Our human societies and certainly also (bio) medicine are more and more permeated with technology. There seems to be an increasing awareness among bioethicists that an effective and comprehensive approach to ethically guide these emerging biomedical innovations into society is needed. Such an approach has not been spelled out yet for bioethics, while there are frequent calls for ethical guidance of biomedical innovation, also by biomedical researchers themselves. New and emerging biotechnologies require anticipation of possible effects and implications, meaning the scope is not evaluative after a technology has been fully developed or about hypothetical technologies, but real-time for a real biotechnology.In this paper we aim to substantiate and discuss six ingredients that we increasingly see adopted by ethicists and that together constitute “ethics parallel research”. This approach allows to fulfil two aims: guiding the development process of technologies in biomedicine and providing input for the normative evaluation of such technologies. The six ingredients of ethics parallel research are: (1) disentangling wicked problems, (2) upstream or midstream ethical analysis, (3) ethics from within, (4) inclusion of empirical research, (5) public participation and (6) mapping societal impacts, including hard and soft impacts. We will draw on gene editing, organoid technology and artificial intelligence as examples to illustrate these six ingredients.Ethics parallel research brings together these ingredients to ethically analyse and proactively or parallel guide technological development. It widens the roles and judgements from the ethicist to a more anticipatory and constructively guiding role. Ethics parallel research is characterised by a constructive, rather than a purely critical perspective, it focusses on developing best-practices rather than outlining worst practice, and draws on insights from social sciences and philosophy of technology. Until the 1960’s, ethics as an academic discipline hardly ever dealt with ethical questions of everyday life and had a strong focus on abstract debates such as the nature of morality , 3.These days, societies and certainly also (bio) medicine become more and more permeated with technology, here used to refer to methods, systems, artefacts and devices which are the result of scientific knowledge that can be used for practical applications . SimilarWhile scholarly attention has been paid to identifying and anticipating ethical aspects of innovation in other technological fields like engineering and design, –7 such aDisentangling wicked problemsWe identify six ingredients that together can be referred to as ethics parallel research: It concerns (1) disentangling wicked problems, (2) upstream or midstream ethical analysis, it is (3) ethics from within, it includes (4) empirical research, draws on (5) participatory design and it focuses on (6) societal impacts. As we will describe, these aspects are related and even overlap, and taken together, these six ingredients help to provide meaningful guidance in the development of new technologies in health care and convene to identifying the ethical implications as the input for a normative evaluation. We first describe the ingredients of ethics parallel research and we will subsequently reflect on the use of these elements in bioethics.Biomedical innovations often invoke fierce public reactions and academic controversies that include a large number of stakeholders. This also means that many different perspectives, questions and implications are involved. The societal and ethical challenges of novel biomedical technologies closely resemble what in STS and in policy studies is known as wicked problems . Wicked This first step of disentanglement does not presuppose the outcome of such debate yet and aims to identify different stances and arguments, which contributes to making the debate more transparent . Disenta(2)Upstream or midstream ethical analysis, not merely end of pipelineFor example, germline gene editing .(3)Ethics from withinFor example, in the past years a new type of biobank has emerged: organoid biobanks Empirical researchFor example, for the development of biobanks for complex tissues, such as organoids, the scientific and clinical applications of such tissues need to be understood in order to assess the ethical dimensions, possible challenges and solutions. By working from within, the ethicist is able to provide realistic guidance to the researchers and simultaneously steer the societal debate away from far-fetched hypothetical scenarios. As indicated above, the applications and manifestations of organoids are multiple and the development of new types of organoids and possible applications goes rapidly. By working together with researchers and other experts, the ethicist is not only up-to-date about new insights and applications of these type of complex tissues, but the ethicist can also help in shaping the ways organoids are stored and used . These aTechnologies and innovations in biomedicine commonly focus on enabling new types of research, improving care or preventing illnesses. Once these technologies are developed, they will interact with users of such technologies, such as patients, healthy citizens or health care professionals. Technologies and innovations in biomedicine differ from other technologies in the sense that they influence our bodies, health (care) and wellbeing, and also our perceptions of health and disease. This specific context, demands extra scrutiny and alignment with end-users perspectives, given that the effects of such technologies will affect pertinent and private aspects of their lives. The perspectives of patients and other societal stakeholders are helpful for understanding how such technologies may be used or which effects it may have in practice, given that end-users have a particular type of knowledge , 28. Eth(5)Participatory designFor example, interviews with organoid-donors with Cystic Fibrosis have provided useful insights into how donors perceive their relation to the organoid. Many donors express an ambiguous relation towards organoids . They seTechnologies in biomedicine (should) serve societal goals such as prevention or treatment of illnesses and the relief of pain and suffering. Because of this integration of societal goals in technologies, the development of these technologies is no longer perceived to be external enterprise, but intrinsically embedded in society . This mehttps://www.nwo.nl/onderzoek-en-resultaten/onderzoeksprojecten/i/58/35158.html).(6)Societal impacts: hard and soft impactsAn example for the value of participatory design can be found in the development of AI -effectiveness, which are in the literature sometimes referred to as ‘hard-impacts’ [The midstream of technological development is a crucial step, because it moves the technology into society. This means that first tests are performed in practice, for example by a pilot study, first-in-man study or a first field trial. It is also an exciting phase for the ethicist, because the analysis can shift focus from impacts’ . Hard imimpacts’ –39. The To give an example: one of the pressing questions in the ethical and societal debate on the implementation of AI in medicine, is how humans and AI should cooperate. While AI is able to deal with an overwhelming amount, production speed and multidimensionality of data, that well exceeds the limits of understanding of the human brain , the rolEthics parallel research brings together elements from traditional bioethics, learned from the ‘empirical turn in bioethics’  and fromBy following the ethics parallel approach, the ethicist is able to identify and evaluate ethical questions already in the early phases of technological development and to constructively guide the development of new technologies, by bringing reflexivity in the design process and information and structure to the societal debate. This expands, rather than replaces, the types of evaluation and roles that bioethicists have in more traditional approaches, where ethicists evaluate developed technologies, evaluate whether technologies are aligned with existing guidelines or speculate and reflect on questions whether the development of a non-existing technology would be desirable. Moreover, a distinctive aspect of ethics parallel research is a constructive, rather than a pure critical outlook: it aims to develop and contribute to best-practices rather than outlining worst practices. Importantly, ethics parallel research does not aim to replace more traditional approaches for developing normative theories and principles, but it results in both empirical and more theoretical insights that can provide the input for normative analysis and the formulation of more abstract normative recommendations. The formulation of such normative recommendations remains certainly also an important task for bioethicists, and would require an additional approach. The insights obtained by the six ingredients can for example be used as input in the widely adopted wide reflective equilibrium , 43.Ethics parallel research has, like any approach, also drawbacks and pitfalls. First, it is an approach to ‘think with’, rather than (only) ‘think about’ practice. This means that the ethicist is ‘embedded’ in technological and scientific practice and must take care to avoid ‘going native’. Embedded ethicists will need to navigate between staying embedded and finding a critical constructive voice. As with other potential conflicts of interest, ethicists should be transparent about the nature of the collaboration and potential  support. In addition, it may be helpful to also involve an external ethicist to the project when proposing specific guidelines . A relatSecond, morality does not only influence technology, but also vice versa. As the technology influences societal values during the technological process, the values identified more upstream in the development may differ from the values with which the technology is evaluated more down-stream of the development. Due to so-called techno-moral change, the moral yardstick to assess the desirability of a certain technology may change along the development of the technological innovation. The goal of ethics parallel research is not to formulate conditions for technological innovation that are carved in stone, but to remain flexible and reflective throughout the development process so that ethical guidelines remain fit for purpose, rather than a dogma. The benefits of ethical guidance during the development process should, however, not be underestimated: mitigating negative effects of technologies, enhancing positive impact and sensitizing researchers and developers to the ethical and societal impact of their research, are in themselves desirable aims within applied ethics.Third, the development of new technologies is unpredictable, some technologies develop faster than foreseen, others fail unexpectedly, while still others may have unforeseen and unintended impacts. These issues relate to the anticipation side of the Collingridge dilemma: in the early stages of development, meaningful guidance is very difficult. The aim of ethics parallel research to provide guidance in the early phases of technological development, therefore brings forward the challenge of finding the right time to jump on and off board. The timing will much depend on the specific technology and the way in which it will develop. Ethicists will have to learn to recognize the right time and widen their skillset to make sense of the early phases of technological innovations. In addition, they will have to adapt ways of exploring expectations, needs and anticipation of patients and other societal stakeholders with regard to new (bio) technologies, rather than focusing solely on their experiences. Only then meaningful orientation and anticipatory governance can be formulated to meaningfully guide development of new (bio)technologies.In this paper we have drawn on three examples, organoids, gene editing and AI to illustrate the types of issues ethics parallel research is suited for. These examples vary greatly in terms of their development, societal implications and wickedness. We believe that ethics parallel research is a valuable approach for guiding the development of these technologies, and virtually any other innovation within biomedicine.To conclude, ethics parallel research is an approach that is well-suited to the ethical evaluation of biomedical innovations. It means that ethicists identify and evaluate the ethical issues of a novel biomedical technology parallel or even proactively as the field develops. By bringing together ingredients that orginate from traditional bioethics, empirical bioethics, STS and philosophy of technology, this approach can ethically guide the increasing permeation of technologies and innovations in the field of biomedicine. It widens the roles and judgements from the ethicist to a more anticipatory and constructively guiding role. The benefits of this approach are to provide guidance along the process of biomedical innovation, to sensitize (bio) medical scientists for the ethical aspects of their work, and to mitigate negative effects.Additional file 1: Supplemental File 1. Description of the technologies used to illustrate the approach of ethics parallel research


