Recognition Of Position Research Articles

Triboresistive Touch Sensing: Grid‐Free Touch‐Point Recognition Based on Monolayered Ionic Power Generators (Adv. Mater. 19/2022)

Triboresistive Touch Sensing In article number 2108586, Jeong-Yun Sun and co-workers report a novel type of touch sensing, termed triboresistive touch sensing, which is developed for self-powered gridless touch recognition. An ionic poly(dimethylsiloxane) exhibiting high transmittance (96.5%), stretchability (539.1%), resilience (99.0%), and ionic conductivity (0.25 mS m−1) is designed to generate power based on the electric field generated by touch. The generated voltages ensure recognition of the touch positions without electrode grid layers and external power sources.

Reimagining Agricultural Embodiment with Feminist Foundations

Reimagining Agricultural Embodiment with Feminist Foundations

Triboresistive Touch Sensing: Grid-Free Touch-Point Recognition Based on Monolayered Ionic Power Generators.

Recent growing pursuit of skin-mountable devices has been impeded by the complicated structures of most sensing systems, containing electrode grids, stacked multilayers, and even external power sources. Here, a type of touch sensing, termed "triboresistive touch sensing", is introduced for gridless touch recognition based on monolayered ionic power generators. A homogeneous monolayer, i.e., ionic poly(dimethylsiloxane) (PDMS), generates electricity based on the electric field generated by touch. Voltages generated at each corner of the ionic PDMS rely on resistance between touch points and each corner, ensuring recognition of the touch positions without the need for electrode grid layers and external power sources. With notable advantages of high transparency (96.5%), stretchability (539.1%), and resilience (99.0%) of the ionic PDMS, epidermal triboresistive sensing is demonstrated to express touch position and readily play a musical instrument. A gridless system of triboresistive sensing allows rearrangement of the touch sections according to a given situation without any physical modification, and thus easily completes consecutive missions of controlling position, orientation, and gripping functions of a robot.

Exercise Science Graduates in the Healthcare System: A Comparison Between Australia and Switzerland.

Physical inactivity (PI) is a leading risk factor for global mortality worldwide, a major preventable cause of non-communicable diseases (NCDs) and a socioeconomic burden for healthcare systems. Fortunately, evidence shows that exercise interventions delivered by qualified exercise science graduates is an effective way to reduce PI, prevent and treat NCDs. This study compares the integration of exercise science graduates, defined as university graduates with degrees in sport and exercise science, in the healthcare systems of Australia, a commonly cited model in this regard, and Switzerland, a country considered to have an effective but costly healthcare system. For both countries, three domains were reviewed: healthcare system, exercise science graduates' education, and roles played by exercise science graduates in healthcare system. Australia formally recognizes specifically trained exercise science graduates (referred to as Accredited Exercise Physiologists) as healthcare professionals. The exercise interventions they deliver, which were shown to be cost-effective and lead to positive health outcomes, are covered by Medicare, the Australian universal health insurance. However, Medicare covers only a maximum of 5 yearly sessions of all allied-health services taken together. Conversely, Switzerland, despite offering university master's degrees that focus on physical activity delivery to clinical populations, does not recognize the respective graduates as healthcare providers. As a result, their services are not covered by the Swiss health insurances. The latter do, however, cover a generous number of services (not formally limited) delivered by other allied-health professionals. In conclusion, Australia makes a better use of exercise science graduates than Switzerland does. Switzerland would benefit from establishing a clinical profession for exercise science graduates, defining competencies that they should acquire and setting their scope of practice. The very restricted number of therapy sessions covered by Medicare might limit the positive impact exercise science graduates have on the Australian healthcare system. Overall, mutual learning between countries can promote development and global recognition of clinical positions for exercise science graduates.

Structural Plasticity Is a Feature of Rheostat Positions in the Human Na+/Taurocholate Cotransporting Polypeptide (NTCP).

