GEUINF: Real-Time Visualization of Indoor Facilities Using Mixed Reality.

Mixed reality (MR) enables a novel way to visualize virtual objects on real scenarios considering physical constraints. This technology arises with other significant advances in the field of sensors fusion for human-centric 3D capturing. Recent advances for scanning the user environment, real-time visualization and 3D vision using ubiquitous systems like smartphones allow us to capture 3D data from the real world. In this paper, a disruptive application for assessing the status of indoor infrastructures is proposed. The installation and maintenance of hidden facilities such as water pipes, electrical lines and air conditioning tubes, which are usually occluded behind the wall, supposes tedious and inefficient tasks. Most of these infrastructures are digitized but they cannot be visualized onsite. In this research, we focused on the development of a new application (GEUINF) to be launched on smartphones that are capable of capturing 3D data of the real world by depth sensing. This information is relevant to determine the user position and orientation. Although previous approaches used fixed markers for this purpose, our application enables the estimation of both parameters with a centimeter accuracy without them. This novelty is possible since our method is based on a matching process between reconstructed walls of the real world and 3D planes of the replicated world in a virtual environment. Our markerless approach is based on scanning planar surfaces of the user environment and then, these are geometrically aligned with their corresponding virtual 3D entities. In a preprocessing phase, the 2D CAD geometry available from an architectural project is used to generate 3D models of an indoor building structure. In real time, these virtual elements are tracked with the real ones modeled by using ARCore library. Once the alignment between virtual and real worlds is done, the application enables the visualization, navigation and interaction with the virtual facility networks in real-time. Thus, our method may be used by private companies and public institutions responsible of the indoor facilities management and also may be integrated with other applications focused on indoor navigation.

Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features.

The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

Prediction of the increase in health services demand based on the analysis of reasons of calls received by a customer relationship management.

Currently, customer relationship management (CRM) tools are very important in our society because they provide a comunication channel to the healthcare system for patients. Salud Responde is a CRM that provides many health services for the entire population of Andalusia, in southern Spain. The number and frequenzy of phone calls received change along the year. They depend on many factors, such as weekdays, seasons, vaccination campaigns, environmental factors, pandemic periods, etc. All these are the main reasons number of health calls changes along the year. This variability makes that the current management of resources for offering emergency services based on historical data is inefficient. The factors, which influence the phone calls along the year, are different from one period to another. Therefore, it is clear to demand an improved in the current management system. In this context, the main goal for this research is to develop an expert system able to identify and analyze, using different data mining algorithms, the most relevant factors to predict the variability of health service demand. Thus, here, it is proposed a methodology in which using reasons calls received in the CRM as input data, it is possible to predict in advance the healthcare resources demand.

Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools.

The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another.

[Importance of health CRM in pandemics and health alerts]. / Importancia de los Customer Relationship Management (CRM) sanitarios en las pandemias y alertas sanitarias.

OBJECTIVE: The aim of this article is to demonstrate the importance of the role a health CRM can play in a pandemic or health alert. During the influenza-A pandemic, Salud Responde played a very important role. Its main objective was to establish protocols and citizens advice lines that would avoid patients with mild influenza-A symptoms going to health centre. DESIGN: A triage system was developed around the Siebel CRM (software tool) to achieve this objective. This allowed the Salud Responde staff to establish the severity of the patient depending on the symptoms and the risk factors of the patient, as well as being able to inform, give health advice or refer the patient to medical centres if necessary. SETTING: All patients (a total of 56,497) who were attended by Salud Responde within its influenza-A service portfolio have been included. PARTICIPANTS: Patients who were attended by Salud Responde. MAIN MEASUREMENTS: The data have been extracted from the Salud Responde data base. RESULTS: Salud Responde attended to 56,497 patients during the influenza-A pandemic, of whom 48,287 patients did not require health care. CONCLUSIONS: Salud Responde attended to 56,497 patients, of whom 48,287 patients did not require health care. Apart from any financial savings that this could entail, it contributed to minimising the pandemic, avoiding the patient having to go to a health centre to receive medical care or information, and prevented, to a great extent, the flooding of casualty departments.