As the scale and depth of artificial intelligence network models continue to increase, their accuracy in albumin recognition tasks has increased rapidly. However, today's small medical datasets are the main reason for the poor recognition of artificial intelligence techniques in this area. The sample size in this article is based on the data analysis and research on urine albumin detection of diabetes in the EI database. It is assumed that the observation group has at least 20 mg UAER difference from the control group, and the standard deviation of the UAER change from baseline to 12 weeks is 30 mg. Therefore, the sample size of the two groups is 77 cases. Assuming that the rate of loss to follow-up during the follow-up period is 20%, at least 92 patients are needed. The final enrollment in this study is 100 patients. Studies have shown that DR is used as an indicator to diagnose NDRD, and its OR value is as high as 28.198, indicating that non-DR can be used as an indicator to distinguish DN from NDRD. The meta-analysis found that DR has a sensitivity of 0.65 and a specificity of 0.75 in distinguishing DN from NDRD in patients with type 2 diabetes, and it is emphasized that PDR is highly specific in the diagnosis of DN. Using a meta-analysis to systematically analyze 45 studies, it was found that the sensitivity of DR to diagnose DN was 0.67, the specificity was 0.78, and the specificity of PDR to predict DN was 0.99, indicating that DR is a good indicator for predicting DN, and the team's latest research has also verified this point of view. They have established a new model for diagnosing DN. In addition to including traditional proteinuria, glycosylated hemoglobin, FR, blood pressure, and other indicators into the diagnostic model, it will also include the presence or absence of DR. The final external verification accuracy rate of this model is 0.875. With the continuous development of proteomics, the proteomics technology system based on protein separation technology, biological mass spectrometry technology, protein interaction technology, and bioinformatics technology has solved the difficulty of accurately characterizing the proteome expression profile and is the DKD (diabetic kidney disease) research system establishment, and improvement of DKD provided technical support and laid a good foundation for DKD functional proteomics research. But there are still many shortcomings in urine albumin detection, which seriously affects the recognition of diabetes .Knobel used iTRAQ technology to identify potential urine biomarkers and verified them with ELISA. The results showed that haptoglobin (HPT) as a biomarker with significant content differences can distinguish healthy individuals from DKD patients . Mise etGroop PH screened 8 kinds of LMW proteins unique to diabetic patients, using these proteins, then analyzed the urine peptide sequence to predict the proteolytic characteristics associated with DKD, and explore the differential regulation mechanism of inflammation and complement system in DKD . Dantas In order to evaluate whether urine exosomes can reflect changes that occur at the tissue level, this paper collected exosomes in urine samples. The study found that the proteome of exosomes and the proteome of kidney tissue have the same changing trend. This paper proposes an artificial intelligence-based diagnostic model for diabetic albumin membrane lesions. First, the collected albumin is analyzed, and the analyzed albumin is subjected to background removal, denoising, and normalization using a digital albumin processing algorithm. The protein pretreatment operation reduces the influence of albumin quality on the model training results; at the same time, in order to avoid the phenomenon of network model overfitting due to the small amount of albumin membrane albumin data, it is necessary to expand the data of the pretreated albumin and then load the processed data set into the deep convolutional neural network model for training. After training, the model can automatically detect the lesion level of the patient's albumin membrane albumin. The artificial intelligence algorithm is used to automatically extract the characteristics of albumin, and the traditional support vector machine (SVM), K nearest neighbor algorithm, and other steps that require feature detection and manual feature extraction of albumin are discarded, reducing human factors. The phenomenon of misdiagnosis can also greatly shorten the diagnosis time of diabetes, which is of great significance for the early prevention and treatment of patients with diabetes.The emergence of urine microalbumin detection technology has further improved the efficiency and accuracy of screening and identification of DKD (diabetic kidney disease) biomarkers and improved the characterization of the body's proteome expression profile in the DKD state. In order to explore the pathogenesis of DKD, screening early diagnostic markers and therapeutic targets and revealing the complex molecular regulatory network of DKD laid the foundation. The difference in protein expression levels predicts protein molecules closely related to diseases and verifies their functions to confirm target molecules related to diseases . The priHowever, for the separation and characterization of protein complexes, the above techniques are not applicable. This is because the harsh denaturation conditions can easily cause the dissociation or even denaturation of protein complex subunits, which makes it impossible to obtain a complete protein complex . In contThe identification work after protein separation can determine the type of protein in the sample, which is a key step in proteome analysis. In recent years, biological mass spectrometry technology has rapidly developed into a proteomic research technology for identifying high-throughput biological macromolecules. The soft ionization method can ensure that biological macromolecules will not produce too many fragment ions, which greatly simplify the complexity of the spectrum, which is conducive to subsequent further analysis . ESI-MS Artificial Intelligence Model Performance Evaluation Index. In order to quantitatively compare and analyze the classification results of recognition networks, this paper uses two indicators of model accuracy and space complexity to objectively evaluate the classification effects of various recognition networks. The indicators used to evaluate the classification effect of the model include accuracy and average accuracy. The calculation formulas are as follows:E represents the number of samples correctly classified and K represents the number of samples tested. The activation function has a slope gradient of 1 for all positive inputs, indicating that using the IC activation function can make the gradient descent run faster and effectively reduce the training time.In the DetectionNet model structure proposed in this article, different convolutional layers use multiple convolution kernels to extract diabetic albumin lesion features:Ew module:In addition, albumin has rich detailed features. If the convolution field of view is too large, it is not conducive to the extraction of detailed features. The convolutional neural network in this article uses two 3 x 3 convolutions to replace a 5 x 5 convolution in the This paper uses the deep convolutional neural network DCNN as the training model for the identification of diabetic albumin membrane lesions. The training network model is based on Google's Inception albumin classification and recognition model, but albumin membrane disease albumin has its own characteristics. On this basis, it is necessary to adjust network parameters such as parameter setting and network model configuration to make it more suitable for classification training and recognition of albumin membrane lesions:Even when the albumin is deformed or there is noise, it will not have a significant impact on the recognition results; on the other hand, it reduces the complexity of the network model and reduces the network parameters through the method of local perception field and shared weights, which is better than traditional models. Higher accuracy is as follows:Increasing the training data set is the most direct solution to overfitting the training model. However, due to the shortage of the data set, no additional data albumin can be provided. Therefore, it can be treated without changing the albumin disease level of the original data set. Perform operations such as zooming, rotating, flipping, and changing the brightness to make the albumin quantity of each disease level data set equal:After the data is expanded, the category with a larger number of samples has less impact, and the category with a smaller number is expanded to complement the category with a larger number:The loss function of this part includes two parts, the confidence loss and the category loss shown inP is assigned to this point, P is a random number between 0 and 1, and P is compared with the generation probability Prob; there areA probability value The proposed loss function makes the bounding box regression process of target detection faster and more accurate than the previous loss function. The loss function is shown inPathological identification has higher requirements for convolutional neural networks  and requt = 3.776, P < 0.001). After adjusting for age, gender, FPG, 2 h PPG, and HbA1c variables, the observation group UAER still showed a significant decrease .The sample size in this article is based on the data analysis and research on urine albumin detection of diabetes in the EI database. The observation group has a UAER difference of at least 20 mg from the control group, and the standard deviation of UAER changes from baseline to 12 weeks is assumed to be 30 mg. Therefore, the sample size of the two groups is 77 cases. Assuming that the rate of loss to follow-up during follow-up is 20%, at least 92 patients are needed. A total of 100 cases were enrolled in this study. And after treatment, the UAER of the observation group decreased even more  condition protein expression profile of podocyte cell line. The identified differential proteins mainly belong to cytoskeleton proteins and specific annexins. The downregulated expression of annexin in glomeruli is also confirmed in patients with DKD. Annexin is a Ca2+ binding protein and phospholipid-binding protein involved in cell membrane organization and transport, and as a membrane scaffold, annexin also participates in the construction of a specific podocyte slit