ALBERT

Description: Celestial tourism, also known as astrotourism, astronomical tourism or, less frequently, star tourism, refers to people’s interest in visiting places where celestial phenomena can be clearly observed. Stars, skygazing, meteor showers or comets, among other phenomena, arouse people’s interest, however, good night sky conditions are required to observe such phenomena. From an environmental point of view, several organisations have surfaced in defence of the protection of dark night skies against light pollution, while from an economic point of view; the idea also opens new possibilities for development in associated areas. The quality of dark skies for celestial tourism can be measured by night sky brightness (NSB), which is used to quantify the visual perception of the sky, including several light sources at a specific point on earth. The aim of this research is to model the nocturnal sky brightness by training and testing a probabilistic model using real NSB data. ARIMA and artificial neural network models have been applied to open NSB data provided by the Globe at Night international programme, with the results of this first model approach being promising and opening up new possibilities for astrotourism. To the best of the authors’ knowledge, probabilistic models have not been applied to NSB forecasting.

Description: One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage.

Description: The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology.

Description: The verification of remotely sensed estimates of surface variables is essential for any remote sensing study. The objective of this study was to compare leaf area index (LAI), surface temperature (Ts), and actual evapotranspiration (ETa), estimated using the remote sensing-based METRIC model and in situ measurements collected at the satellite overpass time. The study was carried out at a commercial corn field in eastern South Dakota. Six clear-sky images from Landsat 7 and Landsat 8 (Path 29, Row 29) were processed and used for the assessment. LAI and Ts were measured in situ, and ETa was estimated using an atmometer and independent crop coefficients. The results revealed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m−2, the Ts comparison had an agreement of r2 = 0.87 and RMSE 1.24 °C, and ETa presented r2 = 0.89 and RMSE = 0.71 mm day−1.

Description: In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported.

Description: Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users’ quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users’ behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users’ authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level.

Description: Brain health refers to the preservation of brain integrity and function optimized for an individual’s biological age. Several studies have demonstrated that our lifestyles habits impact our brain health and our cognitive and mental wellbeing. Monitoring such lifestyles is thus critical and mobile technologies are essential to enable such a goal. Three databases were selected to carry out the search. Then, a PRISMA and PICOTS based criteria for a more detailed review on the basis of monitoring lifestyle aspects were used to filter the publications. We identified 133 publications after removing duplicates. Fifteen were finally selected from our criteria. Many studies still use questionnaires as the only tool for monitoring and do not apply advanced analytic or AI approaches to fine-tune results. We anticipate a transformative boom in the near future developing and implementing solutions that are able to integrate, in a flexible and adaptable way, data from technologies and devices that users might already use. This will enable continuous monitoring of objective data to guide the personalized definition of lifestyle goals and data-driven coaching to offer the necessary support to ensure adherence and satisfaction.

Description: The aim of the present study was to assess the accuracy of a multi-camera tracking system (Mediacoach®) to track elite football players’ movements in real time. A total of 207 observations of 38 official matches from Liga 1, 2, 3™ (2nd Spanish Division, season 2017/18) were included in the study (88 defenders, 84 midfielders, and 35 attackers of the same team). Total distance (TD, m) distance in zone 4 (DZ4) at a speed of 14–21 km/h, distance in zone 5 (DZ5) at a speed of 21–24 km/h (DZ5), distance in zone 6 (DZ6) at a speed of ≥24 km/h, maximum speed (km/h), and number of sprints (actions above 24 km/h) were registered with the Apex® GPS system (STATSports™, Newry, N. Ireland) and Mediacoach® semi-automatic tracking system (LaLiga™, Madrid, Spain). The level of agreement between variables estimated by the two systems was analyzed. Bias was also calculated by deducting the GPS estimated value from the video estimated value, and then dividing the difference score by the GPS estimated value. All variables showed high ICC values (〉0.75) and very large correlations (r 〉 0.70). However the video-based performance analysis system overestimated the results obtained in the different speed zones (DZ5: +16.59 ± 62.29 m; LOA95%: −105.49 to 138.68; DZ6: +93.26 ± 67.76 m; LOA95%: −39.55 to 226.07), the number of sprints (+2.27 ± 2.94; LOA95%: −3.49 to 8.02), and the maximum speed (+0.32 ± 1.25 km/h; LOA95%: −2.13 to 2.77). The maximum bias was found in DZ6 (47%). This demonstrates that Mediacoach® is as accurate as a GPS system to obtain objective data in real time, adapted to physical and movement demands of elite football, especially for total distance and distances traveled at medium speeds.

Description: The conservation and authentication of pictorial artworks is considered an important part of the preservation of cultural heritage. The use of non-destructive testing allows the obtention of accurate information about the state of pictorial artworks without direct contact between the equipment used and the sample. In particular, the use of this kind of technology is recommended in obtaining three-dimensional surface digital models, as it provides high-resolution information that constitutes a kind of fingerprint of the samples. In the case of pictorial artworks with some kind of surface relief, one of the most useful technologies is structured light (SL). In this paper, the minimum difference in height that can be distinguished with this technology was estimated, establishing experimentally both the error committed in the measurement process and the precision in the use of this technology. This study focused on the case of oil paintings on canvas and developed a low-cost system to ensure its wide use.

Description: Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0.75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.