An improvement in the appointment scheduling in primary health care centers using data mining.

An optimal resource management in health care centers implies the use of an appropriate timetabling scheme to schedule appointments. Timetables of health centers are usually divided into time slots whose duration is equal to time required for clinical attendance. However doctors perform a series of tasks that are not always clinical in nature: issuing prescriptions or prescribing sick leave certificates. In this sense the time spent in attending a clinical or an administrative matter is different. This last required less time to attend the patient. This study is focused in the administrative task. A predictive model is generated to provide daily information on how many patients will go to the health center for an administrative issue. The accuracy of the model is less than 4,6 % absolute error and the improvement in scheduling appointments is a time saving of 21,73 %.

Modeling of the 3D Tree Skeleton Using Real-World Data: A Survey.

Tree modeling has been extensively studied in computer graphics. Recent advances in the development of high-resolution sensors and data processing techniques are extremely useful for collecting 3D datasets of real-world trees and generating increasingly plausible branching structures. The wide availability of versatile acquisition platforms allows us to capture multi-view images and scanned data that can be used for guided 3D tree modeling. In this paper, we carry out a comprehensive review of the state-of-the-art methods for the 3D modeling of botanical tree geometry by taking input data from real scenarios. A wide range of studies has been proposed following different approaches. The most relevant contributions are summarized and classified into three categories: (1) procedural reconstruction, (2) geometry-based extraction, and (3) image-based modeling. In addition, we describe other approaches focused on the reconstruction process by adding additional features to achieve a realistic appearance of the tree models. Thus, we provide an overview of the most effective procedures to assist researchers in the photorealistic modeling of trees in geometry and appearance. The article concludes with remarks and trends for promising research opportunities in 3D tree modeling using real-world data.

A virtual reality simulator for training the surgical reduction of patient-specific supracondylar humerus fractures.

PURPOSE: Virtual reality has been used as a training platform in medicine, allowing the repetition of a situation/scenario as many times as needed and making it patient-specific prior to an operation. Of special interest is the minimally invasive plate osteosynthesis (MIPO). It represents a novel technique for orthopedic trauma surgery, but requires intensive training to acquire the required skills. In this paper, we propose a virtual reality platform for training the surgical reduction of supracondylar fractures of the humerus using MIPO. The system presents a detailed surgical theater where the surgeon has to place the bone fragments properly. METHODS: Seven experienced users were selected to perform a surgical reduction using our proposal. Two paired humeri were scanned from a dataset obtained from the Complejo Hospitalario de Jaén. A virtual fracture was performed in one side of the pair, using the other as contralateral part. Users have to simulate a reduction for each case and fill out a survey about usability, using a five-option Likert scale. RESULTS: The subjects have obtained excellent scores in both simulations. The users have notably reduced the time employed in the second experiment, being 60% less in average. Subjects have valued the usability (5.0), the intuitiveness (4.6), comfort (4.5), and realism (4.9) in a 1-5 Likert scale. The mean score of the usability survey was 4.66. CONCLUSION: The system has shown a high learning rate, and it is expected that the trainees will reach an expert level after additional runs. By focusing on the movement of bone fragments, specialists acquire motor skills to avoid the malrotation of MIPO-treated fractures. A future study can fulfill the requirements needed to include this training system into the protocol of real surgeries. Therefore, we expect the system to increase the confidence of the trainees as well as to improve their decision making.

Hybrid curvature-geometrical detection of landmarks for the automatic analysis of the reduction of supracondylar fractures of the femur.