membrane. Therefore, the HG environment affects the construction of the podocyte slit diaphragm by downregulating the expression of annexin, destroys the glomerular filtration barrier, and induces the occurrence and development of DKD. In addition, this paper performed a proteomic integration network analysis on a large amount of relevant data obtained and used HG-treated distal renal tubular cells to conduct a variety of targeted function studies.The Application of Proteomics in the Study of the Pathogenesis of DKD. Understanding the internal mechanism of the occurrence and development of DKD is very necessary for researchers to establish new diagnostic methods and develop new treatment ideas. The application of proteomics in the diabetic nephropathy process from diabetes to diabetic nephropathy involves a series of complex molecular regulation mechanisms. This article systematically analyzes biological samples of patients with DKD, revealing part of the pathogenesis of diabetic nephropathy, and in the early stage of DKD. Certain results have been achieved in diagnosis and treatment, which have promoted people's understanding of the occurrence, development, prediction, and remission of the disease and laid a good theoretical and experimental basis for the comprehensive treatment of DKD. In the DKD research, the establishment of a complete urine proteome database in this paper is the basis for the development of disease urine proteome research. This article attempts to define the urine proteome in various clinical settings, and through various proteomics methods, approximately 800 proteins have been identified, laying a foundation for candidate molecules for the discovery of biomarkers from the urine proteome.This article uses urine microalbumin samples to train the artificial intelligence model. As shown in As shown in As shown in As shown in The results of albumin detection and analysis are shown in 2+ absorption obstacles in distal renal tubular cells, which will eventually lead to renal tubules. As shown in As shown in As shown in As shown in As shown in R is 16.6 times higher than that in patients without DR. It is worth noting that these studies all use proteinuria or EGFR to diagnose nephropathy but cannot make a clear diagnosis of DN, and many studies use ophthalmoscope or indirect ophthalmoscope instead of gold standard color photography for the diagnosis of DR at baseline. Therefore, although the correlation between PDR and DN can be made clear, the diagnostic value of PDR for DN still needs to be further studied. As shown in As shown in As shown in Before the obvious pathological changes of DKD, the whole proteome of patients has changed. Therefore, the detection and discovery of urinary microalbumin are of great significance for the early diagnosis of DKD. Urine and blood samples are convenient, safe, painless, and noninvasive and have been widely used in clinical biomarker screening. At present, DKD proteomics research mainly screens and identifies protein biomarkers from patients' urine, serum, and other noninvasive samples, in order to achieve the early diagnosis of DKD. The objective is to study the biomarkers of DKD urinary proteome. In kidney-related diseases, urine is a potential source of kidney-related biomarkers. Urine proteomics studies are an important way to investigate the associated changes in renal dysfunction. Urine protein is composed of a mixture of plasma and kidney protein, of which about 30% is plasma protein and the remaining 70% is produced in the kidney. Because the normal urine protein usually reflects the normal physiological characteristics of renal tubules, the changes of urine protein excretion can reveal the response of renal tissues such as renal tubules and glomeruli to physiological stimulation.So far, many studies have identified candidate biomarkers in urine proteome for early diagnosis and prediction of DKD progression, mainly including biomarkers reflecting renal injury caused by glomerular injury, podocyte injury, renal tubular injury, oxidative stress, inflammation, and activation of the renin-angiotensin system (RAS), such as transferrin related to glomerular injury protein, type IV collagen (IV), podx protein (podxl), and wilmstumor-1 factor related to podocyte injury.Methods based on computational analysis and interaction model construction are gradually developed, such as gene proximity method, gene cluster method, phylogenetic spectrum, network integration, and GO-based association method. The combination of experimental methods and computational methods has further improved the quality and quantity of protein-protein interaction data. At the same time, through the collection and collation of massive data, many different protein interaction retrieval databases have been formed, such as biogrid database, dip database, MIPs database, and mint database. In the process of DKD disease development, complex biological processes involve the interaction and regulation of a variety of proteins. However, the interaction relationship between the existing candidate DKD markers has not been fully explored, which hinders the comprehensive construction of the DKD protein molecular regulatory network. This enables researchers to improve the overall interaction level through experiments and computer simulation. Therefore, it is helpful to elucidate the molecular mechanism of DKD. We have continuously improved the research system of DKD proteomics, which has laid a good technical foundation for the follow-up study of DKD pathogenesis, early diagnostic markers, therapeutic targets, and efficacy evaluation from the level of proteomics.


Mountainous freeways with high bridge and tunnel ratios are a new type of road that rarely contain many special road sections formed by various structures. The crash characteristics of the road are still unclear, but it also provides conditions for studying how various road environments affect traffic. In view of the various structures and differences in the driving environments, a scenario-based discretization method for such a road was established. The traffic-influence areas of elementary and composite structures were proposed and defined. Actual data were analyzed to investigate the crash patterns in an entire freeway and in each special section through statistical and comparative research. The results demonstrate the applicability and validity of this method. The crash rates were found to be the highest in interchange and service areas, lower in ordinary sections, and the lowest in tunnels, being mostly attributed to collisions with fixtures. The crash severity on bridges and bridge groups was significantly higher than that on the other types of road sections, being mostly attributed to single-vehicle crashes. The annual average daily traffic and driving adaptability were found to be related to crashes. The findings shed some light on the road design and traffic management implications for strengthening the traffic safety of mountainous freeways. Mountainous freeways with high bridge and tunnel ratios are becoming more prevalent in the middle and western regions of China owing to the rapid improvement in the economy and the implementation of the national freeway network improvement plan. For example, the Ankang section of the Xihan freeway, which has a total length of 78 km, has a bridge and tunnel ratio as high as 70%. Similarly, bridges and tunnels constitute 63% of the Chongqing section of the Yuxiang freeway, which has a total length of 270 km. This is because tunnels and bridges can not only traverse complex terrains within a short distance but also help improve traffic efficiency and reduce air pollution; they are becoming more common owing to modern construction technologies and lower construction costs. Consequently, long tunnels and bridges are replacing long roadways in mountainous freeways.Owing to their large number of bridges, tunnels, and interchanges, these new mountainous freeways are characterized by frequent cross-section switching, abrupt changes in longitudinal driving environments, and alternating light and dark areas. This makes the entire driving process dynamic and difficult , 2. ThesNumerous crash investigations have been conducted on typical mountainous highways –7. Some Because mountainous freeways with high bridge and tunnel ratios are relatively new, few studies have been conducted on crashes on these types of roads, and the mechanisms are still unclear. Existing research , 15–17 hIn mountainous freeways with high bridge and tunnel ratios, many bridges, tunnels, and necessary interchanges and freeway service areas have to be constructed; therefore, they have a large number of structures, which have external influence areas outside their own physical area, and the driving environment in these influence areas are different from those on ordinary roads. In addition, when the same type or different types of structures interact, they form unique road sections such as bridge groups, tunnel groups, and bridge–tunnel groups. Therefore, compared with ordinary highways, these structures have become major components of such roads, with a significant advantage in terms of length and larger traffic influence areas. More importantly, these structures form a variety of unique road sections, each of which has its own complex traffic operations. Although these small sections create different and independent driving conditions, they coexist within the entire driving process and thus determine the overall safety performance of the road.To investigate the crash patterns for the entire road and each type of road section, in mountainous freeways with high bridge and tunnel ratios, appropriate and accurate discretization methods are critical for exploring the differences in crash mechanisms and determining how these factors are influenced by various road sections. Therefore, this study attempts to first establish a scenario-based discretization method of the road. Based on the specific traffic scene formed by the various types of individual and composite structures, the degree of intervention in driving is comprehensively measured in terms of the driver’s visual needs and adaptations, traffic organization behavior, and road conditions. The traffic impact areas of elementary structures are proposed and determined, and the special sections for composite structures are defined by calculating the critical safety distance index. Finally, with the help of this method, the statistics and comparative analysis of actual crashes in China are obtained and conducted, respectively.The mainline of mountainous freeways with high bridge and tunnel ratios comprises elementary structures including bridges, tunnels, interchanges, and service areas. In addition to having their own specific traffic operating environments, these structures have a certain external influence on areas near entrances and exits. All these areas constitute the scope of special road sections for elementary structures. The scope is defined as the zone that influences traffic—the structure body and its contiguous section that influences a driver’s normal safe driving pattern. This is often due to the road traffic specific to the structure. Although both the structure and its traffic influence area are special sections with different driving environments, the scope of the latter is significantly larger than that of the former. The influence area determines the real boundary of the influence of the structure on the driving conditions, and therefore, it is reasonable to apply it to road discretization. The entrances and exits of different elementary structures have different driving safety characteristics. Therefore, when calculating the external influence distance, it is necessary to consider the following main factors and treat them differently to determine the influence area.TunnelsWhen vehicles approach the entrance of a tunnel during the day, drivers often tend to slow