In the Na+/taurocholate cotransporting polypeptide (NTCP), the clinically relevant S267F polymorphism occurs at a “rheostat position”. That is, amino acid substitutions at this position (“S267X”) lead to a wide range of functional outcomes. This result was particularly striking because molecular models predicted the S267X side chains are buried, and thus, usually expected to be less tolerant of substitutions. To assess whether structural tolerance to buried substitutions is widespread in NTCP, here we used Rosetta to model all 19 potential substitutions at another 13 buried positions. Again, only subtle changes in the calculated stabilities and structures were predicted. Calculations were experimentally validated for 19 variants at codon 271 (“N271X”). Results showed near wildtype expression and rheostatic modulation of substrate transport, implicating N271 as a rheostat position. Notably, each N271X substitution showed a similar effect on the transport of three different substrates and thus did not alter substrate specificity. This differs from S267X, which altered both transport kinetics and specificity. As both transport and specificity may change during protein evolution, the recognition of such rheostat positions may be important for evolutionary studies. We further propose that the presence of rheostat positions is facilitated by local plasticity within the protein structure. Finally, we note that identifying rheostat positions may advance efforts to predict new biomedically relevant missense variants in NTCP and other membrane transport proteins.

Recognition of Contact Force and Position of a Flexible Array-Less Capacitive Tactile Sensor

As an important medium of human-computer interaction, flexible tactile sensors have a vital influence on the intelligence and safety of a system by accurately identifying the contact position and force. For now, most of the existing flexible capacitive pressure sensors adopt an array structure, which still cannot meet the requirements including simple manufacturing, simple wiring, and low cost. This paper innovatively proposes a flexible array-less capacitive tactile sensor, a 30 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times30$ </tex-math></inline-formula> mm sensor prototype, consisting of flexible electrodes and a dielectric layer of a polydimethylsiloxane (PDMS) and multiwalled carbon nanotube (MWCNT) composite. Under the action of a contact force, the contact area is deformed, resulting in a capacitance change in the flexible sensor. First, Poisson’s equation is used to determine the relationship between the electric potential and position. Then, elastic contact mechanics is adopted to describe the relationship between the electric charges and force. Finally, the contact position and force are recognized by measuring the change in capacitance in real time, based on the classical principle of capacitance. Simulation and experimental results verified the effectiveness of the proposed recognition method, showing that the recognition resolution of contact position can reach <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$500\,\,\mu \text{m}\,\,\times 100\,\,\mu \text{m}$ </tex-math></inline-formula> , and the accuracy of contact force is 95.85%. Theoretically, the proposed flexible array-less sensor in this paper has infinite spatial resolution, and most importantly, it does not rely on complex manufacturing, redundant circuits, or sensor cell arrays. Therefore, it has broad application prospects in related fields such as electronic skin and wearable electronic devices.

Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor.

To describe a newly developed machine-learning (ML) algorithm for the automatic recognition of fetal head position using transperineal ultrasound (TPU) during the second stage of labor and to describe its performance in differentiating between occiput anterior (OA) and non-OA positions. This was a prospective cohort study including singleton term (> 37 weeks of gestation) pregnancies in the second stage of labor, with a non-anomalous fetus in cephalic presentation. Transabdominal ultrasound was performed to determine whether the fetal head position was OA or non-OA. For each case, one sonographic image of the fetal head was then acquired in an axial plane using TPU and saved for later offline analysis. Using the transabdominal sonographic diagnosis as the gold standard, a ML algorithm based on a pattern-recognition feed-forward neural network was trained on the TPU images to discriminate between OA and non-OA positions. In the training phase, the model tuned its parameters to approximate the training data (i.e. the training dataset) such that it would identify correctly the fetal head position, by exploiting geometric, morphological and intensity-based features of the images. In the testing phase, the algorithm was blinded to the occiput position as determined by transabdominal ultrasound. Using the test dataset, the ability of the ML algorithm to differentiate OA from non-OA fetal positions was assessed in terms of diagnostic accuracy. The F1 -score and precision-recall area under the curve (PR-AUC) were calculated to assess the algorithm's performance. Cohen's kappa (κ) was calculated to evaluate the agreement between the algorithm and the gold standard. Over a period of 24 months (February 2018 to January 2020), at 15 maternity hospitals affiliated to the International Study group on Labor ANd Delivery Sonography (ISLANDS), we enrolled into the study 1219 women in the second stage of labor. On the basis of transabdominal ultrasound, they were classified as OA (n = 801 (65.7%)) or non-OA (n = 418 (34.3%)). From the entire cohort (OA and non-OA), approximately 70% (n = 824) of the patients were assigned randomly to the training dataset and the rest (n = 395) were used as the test dataset. The ML-based algorithm correctly classified the fetal occiput position in 90.4% (357/395) of the test dataset, including 224/246 with OA (91.1%) and 133/149 with non-OA (89.3%) fetal head position. Evaluation of the algorithm's performance gave an F1 -score of 88.7% and a PR-AUC of 85.4%. The algorithm showed a balanced performance in the recognition of both OA and non-OA positions. The robustness of the algorithm was confirmed by high agreement with the gold standard (κ = 0.81; P < 0.0001). This newly developed ML-based algorithm for the automatic assessment of fetal head position using TPU can differentiate accurately, in most cases, between OA and non-OA positions in the second stage of labor. This algorithm has the potential to support not only obstetricians but also midwives and accoucheurs in the clinical use of TPU to determine fetal occiput position in the labor ward. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Earnings management and fraudulent financial reporting: Distinctive criteria of suspicious accounting practices