BACKGROUND AND OBJECTIVE: The analysis of the features of certain tissues is required by many procedures of modern medicine, allowing the development of more efficient treatments. The recognition of landmarks allows the planning of orthopedic and trauma surgical procedures, such as the design of prostheses or the treatment of fractures. Formerly, their detection has been carried out by hand, making the workflow inaccurate and tedious. In this paper we propose an automatic algorithm for the detection of landmarks of human femurs and an analysis of the quality of the reduction of supracondylar fractures. METHODS: The detection of anatomical landmarks follows a knowledge-based approach, consisting of a hybrid strategy: curvature and spatial decomposition. Prior training is unrequired. The analysis of the reduction quality is performed by a side-to-side comparison between healthy and fractured sides. The pre-clinical validation of the technique consists of a two-stage study: Initially, we tested our algorithm with 14 healthy femurs, comparing the output with ground truth values. Then, a total of 140 virtual fractures was processed to assess the validity of our analysis of the quality of reduction. A two-sample t test and correlation coefficients between metrics and the degree of reduction have been employed to determine the reliability of the algorithm. RESULTS: The average detection error of landmarks was maintained below 1.7 mm and 2∘ (p< 0.01) for points and axes, respectively. Regarding the contralateral analysis, the resulting P-values reveal the possibility to determine whether a supracondylar fracture is properly reduced or not with a 95% of confidence. Furthermore, the correlation is high between the metrics and the quality of the reduction. CONCLUSIONS: This research concludes that our technique allows to classify supracondylar fracture reductions of the femur by only analyzing the detected anatomical landmarks. A initial training set is not required as input of our algorithm.

The role of virtual and augmented reality in orthopedic trauma surgery: From diagnosis to rehabilitation.

Virtual and augmented reality have been used to assist and improve human capabilities in many fields. Most recent advances allow the usage of these technologies for personal and professional purposes. In particular, they have been progressively introduced in many medical procedures since the last century. Thanks to immersive training systems and a better comprehension of the ongoing procedure, their main objectives are to increase patient safety and decrease recovery time. The current and future possibilities of virtual and augmented reality in the context of bone fracture reduction are the main focus of this review. This medical procedure requires meticulous planning and a complex intervention in many cases, hence becoming a promising candidate to be benefited from this kind of technology. In this paper, we exhaustively analyze the impact of virtual and augmented reality to bone fracture healing, detailing each task from diagnosis to rehabilitation. Our primary goal is to introduce novel researchers to current trends applied to orthopedic trauma surgery, proposing new lines of research. To that end, we propose and evaluate a set of qualitative metrics to highlight the most promising challenges of virtual and augmented reality technologies in this context.

Automatic detection of landmarks for the analysis of a reduction of supracondylar fractures of the humerus.

An accurate identification of bone features is required by modern orthopedics to improve patient recovery. The analysis of landmarks enables the planning of a fracture reduction surgery, designing prostheses or fixation devices, and showing deformities accurately. The recognition of these features was previously performed manually. However, this long and tedious process provided insufficient accuracy. In this paper, we propose a geometrically-based algorithm that automatically detects the most significant landmarks of a humerus. By employing contralateral images of the upper limb, a side-to-side study of the landmarks is also conducted to analyze the goodness of supracondylar fracture reductions. We conclude that a reduction can be classified by only considering the detected landmarks. In addition, our technique does not require a prior training, thus becoming a reliable alternative to treat this kind of fractures.

Spatial analysis and prediction of the flow of patients to public health centres in a middle-sized Spanish city.

Human and medical resources in the Spanish primary health care centres are usually planned and managed on the basis of the average number of patients in previous years. However, sudden increases in patient demand leading to delays and slip-ups can occur at any time without warning. This paper describes a predictive model capable of calculating patient demand in advance using geospatial data, whose values depend directly on weather variables and location of the health centre people are assigned to. The results obtained here show that outcomes differ from one centre to another depending on variations in the variables measured. For example, patients aged 25-34 and 55-65 years visited health centres less often than all other groups. It was also observed that the higher the economic level, the fewer visits to health centres. From the temporal point of view, Monday was the day of greatest demand, while Friday the least. On a monthly basis, February had the highest influx of patients. Also, air quality and humidity influenced the number of visits; more visits during poor air quality and high relative humidity. The addition of spatial variables minimised the average error the predictive model from 7.4 to 2.4% and the error without considering spatial variables varied from the high of 11.8% in to the low of 2.5%. The new model reduces the values in the predictive model, which are more homogeneous than previously.