the car down owing to the black hole effect. According to the luminance recommendations as per the highway lighting guidelines , the tunBridgesStop), which is directly related to the driving safety, is considered as the bridge entrance and exit influence distance.On the one hand, bridge decks are very high, which results in the driver having a certain sense that the canyon is deep and that there is a high risk of injuries and casualties in the event of a fall. On the other hand, changes in the visual environment of the bridge entrance and exit are less significant than those of tunnels. The influence on driving is relatively low, which was also demonstrated by the results of a vehicle driving test . ConsideInterchanges and freeway service areasBoth interchanges and service areas have specialized traffic organization sections. Their traffic influence areas are similar and should be determined mainly according to the influence of traffic flow, which is very different from those of bridges and tunnels and is mainly affected by visual characteristics. According to the highway capacity manual (2010), the influence area of a freeway entrance begins and ends 450 m before and 150 m after the exit ramp-way junction, respectively, and that of a freeway exit begins and ends 150 m before and 450 m after the entrance ramp-way junction, respectively. In this study, the affected road sections of the entrance and exit and their connecting section are considered as the traffic influence area of an interchange and service area.When elementary structures are sufficiently close, unique road sections with composite structures are formed by a similar or heterogeneous combination such as tunnel groups, bridge groups, and bridge–tunnel groups. Although existing research has provided definitions for these unique road sections , 15, 23,C) refers to the specific distance between them, which is determined for safety reasons. This distance is the only quantitative basis for judgment. As shown in 1, and S2 are two adjacent structures. If DExit1 is defined as the exit influence distance of the first elementary structure and DEntr.2 the entrance influence distance of the second elementary structure, then LC is equal to the sum of DExit1 and DEntr.2. For different composite structure types and orders of appearance, the values of DExit1 and DEntr.2 are different owing to various factors, and different values of LC can be obtained. If the distance between two adjacent structures (L) is less than LC, there are overlapping areas between the traffic influence areas of the two structures. When driving on these sections, the driving adaptation of S1 will affect the driving process when approaching S2. Hence, on a portion of the connecting section, driving will be affected by the influence of both the structures. Such adjacent structures have a compound influence on driving safety; thus, they should be treated as a composite structure instead of two single structures.When defining a unique road section with composite structures, the safety-critical distance  should be more carefully designed, focusing on road alignment. Generally, because the complex alignment of mountainous freeways with high bridge and tunnel ratios cannot be realized in tunnels with high alignment requirements, most of the open sections inevitably become alignment adjustment sections or special sections such as sections with long and steep slopes and small radius curves. Therefore, the road alignment of these sections is complex, which results in frequent crashes. Third, on a whole, frequent multi-vehicle crashes indicate that driver assistance systems may be an effective solution to solve this problem. Meanwhile, for general open sections, the high number of single vehicle crashes shows that speed management systems such as variable speed systems need to be considered. These systems have delivered promising results in reducing crashes –26. FinaSeveral limitations should be noted for this study. Because of the limitation of crash data sources, this study only analyzes crashes that occurred between 2014 and 2016 on a mountainous freeway with a high bridge and tunnel ratio. Mountainous freeways with high bridge and tunnel ratios are unique roads with many road environments and complex geometrical road alignments, in which a crash is a result of the coupling effect of the two factors . However


Electromyography (EMG) signals are biomedical signals that measure electrical currents generated during muscle contraction. These signals are strongly influenced by physiological and anatomical characteristics of the muscles and represent the neuromuscular activities of the human body. The evolution of EMG analysis and acquisition techniques makes this technology more reliable for production engineering applications, overcoming some of its inherent issues. Taking as an example, the fatigue monitoring of workers as well as enriched human–machine interaction (HMI) systems used in collaborative tasks are now possible with this technology. The main objective of this research is to evaluate the current implementation of EMG technology within production engineering, its weaknesses, opportunities, and synergies with other technologies, with the aim of developing more natural and efficient HMI systems that could improve the safety and productivity within production environments. Electromyographic (EMG) signals are biomedical signals that measure the electrical current generated during muscle contractions. These signals represent neuromuscular activity and are dependent on the anatomical and physiological characteristics of the muscle. Nowadays, EMG has many applications, including the analysis of neuromusculoskeletal diseases, orthosis control, or ergonomic assessment. However, EMG technologies present some limitations that prevent their use beyond research and clinical environments.The evolution of EMG signal analysis techniques combined with the development of new instrumentation has great potential in production engineering: fatigue monitoring of workers—based on live musculoskeletal risk assessment associated with an activity—or the development of new human–machine interaction systems, including remote control of robotic arms and other industrial equipment or the prediction of movements of workers, are now feasible technologies. There are still many challenges to overcome for the effective use of this technology to offer new possibilities in the future. The digital development of the industry, which is now capable of generating and processing an enormous amount of data, will undoubtedly be benefited from techniques such as massive data analysis and artificial intelligence, which when applied to the analysis of biomedical signals will transform the human being into an active element of the production processes in an evolved and completely different way from what is known.In this paper, a state-of-the-art review of current applications of EMG techniques applied to production engineering is presented. After analyzing EMG signal acquisition and processing techniques, successful production engineering EMG cases of use are reviewed. Trends, synergies with other technologies, opportunities, and limitations are identified, establishing a compendium of knowledge to allow the improvement of safety and productivity within production environments.animal electricity”, which refers to the generation of electricity within the body that is subsequently channeled through the nervous tissue [De Viribus Electricitatis [The first reference of muscle generated electricity dates from 1666; Francesco Redi suspected that the discharge produced by an electric fish was of muscular origin. However, it was not until 1791 that Luiggi Galvani 1737–1798), professor of anatomy at the University of Bologna, recognized the relationship between the electrical stimulation of a nerve and the contraction that is generated in a muscle [s tissue  and a co7–1798, picitatis . In 1838Elektrophysiologie menschlicher Muskeln”.The evolution of electromyography has been possible due to the evolution of acquisition equipment and processing techniques. Ferdinand Braun developed in 1897 cathode-ray tubes, which were used with a string galvanometer by Forbes and Thatcher in 1920  to measuEdgar Douglas Adrian 1889–1977), the winner of the Nobel Prize in Medicine in 1932 for his discoveries related to the functioning of neurons, collaborated with the American physiologist Detlef Bronk in an experiment to quantify the action generated by a single nerve fiber. They used a needle electrode and a speaker to record the electrical activity of muscle fibers that are generated through a single nerve fiber, which would later be called "Motor Potential Unit". The electromyogram obtained from a human tricep is composed of “rhythmic discharges of different groups of muscle fibers” [7, the wiFritz Buchthal (1907–2003) developed a microelectrode in 1934 to record the potential of isolated muscle fibers. One of his greatest achievements was to develop the normative data used today regarding the amplitude and duration of motor potential units in different muscles and ages between the years 1954 and 1955. Edward H. Lambert (1915–2003) established the first clinical electromyography laboratory in the United States in 1943 upon joining the Mayo Clinic after earning his PhD from the University of Illinois. Years later and after numerous studies, James A. Fizzel, Herbert Jasper, and James Golshet designed and developed the first commercially viable electromyography equipment for medical use in 1948.The electromyography is rapidly adapted to the clinical field, so standardization and improved communication between researchers and professionals in the sector is necessary. In August 1953, the American Association of Electromyography and Electrodiagnosis was created in Chicago, developing the first scientific seminar in September 1954 [Erik Stålberg, the author of the book Single Fiber Electromyography  in 1979,The development of electromyography has been possible thanks to the contribution of many scientists and professionals from different fields, both clinical and engineering. Although its foundations have been known for many years, the evolution it has had in the last century has been possible thanks to the technological advancement of instrumentation, which still has several drawbacks that make it difficult to use. Sensors, data processing, and other features will need to continue evolving so that in the future, we can see this technology fully integrated into society and industry.Muscle contraction is the physiological process by which a muscle shortens or relaxes due to the sliding of the internal structures that compose it. An action potential is a wave of an electrical discharge that passes through a cell membrane and modifies its polarity by altering the concentration of sodium, potassium, and calcium ions inside and outside the cells. In general, the muscle contraction process begins with the action potential initiated at the neuromuscular junction, which spreads to the muscle fibers and T-tubules.During an action potential, the inflow of sodium causes the depolarizing phase of an action potential, whilst an outflow of potassium ions causes its repolarizing. The inflow of sodium within a cell changes the membrane potential during an action potential from −55 to +30 mV. Calcium required to activate contractile proteins is stored in bag-shaped structures called sarcoplasmic reticules, whose function is to measure the concentration of calcium ions in the sarcolemma through its release. When crossing an action potential, the T-tubules produce an interaction with the sarcoplasmic reticulum, which produces a release of calcium ions and initiates the interaction between myosin and actin, finally producing muscle contraction ,10.Muscle contractions include four different phases: ATP hydrolysis that reorients and energizes the myosin head, the attachment