The financial reports, as the final product of the accounting information system, need to be accurate in order to maintain the main purpose of financial reporting. Applied methods and techniques of recognition and measurement of financial positions should be in the purpose of realistic and transparent presentation of the financial performances of the entity. However, creative accounting techniques are often used in the process of preparing and presenting financial statements in order to manage earnings and manipulate financial values. Manipulation of financial statements seems to be a generally present phenomenon, and the paper aims to identify the criteria used by members of the accounting profession in the Federation of Bosnia and Hercegovina (FBiH) to distinguish earnings management techniques from fraudulent financial reporting. The research reflects the perception of accountants and auditors in the FBiH regarding the possibility of recognizing suspicious accounting practices in business entities. The research is based on distinctive criteria used in developed economies of the world: measurement subjectivity, the materiality of financial item and compliance of financial reporting with generally accepted accounting principles, but identifies specific distinctive measures used by respondents, too. The results of the research reveal that the accounting professionals in the FBiH rely on distinctive criteria: materiality and compliance of reporting with International Financial Reporting Standards, including two additional measures: compliance of financial reporting with generally accepted practices and confidence of financial information. Respondents did not recognize subjectivity in assessment as a relevant measure of the distinction between earnings management and fraudulent financial reporting.

Desafios de la pedagogía decolonial: experiencias en una comunidad de aprendizaje

This article collects the educational experiences in matters of cultural diversity and educational inclusiveness in a constituent school as a learning community located in a plural and culturally different context. The methodology follows a Case study within the phenomenological-ethnographic perspectives of the research. To do this, we carry out semi-structured interviews, informal conversations and discussion groups accompanied by participant observation and the field notebook. The analysis of the results shows us the manifest rejection of the teachers towards the most significant racist attitudes and expressions, however, the lack of recognition of subtler racist colonial gestures and positions, framed within the collective and colonial imagination. In conclusion, the colonialist of knowledge has become so deeply submerged in the collective imagination that, including teachers involved with inclusive education, find it difficult to recognize.

Making others (un)equal: The social ethics of Scandinavian enclaving in Maputo, Mozambique

This article explores practices and ideologies of equality as the central mechanisms through which cosmopolitan Scandinavians in the capital of Mozambique simultaneously build themselves as a community and sever relationships with locals, thereby constructing a socioeconomic, cultural and moral enclave within the city. Scandinavian sociality is predicated upon the absence of overt signs of social differentiation and these practices are reproduced in their interactions in Maputo. Egalitarian values, paradoxically, allow Scandinavians to mask the structures of inequality inherent to local society and engage in structurally unequal relations in which they act as if all interactions were between autonomous equals, possessed of equivalent social and economic capital. Specifically, the article explores the ways through which Scandinavian expatriates justify the use of domestic labour while refusing to recognise the implication of this structurally unequal employment in the local context. By insisting on equality and autonomy as the basis for social interactions, Scandinavians reject local forms of constructing relationships that are predicated upon the recognition of unequal positions and an obligation of responsibility towards dependents. They thereby refuse to engage with local expectations and understandings of labour relations and fail to recognise the implications of their position within the Mozambican social hierarchy.

