging standards. Therefore, this experiment mainly analyzes the reasons for participating in online dance training and the children’s age group, and comes to the following reasons: exercise (a), cultivating children’s self-confidence (b), cultivating children’s temperament (c), and children like (d). Therefore, data statistics are made on the correlation between the above four reasons and age, and the experimental results are shown in On the statistics of the reasons for participating in the training and the changes between ages, it is believed that online dance teaching and training can cultivate children’s temperament, which is the first reason; the second is that it can cultivate confidence in the second place; then the third one thinks it is possible to exercise, and the last one comes to the training because the children like it. Let the children participate in the training.The quality perceived by customers comes from customers’ feelings. Customers will not use special scientific instruments to measure the quality of products like enterprises, but mainly evaluate the quality of products through vision, hearing, feeling, and the analysis and judgment of the information they know. In addition, subjectivity is also reflected in that different people have different aesthetics and values. For the same product, different customers will have different quality perceptions of the product. This paper analyzes the evaluation of parents’ perceived quality from four index factors: external environment, teaching professional level, teaching environment, and school content effect. According to the average score of the comprehensive evaluation of perceived quality, the differences of perceived quality among parents of different ages, parents of different genders and students of different training times are analyzed. The distribution of perceived quality evaluation is shown in The average score of the comprehensive evaluation of perceived quality has four index factors, which are: external environment, teaching professional level, teaching environment, and school content effect. The order of satisfaction is: external environment > teaching environment > teaching professional level > school content effect. The purpose of quality analysis is not to rank teachers and students. With the help of an effective quality analysis system, we can deeply mine the basic data, compare horizontally and track the teaching quality vertically. Diagnose and feed back the teaching quality in an all-round and multi angle way, and help download schools to find problems in the teaching process. Help teachers find problems in the teaching process, and help students and parents find problems in the learning process. From a deep level of multi angle and multi-faceted analysis, through exquisite teaching quality analysis and teaching quality tracking analysis, we can find and solve problems in time, so as to achieve the purpose of improving teaching quality.The business models of online educational institutions all rely on the technical support of the Internet. No matter what kind of model, it is necessary to connect the platform and users through the Internet. The stable operation of online educational platform is crucial to the development of online educational institutions and has an important impact on customers’ trust in online education. In this paper, the influence of parents’ perception on the brand recognition of online education and training is studied. Through the analysis of online dance training institutions, the research shows that among the three categories, teachers’ graduation colleges account for the highest proportion, with an average ratio of 47.07%, followed by teachers’ grade certificates, with a ratio of 32.29%, and teachers’ competition scores of 25.57%. Therefore, in the course of operation, online dance training institutions can display teachers’ professional displays through appropriate channels to meet the needs of parents for their understanding of teachers’ professional level. Parents have a better understanding of brand recognition through online educational institutions in business model innovation. They should pay attention to independent research and development of new patented technologies and introduce advanced new technologies to assist classroom teaching. For the live broadcast one-to-one mode, class mode and 020 mode, advanced AI and big data technologies can be introduced to assist classroom teaching to attract students’ attention and improve learning efficiency. In the perceived quality, the service has reached the expected level, the internal and external environment of the institution, the degree of brand recognition of learning effect has reached the expected level, and the security score is low. The institution has not done enough for parents’ expectations, and the products and services have room for improvement. In terms of perceived quality, the service has reached the expected level. But the safety score is very low. Therefore, there are certain limitations, which need to be further discussed and analyzed in the future.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.