of myosin to actin to form cross-bridges, the power stroke produced as the cross-bridges rotates toward the center of the sarcomere and the detachment of myosin from actin after the power stroke .The action potential of a muscle fiber (MFAP) is the fundamental component of an electromyographic signal. It corresponds to the potential action that crosses a muscle fiber that belongs to a motor unit (MU). The characteristics of the MFAP will depend on the diameter of the fiber, the conduction speed, and its relative position with respect to the electrode that acquires the signal .A group of muscle fibers innervated by a single motor neuron is called a motor unit, being the minimum functional unit that can be controlled by neuronal action. Muscle force is produced by activating the motor neuron, which causes tension in the associated fibers and produces a muscle contraction. When the activity of the motor neuron ceases, muscle relaxation occurs.s generated from a single MU is noted as motor unit action potential train, MUAPT. The EMG signal obtained from a single electrode in a single contraction could be represented as follows [The motor unit action potential (MUAP) is the sum of the action potential of muscle fiber contractions in a motor unit, which can be measured by surface electrodes or sensors. The voltage detected by the sensors represents the summation of the activity of all active motor units. Those MU with a closer proximity to the electrode will be detected easily, causing attenuation and greater noise in more distant MU. The aggrupation of MUAP follows :EMGt = ∑m of N motor units that contributes to the signal. The function n(t) represents all the components of the signal not associated with the motor unit potential, being environmental and biological noises. The characteristics of this signal are highly dependent on the level and duration of contraction, dynamic, and static state of the muscle, fatigue, and skin sweat. Using needle electrodes that can be placed close to a single muscle fiber, the electrical activity of that fiber can be detected, identifying the activity of a single motor unit. On the contrary, using surface electrodes, the detected signal will be composed of MUAPTs of various motor units, not being able to discriminate the origin of each signal component.In this expression, The acquisition of EMG signals is carried out by electrodes that acquire the electrical potential of muscles during contraction and are positioned in contact with muscle fibers or the skin. Two general types of electrodes could be identified: surface and needle electrodes.Needle electrodes could acquire EMG signals from a single muscle fiber with high precision, but their invasive nature is a major drawback, making them unviable for any commercial application. Surface electrodes acquire EMG signals from several motor units, obtaining noisy signals. Two types of surface electrodes could be identified: dry and wet electrodes. Wet electrodes require a layer of conductive gel between the skin and the metal part, while surface electrodes will be in direct contact with the skin. Both types of electrodes can achieve equivalent signal quality , which mDifferent electrode configurations are considered for the acquisition of EMG signals: monopolar, bipolar, and Laplacian . The LapElectromyography technology has some limitations that prevent its full implementation in applications beyond the clinical field, such as industry. The equipment used for the acquisition of EMG signals, especially in real-time commercial applications, needs to fulfill some important characteristics: to be simple, intuitive, portable, cheap, reliable, and robust .One of the most recurrent problems is the displacement or loss of the electrode from the analyzed muscle, which causes noise or the total loss of the signal. In this regard, needle sensors are not appropriate for commercial applications, as they cause discomfort to the user despite avoiding the displacement of the sensors. New high-density surface electromyography sensors, HD-sEMG, have been developed for signal acquisition. The use of these sensors allows the processing of the entire surface of the interaction between muscle and skin, so the noise caused by its displacement during use can be solved by reconstructing the interaction map using algorithms.A displacement in the position of the electrodes causes a considerable reduction in the performance of the identification system, as the new signals obtained from the sensors are different from those used during the learning process. To solve this problem, different alternatives have been evaluated from the point of view of data processing such as using data from displaced sites in the learning process  or evaluIn addition to a possible displacement of the sensor during use, one of the existing limitations is the required contact of the electrode with the skin. The development of integrated sensors in textile products for the acquisition of biomedical signals allows the acquisition of data in a more realistic, casual, and outside laboratory conditions . As an eThe objective of processing and segmenting the EMG signal is to adapt it for later analysis, eliminating the noise that might lead to wrong interpretations.Ratio and resolution should be considered in EMG signal sampling . Most ofThe use of filters is one of the most effective methods to reduce the noise of a signal, improving its fidelity. High and low-pass filters are used to process EMG signals, allowing the low and high-frequency components to be attenuated, respectively. De Luca et al.  evaluateIn pattern classification applications, feature vectors obtained from EMG signals are used as input for classification algorithms. In continuous classification algorithms, adjacent or overlapping segments are used, and therefore, the performance of the system will be affected by window length and allowable delay . Window Once the raw signal obtained from electromyographic sensors has been processed, the next step will be to obtain different features, identifying useful information that is hidden in EMG signals and removing interference or other irrelevant information .The extracted features could be classified in time or frequency domains. Time-domain features represent the energy of an EMG signal, which generally requires low computational load. On the other hand, the features of the frequency domain, in addition to showing the level of activation of the muscles, have the ability to eliminate signal noise but have a higher computational cost . A combiSignals are segmented to extract features of the time domain. Using high segment lengths requires high computational load, while low segment lengths cause biases in the feature extraction. Whilst real-time activities are processed, segments greater than 200 milliseconds require some overlap . Some ofThere is some redundancy in some parameters of time and frequency domains. To avoid data redundancy, Phinyomark et al.  evaluateFor certain applications, it is convenient to use complementary parameters to validate or complement electromyographic signals. The acquisition of these parameters will be carried out through sensors of a different nature placed on the human body, such as inertia or force sensors. The combination of signals makes it possible to overcome some of the obstacles encountered in electromyography applications, improving performance and robustness in processes such as motion prediction .The use of inertial sensors for the acquisition of movements represents a reliable alternative to the classical systems of optical classification of movements, being able to reconstruct the movement of various body segments . IMU senThe use of IMU has some limitations, such as accumulated error, magnetic disturbances, and the need to be calibrated before use. FSR sensors are used to detect pressure, slip, and weight change. The use of FSR sensors represents a prevalent method for identifying gait phases, presenting fewer errors than other alternative systems. Using two sensors on each foot, which will be placed on the heel and toe, the weight distributions can be analyzed during a gait cycle.Nazmi et al.  use the Electroencephalographic signals, also called EEG, represent brain bioelectric activity and are related to neuronal activity produced within the brain. There is a variation in the functional integration of brain and muscles when there is a movement intention . HoweverLange et al.  develop An electrocardiogram, identified as an ECG, is the graphical representation of the electrical activity of the heart as a function of time. Although the activity of the heart usually presents several drawbacks in the acquisition of electromyographic signals due to their overlap, there are procedures to eliminate the noise produced . One of ) and Principal Component Analysis (PCA), are simpler to implement and require less learning time, which makes them good candidates for real-time application classifiers [The information extracted from the EMG signals and other parameters is fed into a classifier, which will be used to identify patterns and match results. Several classification methods are used for the analysis of EMG signals. Statistical classifiers, such as Linear Discriminant Analysis ; in 1993, Hudgins et al.  proposesAlthough neural networks applied to electromyography are usually applied in movements and gestures classification, other applications could be found, such as the identification of an ergonomic index related to the user activities. Varrecchia et al.  analyze Deep Neural Networks, DNN, are multilayer perceptron with a complex structure of layers. Its fundamental difference with ANN networks lies into the complexity of connecting each layer and a greater number of neurons, which is why high computing power is required to process complex problems . MukhopaCNN, which has a structure adapted to process matrix data, making it especially useful for image processing [One of the most widely used deep networks is the Convolutional Neural Network, ocessing . The useocessing  use a coExtreme Learning Machines consists of a network with a single layer connected with random weights to the input layer. Developed by Huang et al. ,69, ELM Freitas et al.  evaluateSupport Vector Machines are originally developed by Bernhard et al.  and CortHassan et al.  used a MEisenberg et al.  evaluateProduction engineering is the field of engineering that focuses on the manufacturing processes and activities required for the transformation of raw materials into industrial and consumer goods. To identify the state-of-the-art of EMG applications in production engineering, a systematic review was conducted. See electromyography, EMG, sEMG; and from production engineering: production engineering, manufacturing, industry. The articles identified through database searching include selected keywords from areas in the title, keywords, or abstract. The search was limited to papers published in journals, books, and conference proceedings.The literature search was performed between 2015 and 2020 on the following databases: Scopus, SpringerLink, IEEE Xplore, and ScienceDirect. The following keywords were identified from electromyography: n = 868), duplicates were removed (n = 86), and a first screening was conducted to evaluate title and abstract (n = 782). The full-text reading was done after the first screening for articles that offered valuable information in the title and abstract. Articles and reviews that present real cases of use in production engineering, whether real or simulated, are selected (n = 49). Discarded articles could be classified as Healthcare if they evaluate the development of prosthesis and orthosis, augmentation exoskeletons for rehabilitation or other purposes; Acquisition was used for those analyzing sensor and electrodes technologies. Processing articles investigate about feature selection, classification algorithms, and methods, and