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

The proportion of welding work in total man-hours required for shipbuilding processes has been perceived to be significant, and welding man-hours are greatly affected by working posture. Continuous research has been conducted to identify the posture in welding by utilizing the relationship between man-hours and working posture. However, the results that reflect the effect of the welding posture on man-hours are not available. Although studies on posture recognition based on depth image analysis are being positively reviewed, welding operation has difficulties in image interpretation because an external obstacle caused by arcs exists. Therefore, any obstacle element must be removed in advance. This study proposes a method to acquire work postures using a low-cost RGB-D camera and recognize the welding position through image analysis. It removes obstacles that appear as depth holes in the depth image and restores the removed part to the desired state. The welder’s body joints are extracted, and a convolution neural network is used to determine the corresponding welding position. The restored image showed significantly improved recognition accuracy. The proposed method acquires, analyzes, and automates the recognition of welding positions in real-time. It can be applied to all areas where image interpretation is difficult due to obstacles.

Van patiëntenrechten naar patiëntenwaarden

From patients' rights to patients' values In 2022, the Patient’s Rights Act will be 20 years old. This Act has created a permanent recognition of the central position of the patient and his autonomy within the Belgian healthcare system. The Act did undergo some limited changes, but generally remained the same. Nevertheless, the evolution of the healthcare reveals new points of interest and demands attention to so-called patient values, namely quality, care innovation and patient participation. These patient values also reflect the critics formulated on the Patient’s Rights Act. The Belgian patient’s rights framework today consists of a number of other regulations besides the Patient’s Rights Act itself. All these regulations currently form a diffuse patient’s rights framework. In order to guarantee the central position of patient’s rights in our health care system for the next 20 years, a new approach of patient’s rights protection through the patient values is suggested in this article, focusing on quality, care innovation and patient participation and based on the existing legal patient’s rights framework.

Intelligent Tidal Lane System Based on Vehicle Attribute Recognition Algorithm and Related Laws and Regulations

In this paper, an intelligent tidal lane combined with artificial intelligence technology which can adapt to the change of the number of vehicles is designed. The recognition of vehicle position and the direction of vehicle front and rear are recognized by using the vehicle attribute recognition technology based on artificial intelligence. In order to alleviate the morning and evening peak of traffic flow, holiday travel or emergencies caused by two-way lane traffic imbalance, the tidal lane can automatically switch the direction of tidal lane according to the number and direction of vehicles on the two-way road. In addition, we study and analyze the construction of laws and regulations of tidal lane, explore the problems and put forward some suggestions to protect the interests of travelers and maximize social benefits.

Comparison between Medical Student Educators and Program Directors in 2010 and 2019.

Objective Over the last decade, prominence of the Directors of Medical Student Education in Ophthalmology (DMSEs) within the Association of University Professors of Ophthalmology (AUPO) has increased. With increased recognition of this important leadership position, an examination of the trending demographic differences between DMSEs and the Ophthalmology Program Directors (PDs) may be helpful, especially in regard to the potential for academic promotion from each track. Design Present study is a database study. Methods The AUPO directory was used to ascertain the DMSE and the PD members between 2010 and 2019. The demographic profile for each member was determined using publicly available resources. Chi-square analysis of the data was performed. Main Outcome Measures Number of DMSE and PD AUPO from 2010 to 2019 in AUPO Directory, sex, geographic location, and graduation year were measured through this study. Results There has been a steady increase in the number of DMSEs from 2010 to 2019, whereas the number of PDs have remained stable. The number of DMSEs have increased in all regions of the United States. The year of ophthalmology residency graduation was similar in the DMSE and PD cohorts in 2010 and 2019. The number of women have increased in the DMSEs in 2019 compared with 2010; the number of women who were PDs in 2019 were similar to 2010. Finally, more PDs in 2010 became Chairs in 2019, whereas no DMSEs in 2010 became Chair in 2019. Conclusion There are a growing number of DMSEs, overall, with an increasing proportion of women in the 2019 DMSE group compared with 2010. In contrast, the overall number of PDs have remained stable, as has the proportion of female PDs in 2019 compared with 2010. DMSEs, compared with PDs, are less likely to become Chairs. The increased opportunity for academic leadership with the growing DMSE group may help change leadership patterns overall in academic ophthalmology.