The social-ecological model (SEM) provides a framework for developing multidimensional preventive strategies at the intrapersonal, interpersonal, organizational, community, and policy levels. While sarcopenia forms due to multiple factors, the SEM has not yet been applied to the prevention of sarcopenia. We aimed to categorize the factors associated with sarcopenia in each level of the SEM.The electronic databases were searched between 2010 and 2021 to collect factors associated with sarcopenia. Information from guidelines, reference lists, and expert consultation was further hand-searched. The factors mentioned in the reviewed studies were classified into the SEM.In 17 studies, 42 factors associated with sarcopenia were identified; thereafter, 33 biological and individual factors were categorized at the intrapersonal level, isolation was categorized at the interpersonal level, a sedentary lifestyle was at the organizational level, community involvement and social capital were at the community level, as well as knowledge about the disease, disability, income, education, and residential area were categorized at the policy level. The categorization of factors associated with sarcopenia, based on the SEM level, may be useful in the development of preventive strategies; however, further research is required. Sarcopenia is an age-related progressive and generalized skeletal muscle disorder that is caused by multiple factors and was defined by expert panels, such as the European Working Group on Sarcopenia in Older People (EWGSOP) 2  and the The social-ecological model (SEM) is a schematic framework designed to improve our understanding of disease at multiple levels  . In addiGiven the multifactorial manifestations of sarcopenia, there seems to be a need to address risk factors related to the prevention of sarcopenia using the SEM. The purpose of our present work is to outline the factors associated with sarcopenia and categorize the factors into each level of the SEM, which has been never performed. This would be useful for understanding sarcopenia and its risks, as well as for promoting prevention strategies of sarcopenia based on the level of SEM.Materials and methodsThe present systematic review adhered to the guidelines of the PRISMA-ScR  checklist for the reporting of findings . To streIdentify the Research Question and Relevant StudiesThe goal of this literature review was to identify and categorize the factors associated with sarcopenia at each level  in the SEM Table  5]. The. The5]. Select Studies and Chart the DataThe following inclusion criteria were applied: 1) availability of full text; and 2) publication between January 2010 and June 2021. To use the international guideline definition of sarcopenia, studies before 2010 were excluded. Two reviewers (JS and JW) independently conducted screening and further reviews. First, the title and abstracts were compared against the inclusion criteria. The articles that appeared to meet the criteria and those for which there was any uncertainty were obtained as full-text articles. Then, both reviewers screened the full text for inclusion. To confirm the robustness, the eligibility of the collected articles was discussed between the two reviewers until a consensus had been reached. Arbitration was conducted by a third reviewer (KK) if required. Two reviewers independently extracted the data from the included studies. We extracted key information about the selected articles . If data on participants, interventions, or study characteristics were missing or not sufficiently described, we contacted the corresponding authors and tried to obtain missing information.Collate/SummarizeFindings regarding factors associated with sarcopenia were aggregated, and the authors classified the factors into the level of the SEM . ConcretResultsStudy SelectionFigure Descriptive Findings From the Reviewed PapersTable DiscussionWe identified 42 factors associated with sarcopenia. Then, based on the SEM, 33 of these factors were categorized at the intrapersonal level, isolation was categorized at the interpersonal level, sedentary lifestyle was categorized at the organizational level, community involvement and social capital were categorized at the community level, and knowledge about the disease, disability, income, education, and residential area were categorized at the policy level. The findings obtained by displaying the factors associated with sarcopenia at each level of the SEM would be of value to understanding sarcopenia and its risk factors at multifaceted levels and for promoting interactive approaches to the prevention of sarcopenia.In the present work, many factors were found at the intrapersonal level and several factors were found at the other levels. This might partly reflect that biological and individual research has been mainly conducted to clarify the risk factors for sarcopenia, as it is in other medical fields in general. The prevention of sarcopenia is not always thought to be sufficient to exert influence on individuals and their surrounding environments, given the multifactorial manifestations of sarcopenia ,2,4. In The approach to the factors categorized at the intrapersonal level is easily understood; that is, lifestyle modification and medical treatment are usually provided to individuals in health services and clinics. The approach to environments may be hard to be understood directly, but some examples exist. For example, at a familial interindividual level, if a family member takes care to ensure that protein-containing food is available in daily life and this food intake is shared in a family, it may naturally contribute to the prevention of sarcopenia in all members as an interpersonal-level approach. Workplace health promotion can help individuals in the same workplace to adopt and maintain healthy lifestyle behaviors . As sedeThe present scoping review was associated with several limitations. First, the search methods  may have been restrictive for collecting information, although comprehensive information is needed to use the SEM. We made an effort to use standard methods for the review, and we completed a full inspection of references in the included studies and collected additional information from experts. Second, the definition of sarcopenia was different depending on the year of publication because the new criteria reported by the EWGSOP in 2018 allow for the diagnosis of probable sarcopenia based on grip weakness alone [In summary, the present study using the SEM sorted the biological and individual factors of sarcopenia at the intrapersonal level and other factors at the interpersonal level (isolation), organizational level (sedentary lifestyle), community level , and policy levels . The SEM-based categorization of factors associated with sarcopenia may serve to improve the understanding of sarcopenia and its risks as well as interactive approaches to prevent sarcopenia, and in turn, be applied in the development of multidimensional strategies against sarcopenia. Further confirmation of the categorization and implementation of approaches based on the categorization is required.