finally, Ergonomic articles are those assessing ergonomics performance, muscle fatigue, and kinematics estimations not applied to production engineering. Discarded articles classified as Others could not be classified in any other category and do not involve an EMG or production engineering research topic. Articles that cannot be accessed are also classified under this category.After identifying articles from the selected databases , and Monitoring. Results classified as Ergonomics include ergonomic assessments of activities, workstations, productive structures, tools, PPEs , or exoskeletons carried out using EMG technologies. HMI systems acquire, process, and extract EMG signals to translate this information into control signals of industrial equipment. Finally, monitoring processes allow identifying activity, conditions, and capacities of workers in real-time. The articles analyzed are presented in The main objective of industrial ergonomics is to optimize industrial workers well-being, ensuring optimal mental and physical conditions to improve overall system performance, reducing risks and improving the quality of working life. EMG could be applied to assess the ergonomic performance of the workplace, activity, tool, PPE, device, and the impact of the use of exoskeletons in the activities of the worker; it is designed to improve the capabilities of a worker in carrying out their daily activities, improving the ergonomics of their workplace, and reducing the muscle fatigue. Ergonomic assessments allow increasing efficiency, productivity, and quality in the industry, making more cost-efficient processes .The main EMG features utilized for the ergonomic assessment of activities in production engineering are RMS, MNF and %MVC (Maximum Voluntary Contraction). Those features are analyzed using statistical techniques such as ANOVA, t-tests, or post-hoc Tukey’s HSD (Honest Significant Differences) to compare the reduction of muscular effort and muscular fatigue. Furthermore, most of the EMG ergonomic assessment studies analyzed are carried out in simulated environments and have a short duration, not comparable to a work shift. See The ergonomic assessment of activities using electromyography technologies in the industry is mainly focused on the assessment of muscle load, fatigue, and musculoskeletal risks on activities carried out within the aerospace and automotive sectors.Ergonomic assessment processes are regulated by national and international regulations, which establishes a common criterion to identify the risk associated with an activity. A typical example is the NIOSH equation , which wClip insertion is a frequent activity in the assembly processes of the automotive industry. These small, variously shaped components made of metal, plastic, or composite material can be inserted automatically or manually. Gaudez et al.  evaluateSoewardi et al.  analyze Overhead activities are widely performed in production environments and are a frequent source of shoulder injuries. These types of activities are identified by requiring the position of hands above the acromion or over 60° shoulder flexion or abduction. Maciukiewicz et al.  comparedGrazioso et al.  analyze Chen et al.  evaluateInjuries and pain in the shoulder could be produced because of the exposure to pushing and pulling activities. Gruevski et al.  analyze Carpal tunnel syndrome is a condition in which there is excessive pressure on the median nerve of the wrist, which can cause numbness, weakness, and muscle damage in hand and fingers. This syndrome can be caused by repetitive movements of the hand and wrist, as well as the use of vibrating hand tools. Kumar et al.  compare Guo et al.  assess mSeveral outdoors and indoors works are exposed to high and low ambient temperatures, which may affect the performance of manual tasks. Renberg et al.  evaluatePlant layout is a critical aspect of the efficiency of an industry. This distribution affects all areas, including production lines or different product warehouses. Furthermore, the physical distribution of the workstation has a great impact on a worker’s day-to-day life. Good workstation design reduces the muscular loads and fatigue in operations that are carried out daily. The use of CAD/CAE tools for workstation design and postural assessment could be used to reduce musculoskeletal injuries in productive processes . The useOne of the parameters that affects the configuration of a workstation is the position and height of parts and tools containers. Lee et al.  analyze Antle et al.  conducteElectromyography could also be used to identify how productive planning affects the physical condition and productivity of workers. A simple modification on the rotation of activities, diversification of jobs, and the increase of rests periods is widely accepted as a good practice to improve the health of workers without significantly altering the production process .Santos et al.  evaluateBergamin et al.  investigIt can be verified that electromyography is an effective tool to ergonomically evaluate the production organization and identify the best possible configuration, being able to improve the health conditions and worker performance.One of the objectives of the ergonomic evaluation carried out by electromyography is to evaluate the impact of the use of new tools, equipment, or other elements whose objective is to improve the health and safety conditions of workers. The correct identification of the outcome produced on the workers allows selecting the optimal tools or equipment for the production process. Reinvee et al.  analyze flexor digitorum, deltoid, and upper trapezius with a force sensor located on the handle and the worker´s pulse. Using genetic algorithms based on the obtained data from different subjects, the optimal handle height that causes the minimum compression loads in the L5/S1 vertebral segment turns out to be 109 centimeters. Previous studies had estimated an optimal range for similar conditions between 91 and 112 centimeters. Rashid et al. [Wilson et al.  measure extensor digitorum communis, related to the grip strength of the hand.The hot forging industry has work environments that are very harmful to workers health, such as noise, vibrations, and high temperatures, so workplace incidents occur frequently. Existing industrial processes in this sector cause great fatigue in workers, which is generally due to the transport of heavy products. Song et al.  had deveThe use of safety footwear is necessary for the production area of any industrial plant, so in addition to safety, comfort during its use is a fundamental factor that must be considered. Huebner et al.  analyzesExoskeletons are devices used in the industry to improve the capabilities of workers, reducing the physical workload required on their activities. According to energy utilization, two different types of exoskeletons could be identified: active or passive. The difference between them is that active exoskeletons use powered actuators, such as electrical, pneumatic, or hydraulic, whilst passive use flexible materials that can store and transmit energy, such as springs. Viable prototypes are beginning to be developed and are suitable to be implemented in a production system, as evidenced by the results obtained from ergonomic assessments during their use.Bosch et al.  analyze Lee et al.  evaluateA comparison between three upper-limb passive exoskeletons is carried out by Alabdulkarim et al. , analyziThe Hyundai Vest Exoskeleton (H-Vest), a light-weight passive upper arm exoskeleton for overhead tasks, is introduced by Hyun et al. . The proChen et al.  develop Ogawa et al.  designedThakur et al.  propose Otten et al.  evaluateThe use of industrial exoskeletons reduces muscle activity and risks of developing musculoskeletal diseases among workers, but there is no standardized methodology to compare their effectivity. Grazi et al.  propose A human–machine interaction (HMI) system must be able to acquire, process, and extract data from electromyography signals. Afterwards, these data should be translated into control signals that will be used to adapt the operation of industrial equipment. Within this sector, it is possible to identify two clear sections: myoelectric control systems and human–machine cooperation systems. The first group seeks to replace the traditional equipment control channels through new interfaces based on EMG, providing a more intuitive and effective control system. The analyzed experimental studies are carried out in simulated environments and combine EMG signals with other control systems and equipment, such as the HoloLens VR Kit  or an inOn the other hand, HMI cooperation systems use EMG to integrate the human body into the production system, being able to provide information on the movement or muscular fatigue to optimize a collaborative activity. Some examples of the collaborative activities analyzed are supervisory control, fatigue adaptation, hand-over activities, and motion prediction. Artificial intelligence processing techniques are widely used to classify information obtained from EMG acquisition systems, which can be combined with ECG signals  or augmeThe development of natural control strategies represents one of the significant challenges of human–machine interface design. In this sense, myoelectric control seeks to replace traditional control channels through new interfaces based on electromyography, being able to provide a more intuitive and efficient control system integrated with the user’s movements. See Being able to control a robot remotely is crucial in industrial environments where the physical integrity of the worker is at risk. Furthermore, the use of technologies such as virtual reality allows the operator to interact in different ways with the environment in real time. Wu et al.  explore Some of the gripping devices used in the industry have unintuitive control systems that have no relationship between the handheld system and the action produced. Thus, a tailored control system could improve the overall usability of the system. Meattini et al.  explore One of the limitations of the implementation of electromyography in industrial environments is the displacement of the electrodes in workers due to their activities. Fu et al.  evaluateDespite having achieved great advances in the laboratory, the real applications of pattern recognition for myographic control do not have significant improvements. One of the reasons behind this limitation is the lack of two-way interaction between the user and the device. In the extremities of the human body, touch works as a feedback loop that helps control muscle contraction during movement . In the With the automation of production processes, it becomes necessary that an effective human–machine communication system is integrated, allowing workers to interact efficiently and safely with industrial equipment. The cooperation between people and robots allows combining the cognitive capacity of the human being with the physical power of a machine , defininThe collaborative manipulation of tools requires an adaptive two-way interaction between human and machine, understanding the intentions of the collaborator and being able to anticipate their movements, as well as communicate autonomously and adapt to the requirements of the activity. Wang et al.  analyze Peternel et al.  develop Similarly, Huang et al.  conducteThe use of biosignals such as electromyography (EMG) and electroencephalography (ECG) can be used to anticipate human intention in a collaborative environment. One of the most significant uses of these signals is the estimation of the physical and mental state of the user, being able to evaluate muscle fatigue, inattention, or anxiety .DelPreto et al.  use a miIt seems clear that the development of this technology can be applied in the industrial field to improve worker safety in an automated environment and can also modify the operating modes