Recognition of LHD Position and Maneuvers in Underground Mining Excavations—Identification and Parametrization of Turns

The Inertial Measurement Unit (IMU) is widely used in the monitoring of mining assets. A good example is the Polish underground copper ore mines of KGHM, where research work with the use of the IMU has been carried out for several years. The potential of inertial sensors was ensured by the development of advanced analytics using machine learning methods to support the maintenance management of an extensive machine park and machine manufacturer in adapting various construction elements to mining conditions. The key algorithms developed in the field of inertial data concern: identification of cycles and components of the haulage process operations, identification of dynamic overloads, technical diagnostics of rotating elements, assessment of road conditions (bumps, slopes, damages), assessment of the technical condition of the pavement, assessment of the operator’s driving style, and finally the machine location in the mining excavation. One of the key operational contexts, necessary in the development of analytics for underground mining vehicles, is the identification of the turning moment of the machine at the intersection together with the determination of the driving direction and the turn angle. In the case of a mine with a room-and-pillar system, where the excavation system has the Manhattan structure, it is possible to use many simplifications to correctly estimate the machine motion path. The identification of the spatial context and the turning maneuver is of key importance both in the development of the machine location system, but also in multi-dimensional analyzes, including the analysis of dynamic overloads or the assessment of the operator’s driving style and work safety. The article presents a comparison of several mathematical models used for the machine turn detection problem, which were trained and tested on the real-life industrial data recorded using IMU during a single working shift of the self-propelled machine.

CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots

The assisted assembly of customized products supported by collaborative robots combined with mixed reality devices is the current trend in the Industry 4.0 concept. This article introduces an experimental work cell with the implementation of the assisted assembly process for customized cam switches as a case study. The research is aimed to design a methodology for this complex task with full digitalization and transformation data to digital twin models from all vision systems. Recognition of position and orientation of assembled parts during manual assembly are marked and checked by convolutional neural network (CNN) model. Training of CNN was based on a new approach using virtual training samples with single shot detection and instance segmentation. The trained CNN model was transferred to an embedded artificial processing unit with a high-resolution camera sensor. The embedded device redistributes data with parts detected position and orientation into mixed reality devices and collaborative robot. This approach to assisted assembly using mixed reality, collaborative robot, vision systems, and CNN models can significantly decrease assembly and training time in real production.

Driver posture monitoring in highly automated vehicles using pressure measurement

Objective Driver posture monitoring is useful for evaluating the readiness to take over from highly automated driving systems as well as for designing intelligent restraint systems to reduce injury. The aim of this study was to develop a real-time and robust driver posture monitoring system using pressure measurement. Methods Driver motion and pressure measurement were collected from 23 differently sized participants performing 42 driving and non-driving activities. Nine typical driver postures were identified by analyzing trunk and feet positions in 3 D space for classification. One deep learning classifier and two Random Forest classifiers were trained respectively on pressure distribution, absolute and relative pressure features extracted from pressure measurement. Leave-One-Out cross-validation was performed to evaluate the performance of the classifiers. Results Without considering feet positions, all the classifiers could provide reliable recognition of the normal trunk position for standard driving with an accuracy around 98%. With help of a reference sitting position, the best performance was achieved by Random Forest classifier trained on the relative pressure features with an average classification accuracy of 80.5% across 9 typical postures and 23 drivers. The main errors were related to the recognition of feet positions when applying braking and relaxing both feet on the floor. Conclusions Pressure measurement could be a good alternative or complementary to camera based driver postural monitoring system. Results show that all classifiers proposed in the work could predict the trunk position for standard driving. With help of an initial posture, Random Forest classifier with relative pressure features could classify trunk positions with high accuracy. However, further effort is needed to improve the accuracy of feet position prediction especially by adding more foot related task data.