EL-REP: A New 2D Geometric Decomposition Scheme and Its Applications.

This work describes the EL-REP, a new 2D decomposition scheme with interesting properties and applications. The EL-REP can be computed for one or more simple polygons of any kind: convex or nonconvex, with or without holes and even with several shells. A method for constructing this decomposition is described in detail, together with several of its main applications: fast point-in-polygon inclusion test, 2D location, triangulation of polygons, and collision detection.

Importancia de los Customer Relationship Management (CRM) sanitarios en las pandemias y alertas sanitarias / Importance of health CRM in pandemics and health alerts

OBJETIVO: El objetivo es demostrar la importancia que puede tener un CRM sanitario en una pandemia o alerta sanitaria. Durante la pandemia de la gripe A, Salud Responde jugó un papel muy importante; su principal objetivo era establecer unos protocolos y la atención al ciudadano que evitara que pacientes con sintomatología leve de gripe A se desplazaran a los centros de salud. DISEÑO: Para lograr este objetivo se desarrolló sobre el CRM de Siebel (herramienta informática) unos triajes que permitieron al personal de Salud Responde, en función de los síntomas del paciente y sus factores de riesgo, establecer la gravedad de este, y así poder informar, dar consejos sanitarios o derivar al paciente a los centros médicos en caso de necesidad. Emplazamiento: En este estudio se ha tenido en cuenta a todos los pacientes que fueron atendidos por Salud Responde en su cartera de servicios de gripe A. En total fueron atendidos 56.497 pacientes. PARTICIPANTES: Pacientes que fueron atendidos por Salud Responde. MEDICIONES PRINCIPALES: Los datos han sido extraídos de las bases de datos de Salud Responde. RESULTADOS: En el caso de la pandemia de la gripe A, Salud Responde atendió a 56.497 pacientes, de los que 48.287 no requirieron atención sanitaria. CONCLUSIONES: Salud Responde atendió a 56.497 pacientes, de los cuales 48.287 no requirieron atención sanitaria. Aparte del posible ahorro económico que esto pudo suponer, contribuyó a minimizar la pandemia, evitando que pacientes con sintomatología leve fueran a su centro de salud para recibir atención médica o información, y evitó en gran medida el desbordamiento de las urgencias

OBJECTIVE: The aim of this article is to demonstrate the importance of the role a health CRM can play in a pandemic or health alert. During the influenza-A pandemic, Salud Responde played a very important role. Its main objective was to establish protocols and citizens advice lines that would avoid patients with mild influenza-A symptoms going to health centre. DESIGN: A triage system was developed around the Siebel CRM (software tool) to achieve this OBJECTIVE: This allowed the Salud Responde staff to establish the severity of the patient depending on the symptoms and the risk factors of the patient, as well as being able to inform, give health advice or refer the patient to medical centres if necessary. SETTING: All patients (a total of 56,497) who were attended by Salud Responde within its influenza-A service portfolio have been included. PARTICIPANTS: Patients who were attended by Salud Responde. MAIN MEASUREMENTS: The data have been extracted from the Salud Responde data base. RESULTS: Salud Responde attended to 56,497 patients during the influenza-A pandemic, of whom 48,287 patients did not require health care. CONCLUSIONS: Salud Responde attended to 56,497 patients, of whom 48,287 patients did not require health care. Apart from any financial savings that this could entail, it contributed to minimising the pandemic, avoiding the patient having to go to a health centre to receive medical care or information, and prevented, to a great extent, the flooding of casualty departments