of each equipment.Knowing the activity, conditions, and capabilities of the worker during the performance of their work allows the optimization of a production process, evaluating the performance of the worker and avoiding or preventing musculoskeletal risks. The analyzed experimental studies require both muscular assessment and motion reconstruction, so EMG acquisition techniques are often combined with optical or inertial motion analysis systems, such as the SMART-DX 6000 System   or ViconOccupational musculoskeletal injuries are often caused by common movements, such as lifting weights, grasping movements, or intense typing, which can become risky movements if performed too frequently and without rest  system must be able to acquire, process, and extract data from electromyography signals. These signals, combined with other useful information, is used to generate controls signals to adapt the operation of industrial equipment. Myoelectric control systems seek to replace or complement traditional equipment through new interfaces based on EMG. For example, the remote control of robots is crucial in industrial environments where the physical integrity of the worker is at risk. HMI cooperation systems aim to optimize collaborative activities. With the automation of production processes, it becomes necessary that an effective human–machine communication system is integrated, allowing workers to interact efficiently and safely with industrial equipment. The use of EMG signals and other useful information allows the supervision of robots, motion prediction, or live adaptation to workers capabilities, combining the cognitive capacity of workers and physical power of robots.Finally, monitoring applications of EMG intend to identify the activity, conditions, and capabilities of the worker during the performance of their work. Generally, muscular assessment will be complemented with motion reconstruction techniques, whether optical or inertial. Muscle fatigue, workers activities, and musculoskeletal risks could be monitored using the combination of these technologies.EMG ergonomics applications are the most widely and successfully used in industry, as laboratory conditions could be replicated in the industrial environment. Its success relies on the possibility of obtaining conclusive results without affecting workers continuously. Under laboratory conditions, different monitoring and HMI applications could achieve more than 95% of accuracy in real-time classification applications but are not suitable for daily use within an industrial environment. Some of those limitations that prevent the daily use of EMG technologies are the placement of sensors, skin preparation, or training periods between use. New technological development in acquisition methods, with higher usability compared to traditional wet electrodes, will allow using EMG in an industrial environment overcoming some of its major drawbacks.Most of the production engineering EMG applications reviewed in this paper are carried out in simulated environments and have a short duration that is not comparable to a work shift. Major drawbacks could appear using current acquisition systems daily in real environments. The continuous use of acquisition systems might be necessary for some of the analyzed HMI and monitoring applications, and most of the current EMG acquisition techniques require skin preparation to improve the signal. New data acquisition techniques and tools should be applied to reduce issues related to sensor misplacements and daily setups, such as higher-density EMG sensors embedded in Personal Protective Equipment.Furthermore, reviewed investigations tend to use a limited number of subjects and laboratory conditions; thus, it will be interesting to test a higher number of users and develop new data processing techniques adapted to an industrial environment, identifying if a setup is feasible and improving the generalization capability of classification methods. Decentralized cloud data processing could be used to integrate different biological signals, such as ECG, EEG, and EMG, with personal IMU signals of all workers from an industrial area, as it has been identified that its combination improves the accuracy of classification methods, and the availability of the data could be used to develop new interesting technologies.Synergies between EMG industrial applications have been identified, such as the combination of real-time fatigue monitoring, musculoskeletal risk assessment, and assisted handling devices. Different investigations carried out in those EMG applications obtain high reliability under laboratory conditions and use EMG data obtained from the same muscles. To analyze the future integration of EMG applications, it is interesting to analyze the reliability of different EMG applications with the same data, so that a common data source could be used for different purposes.


The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial.One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject.We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration. It is estimated that one quarter of the world population is infected with (TB). Although the disease is preventable and treatable, about one and a half million people die annually from it, effectively placing TB as the first infectious cause of death. Due to person to person infection and treatment mismanagement, (MDR) TB continues to emerge, increasing the complexity in treatment and thus potentially worsening the transmission rate. There is a growing awareness that TB can be effectively fought only working globally, starting from countries like India, where the infection is endemic .Once a person is diagnosed with TB, one of the most critical issues is the duration of the therapy, because of the high costs involved, the increased chances of non-compliance (which increase the probability of developing an MDR strain), and the time the patient is still infectious to others. One exciting possibility to shorten the duration of the therapy are novel host-reaction therapies (HRT), as an adjuvant for antibiotic therapy. Typical endpoints in the clinical trials for HRTs are time to sputum culture conversion, and incidence of recurrence. While for the first it is in some cases possible to have a statistically powered evidence for efficacy in a phase II clinical trial, recurrence almost always requires a phase III clinical trial with thousands of patients involved, and huge costs.® antitubercular vaccine, provided by Archivel Farma S.L, is a polyantigenic liposomal vaccine containing fragments of Mycobacterium tuberculosis cells, currently being developed as therapeutic vaccine in patients with pulmonary tuberculosis. The vaccine, shown to be one of the most advanced therapeutic vaccines against drug sensitive TB and MDR-TB, has already been studied in healthy volunteers and for the prevention of active TB in patients with latent TB [The in silico trials for tuberculosis vaccine development (STriTuVaD) project is an EU funded, multidisciplinary consortium testing the RUTI vaccine in a Phase IIb clinical trial. RUTIatent TB .To help in this development, we extend Universal Immune System Simulator (UISS) , 4 to inTo reproduce biological the diversity of the subjects to be simulated, an appropriate strategy for the generation of libraries of digital patients is developed by identifying a vector of features involving both biological and pathophysiological parameters, facilitating the personalisation of the digital patient.In this paper we sketch the strategy we adopt to generate the cohort of digital patients, and show some preliminary results about the dynamics of TB on a subset of these patients. First, we briefly describe UISS and its extension to TB.We will briefly describe here the UISS computational framework and its extension to model tuberculosis, UISS-TB. The interested reader can find more detail in .UISS is a multi-agent framework for the simulation of the immune system dynamics that can be extended to track specific diseases and related treatments. Unlike classical top-down approaches, where mean behaviours are modelled through systems of differential equations –8, agentUISS has a proven track record, for instance it has been used for modelling the effects of a vaccine against the onset of mammary carcinoma , 10 and In order to depict individuals, a vector of features comprising biological and pathophysiological parameters has been identified. The list of parameters, their relative range and units are displayed in Table  In order to create an in silico patient, one needs to provide a single value for each feature. These values could be taken from individual physical patients; however, if a cohort of digital patients is to be produced, one should have a mechanism for producing as many different input vectors as needed, that are biological/physiological plausible. Formally, this requires the characterisation of the joint distribution of the inputs in the population. We have compiled typical values and standard deviations for each feature, providing a way to generate plausible values for each component at a time. Proceeding in this way would neglect the biological correlations between features and thus would not guarantee a physiologically plausible input vector. Hence, we must take into account these correlations. Given that we have 22 input variables, we should specify In theory, one could elicit the joint distribution of the features vector, i.e. describe mathematically how each feature relates to the others in a space of 22 dimensions; but this would be not only extremely difficult, but also time consuming and data demanding. Our approach is to rely on current mathematical biology consensus and use a Gaussian to represent the population distribution. The additional advantage of using this approach will be discussed in the next section.d-variate Gaussian distribution with joint probability density function,Formally, we say that the vector The elements in the diagonal, Once We can go further and sort the list of features according to either their importance in determining the profile of a patient, or to the precision of their elicited mean, variance and covariance, and then proceed to sample from the conditional distributions. In general, let The conditional distribution, www.strituvad.eu (accessed on 28/07/20), by selecting the Tuberculosis disease model, hence accessible to any user with a conventional computer and access to the internet.We created an R script  for the The GUI panel displays default values and admissible ranges for the vector of features parameters. Once the specific vector of features is completed, the user can click on the Submit button and a unique identification simulation number is assigned. The user can check the simulation status by clicking on the check status button, after selecting the appropriate simulation id. When the simulation is complete, the user can visualise results of immune system dynamics. In our case, the progression of each patient was simulated 50 times for 1 year, with levels of the various species recorded every 600 seconds. The data from each patient requires roughly 100 MB of disk storage.We use the total (Ab) to exemplify some characterisation of the output; e.g. Fig. UISS-TB is a state-of-the-art agent based model capable of tracking the dynamics of TB infection in humans. Individual digital patients are defined by a vector features, known to be fundamental in TB infection dynamics and normally measured clinically, hence often readily available.In order to produce virtual cohorts of patients, we propose a sequential approach based on a characterisation of the distribution of these features in the population of interest; the approach allows to fix any combination of features, enabling mimicking patient selection criteria, thus yielding a method for setting up augmented in silico clinical trials.Additional file 1: Profile traces.