Knee Valgus during Jump Landing Is Related to the Inaccuracy of Knee Position Recognition in Healthy Young Women.

ABSTRACTObjective:Knee valgus during jump landing is a cause of knee injuries during sports activities. Body recognition is important for maintaining the knees and other body parts in their proper positions. The aim of this study was to investigate whether knee valgus during jump landing in healthy young women is related to the inaccuracy of recognition of bilateral knee positions in the squatting position.Methods:In 39 healthy young women, the degree of knee valgus was evaluated during the drop vertical jump test using the ratio of the knee separation distance to the ankle separation distance. The accuracy of recognition of bilateral knee positions in the squatting position was evaluated by having the blindfolded subjects indicate with their index fingers the subjective positions of their bilateral patellae by placing their fingers on a horizontal bar positioned in front of them 3 cm below the navel. The difference ratio of the recognized distance to the actual distance between the bilateral patellae was measured as an inaccuracy index.Results:The degree of knee valgus during the drop vertical jump test was positively correlated with the degree of inaccuracy of the recognized knee position with the knees in the neutral position (r=0.358, P=0.025).Conclusions:In healthy young women, knee valgus during jump landing was significantly correlated with the inaccuracy in knee position recognition in the squatting posture. This finding suggests that the assessment of knee position recognition in the squatting position could be useful as a screening tool for preventing knee injuries on jump landing during sports activities.

Application Development of Dance Pose Recognition Based on Embedded Artificial Intelligence Equipment

With the development and popularization of artificial intelligence and machine vision, human posture recognition has become one of the most important research directions in the field of machine vision, but its application research in dance movement recognition is in its infancy, especially in the field of Chinese classical dance. In view of this situation, the application of artificial intelligence in basic hand position recognition of Chinese classical dance. Including analysis of basic hand position of Chinese classical dance, comparison of human posture recognition models, recognition and comparison of basic hand position of Chinese classical dance on NVIDIA embedded devices using TensorFlow deep learning framework, and combination of traditional culture and artificial intelligence technology.

Magnetic resonance imaging evaluation of vertebral tumor prediction using hierarchical hidden Markov random field model on Internet of Medical Things (IOMT) platform

Abstract Recently, prediction evaluation of the metastatic spine tumors in therapy is considered a significant area of research. Further, for spinal clinical diagnoses, a large amount of image data from different modalities is often used and interchangeably analyzed based on the automatic vertebra identification. It includes recognition of vertebral positions and recognition in several image modalities. Due to the differences in MR or CT images appearance or shape/size of the vertebras, the identification is however difficult in the present conventional medical research. The segmentation of vertebral tumors that are manually performed by MRI is an important and time-consuming process by the conventional research algorithms. The accuracy of identification of the size and location of spine tumors plays a major role in effective tumor diagnosis and treatment. Therefore, this paper presents the Hierarchical Hidden Markov Random Field Model (HHMRF) to predict the vertebral tumor for the early detection and diagnosis treatment in an effective and efficient manner. The importance of this research is to implement a state-of-the-art strategy for detection of tumors using HHMRF and threshold techniques in MRI images on the Internet of Medical Things Platform (IoMT). HHMRF can coordinate the final section of vertebral tumor homogeneous areas of tissue while preserving the edges between different tissue constituents more effectively using mathematical computation. The proposed method attains the state-of-the-art performance on the diagnosis and segmentation of lumbar spinal stenosis using deep neural network and experimentally analyzed with 97.44% accuracy and 97.11% efficiency ratio on IoMT platform whereas proposed HHMRF achieves 98.5% high precision ratio compared to other existing TDCN (78.2%), DLA (81.6%), M-CNN (78.9%), and DCE-MRI (80.2%) methods.