Recent human imaging studies have revealed the involvement of the secondary somatosensory cortex (SII) in processes that require high-level information integration, such as self-consciousness, social relations, whole body representation, and metaphorical extrapolations. These functions are far beyond its known role in the formation of body maps (even in their most complex forms), requiring the integration of different information modalities in addition to somatosensory information. However, no evidence of such complex processing seems to have been detected at the neuronal level in animal experiments, which would constitute a major discrepancy between human and non-human animals. This article scrutinizes this gap, introducing experimental evidence of human and non-human primates’ SII functions set in context with their evolutionary significance and mechanisms, functionally situating the human SII as a primate brain. Based on the presented data, a new concept of a somatocentric holistic self is proposed, represented as a more comprehensive body-in-the-world map in the primate SII, taking into account evolutionary aspects that characterize the human SII and its implication in the emergence of self-consciousness. Finally, the idea of projection is introduced from the viewpoint of cognitive science, providing a logical explanation to bridge this gap between observed behavior and neurophysiological data. The position of the body is perceived in reference to external objects, such as the monitor and the mouse in the surrounding environment. In other words, objects in the environment that exist independently of the individual are adopted as references when recognizing one's own body position in the frame of the external world—an antithesis of the commonly studied body-centered representation of the world—to produce bodily actions in the environment. The second important message is that the self and its physical body are recognized as a coherent whole. Although parts of the body, such as the trunk, arms, and legs, are manipulated separately to describe the bodily position, collectively, perhaps together with proprioception and even with visceral sensory inputs, they constitute the information of the body, which in turn produces a sense of the body as this coherent whole. This sense cannot merely be a somatotopic representation of the body (body maps) in the primary somatosensory cortex, but rather a mechanism beyond that, allowing the supervision and evaluation of these maps.Where are we? Where is our body located? The answer may be self-evident to laypeople. For example, a person may describe oneself as being in an office, sitting stably on a chair in front of a computer monitor displaying this article. The description may continue, describing the person’s right hand holding the mouse, the right foot crossing over the left. The answer to these questions deliver two important messages to neuroscientists. One is that the body is assimilated in the worldLocated in the region of the parietal operculum, in the upper bank of the lateral sulcus adjacent to the primary somatosensory cortex, the secondary somatosensory cortex (SII) is known to respond to somatosensory , but researchers have not yet developed theories that could unfold the principles of sense of the body information processing and phenomena. The current rising notion of projection from the field of cognitive science might provide clues to understanding this sense and help avoid falling on an infinite regress fallacy of increasingly complex supervising layers. The notion of projection, in short, is an ideal mental function that maps  contents of the internal model of the self and the perceived world onto actual physical environmental worlds . However, neural representations at SI do not code relational information linking the body and the world. Thus, to make the projection possible and create the body-in-the-world representation, neural representation at SI must be incorporated with other representations that code information about the external world. Candidate representations to be incorporated are visual, auditory, and vestibular ones that code environmental information as well as relations between the self and environment. SII is one of the regions that receives inputs from all of these representations. More importantly, we found clear evidence that neurons in SII respond not only to tactile information, but also to visual and vestibular information.In this article, we try to integrate theoretical and empirical frameworks in a complementary manner, proposing a novel concept on the function of the primate secondary somatosensory cortex, which has neither been sufficiently studied nor fully understood until now. In this attempt, we first overview the current status of experimental evidence on human and non-human primates’ SIIs to expound the discrepancy between current experimental results “”. FollowLateral to the primary somatotopic representation in the postcentral somatosensory cortex (SI), the upper bank of the lateral sulcus of the primate brain was believed to possess an additional body map  covering more than one body part as well as both sides of the body. Although these results suggest that SII is engaged in more complex information processing than SI, its functional roles (besides the integration of the tactile information needed for object recognition) remain to be clarified. Hsiao  investigRomo and de Lafuente  analyzedIn humans, skin palpation activates the contralateral SI and bilateral parietal operculum including SII, but regions that contribute particularly to tactile perception are undetermined. Preusser et al.  showed, In human brain imaging studies, SII activation during motor execution may have a role in sensorimotor integration. Binkofski et al.  demonstrSII functions on sensorimotor integration using macaque monkeys have been rarely attempted. Fitzgerald et al.  observedIn spite of SII somatotopy including neurons with large RFs covering more than one body part and both sides of the body, the above-mentioned functional neurophysiological studies using awake monkeys focused solely on tactile perception by hands or sensorimotor integration during object manipulation with hands or mouth. This leaves unexplored the potential SII functions of neurons with large and bilateral RFs, in which information from other sensory modalities could be potentially integrated  visual stimuli needed to be examined in consciously behaving monkeys. However, it has been generally believed that the monkey SII is devoid of visual input  is the morphological plasticity of monkey SII in relation to tool-use learning. MRIs and voxel-based morphometry (VBM) were performed to detect changes in the brain structure of three adult Japanese macaques trained to use a rake , in which larger brains are structurally analogous to smaller ones , unsurprisingly allow more plastic adaptability over rodents. In situ hybridization of cadherin family molecules (cell adhesion molecules involved in specific synapse formation and function) was compared between developing marmosets and mice , which was recently reported to function in axon regeneration and neuroprotection. Through a comparative analysis, periostin was expressed at higher rates in primates than in mice, in which overexpression induced neurite outgrowth. The results suggest that prolonged and increased periostin expression in the primate cerebral cortex enhances cortical plasticity beyond the typically observed in mammals  space. Indeed, the middle temporal visual area (MT/V5) is particularly evolved in the primate brain, which is crucial for 3D motion processing. As an extension to the studies depicted in the previous section, shifts in cadherin expression patterns were detected between pulvino-MT and lateral geniculate nucleus (LGN)-V1 pathways and in the MT/V5 around the time of birth, which preceded the development of V1-MT connections, suggesting that a change in cadherin expression around the lateral sulcus may be a general mechanism to control neural plasticity in regard to higher cognitive abilities  species with short life spans and mass reproduction adapt to the environment through variations in their numerous offspring, expecting at least a few to survive, whereas (2) species with long life spans and low birth rates adapt to the environment through an individual capacity to adapt, which is carried out by the expansion of an organ for adaptive behaviors. The primate brain, and that of humans in particular, is the representative example of the latter. The primate brain was enlarged by increased numbers of neurons and thereby additions of novel cortical areas, which diverged from existing ones resulting in increased inter-areal connections  than to the motor output side, unlike in the posterior parietal cortex where response properties resemble SII “”. This rThe additional elements that shape SII activity should integrate with other sensory modalities representing relationships between the body in itself and the outside world, including its spatial structures. Dissimilar dimensions, as well as their respective temporal aspects, such as vestibular, visceral, and visual information, establish associations between the inner and outer worlds “”. The hitriadic niche construction, an interactively accelerated feedback loop for the expansion of neural, cognitive and environmental niches occurred  in a somatocentric manner, possibly under the conscious perception of both the self-body and the environment “”—namely This article summarized current gaps in knowledge that contrast the human and non-human primates' SII function, overviewing recent neuroscientific evidence in relation to possible evolutionary aspects. As a result, the supposedly unique characteristics of the human SII could be explained as a continuation of primate brain development, bridging this functional gap. Taken this information, we would like to propose that a primate’s SII  is a site of information integration, grounded essentially in spatial body maps, yet allowing humans and other primates to also form complex relations between the body and the environmental space. This ability, supported by this region's genetically acquired flexibility to incorporate related information, would allow expanding spatial dimensions over the existing body map, establishing the body-in-the-world map. More importantly, the relativism of the body in relation to the surrounding environment might comprise the neural basis for consciousness, situating the self in the world and those together as a whole, while also serving as the observer of the SI homunculus (precise somatotopic map), avoiding an infinite regression, as the brain’s internal processes establishes this homunculus. Such a phase transition seems to have been triggered by a brain expansion surge in ancestral hominids coinciding with the onset of tool use, which establishes a relationship of equivalence between the body parts and the tools , whereby mental mechanisms of self-objectification might have emerged.The map represented in SII is not merely a physically straightforward one, but rather it is also furnished with varieties of complex concrete and abstract concepts found in nature , consciously choosing and creating relationships (a function of projection) as the immediate conditions the self faces in the demands of the external world. In this sense, the self shapes the environment and the reach of these modifications are incorporated as part of the self, integrating corporeal, environmental and cognitive limits. As such, we would like to propose the concept of a somatocentric holistic self, represented as a body-in-the-world map in the primate SII. This concept blurs the border between body and environment by assuming the world as perceived is a product of the self. These mechanisms might comprise the neural underpinnings of the emergence of self-consciousness, by which most of the recently found human SII functions, which initially seemed not directly connected to non-human SII empirical studies, can be reasonably explained.