Search

Emotion recognition through speech is a technique employed in various scenarios of Human-Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%.

Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics.

Heart failure is a severe medical condition with an important worldwide incidence that occurs when the heart is unable to efficiently pump the patient's blood throughout the body. The monitoring of edema in the lower limbs is one of the most efficient ways to control the evolution of the condition. Impedance spectroscopy has been proposed as an efficient technique to monitor body volume in patients with heart failure. It is necessary to research new wearable devices for remote patient monitoring, which can be easily worn by patients in a continuous way. In this work, we design and implement new wearable textile electrodes for the monitoring of edema evolution in patients with heart failure. Impedance spectroscopy measurements were carried out in 5 healthy controls and 2 patients with heart failure using our wearable electrodes for 3 days. The results show the appropriateness of impedance spectroscopy and our wearable electrodes to monitor body volume evolution. Impedance spectroscopy is shown to be an efficient marker of the presence of edema in heart failure patients. Initial patient positive feedback was obtained for the use of the wearable device.

SARS-CoV-2 amino acid variants that contribute to an increased transmissibility or to host immune system escape are likely to increase in frequency due to positive selection and may be identified using different methods, such as codeML, FEL, FUBAR, and MEME. Nevertheless, when using different methods, the results do not always agree. The sampling scheme used in different studies may partially explain the differences that are found, but there is also the possibility that some of the identified positively selected amino acid sites are false positives. This is especially important in the context of very large-scale projects where hundreds of analyses have been performed for the same protein-coding gene. To account for these issues, in this work, we have identified positively selected amino acid sites in SARS-CoV-2 and 15 other coronavirus species, using both codeML and FUBAR, and compared the location of such sites in the different species. Moreover, we also compared our results to those that are available in the COV2Var database and the frequency of the 10 most frequent variants and predicted protein location to identify those sites that are supported by multiple lines of evidence. Amino acid changes observed at these sites should always be of concern. The information reported for SARS-CoV-2 can also be used to identify variants of concern in other coronaviruses.

The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues.

Cerebral hemodynamics describes an important physiological system affected by components such as blood pressure, CO 2 levels, and endothelial factors. Recently, novel techniques have emerged to analyse cerebral hemodynamics based on the calculation of entropies, which quantifies or describes changes in the complexity of this system when it is affected by a pathological or physiological influence. One recently described measure is transfer entropy, which allows for the determination of causality between the various components of a system in terms of their flow of information, and has shown positive results in the multivariate analysis of physiological signals. This study aims to determine whether conditional transfer entropy reflects the causality in terms of entropy generated by hypocapnia on cerebral hemodynamics. To achieve this, non-invasive signals from 28 healthy individuals who undertook a hyperventilation maneuver were analyzed using conditional transfer entropy to assess the variation in the relevance of CO 2 levels on cerebral blood velocity. By employing a specific method to discretize the signals, it was possible to differentiate the influence of CO 2 levels during the hyperventilation phase (22.0% and 20.3% increase for the left and right hemispheres, respectively) compared to normal breathing, which remained higher during the recovery phase (15.3% and 15.2% increase, respectively).

The advantages of using mutual information to evaluate the correlation between randomness tests have recently been demonstrated. However, it has been pointed out that the high complexity of this method limits its application in batteries with a greater number of tests. The main objective of this work is to reduce the complexity of the method based on mutual information for analyzing the independence between the statistical tests of randomness. The achieved complexity reduction is estimated theoretically and verified experimentally. A variant of the original method is proposed by modifying the step in which the significant values of the mutual information are determined. The correlation between the NIST battery tests was studied, and it was concluded that the modifications to the method do not significantly affect the ability to detect correlations. Due to the efficiency of the newly proposed method, its use is recommended to analyze other batteries of tests.

In this paper, we introduce LagunAR, a mobile outdoor Augmented Reality (AR) application for providing heritage information and 3D visualization on a city scale. The LagunAR application was developed to provide historical information about the city of La Laguna in the XVI century, when it was the main city in the Canary Islands. The application provides a reconstructed 3D model of the city at that time that is shown on a mobile phone over-imposed on the actual city using geolocation. The geolocated position is used also for providing information of several points of interest in the city. The paper describes the design and implementation of the application and details the optimization techniques that have been used to manage the full information of the city using a mobile phone as a sensor and visualization tool. We explain the application usability study carried out using a heuristic test; in addition it is probed by users in a qualitative user test developed as preliminary research. Results show that it is possible to develop a real-time application that shows the user a city-scale 3D model and also manages the information of the points of interest.

Intensity modulated radiation therapy (IMRT) is one of the most used techniques for cancer treatment. Using a linear accelerator, it delivers radiation directly at the cancerogenic cells in the tumour, reducing the impact of the radiation on the organs surrounding the tumour. The complexity of the IMRT problem forces researchers to subdivide it into three sub-problems that are addressed sequentially. Using this sequential approach, we first need to find a beam angle configuration that will be the set of irradiation points (beam angles) over which the tumour radiation is delivered. This first problem is called the Beam Angle Optimisation (BAO) problem. Then, we must optimise the radiation intensity delivered from each angle to the tumour. This second problem is called the Fluence Map Optimisation (FMO) problem. Finally, we need to generate a set of apertures for each beam angle, making the intensities computed in the previous step deliverable. This third problem is called the Sequencing problem. Solving these three sub-problems sequentially allows clinicians to obtain a treatment plan that can be delivered from a physical point of view. However, the obtained treatment plans generally have too many apertures, resulting in long delivery times. One strategy to avoid this problem is the Direct Aperture Optimisation (DAO) problem. In the DAO problem, the idea is to merge the FMO and the Sequencing problem. Hence, optimising the radiation's intensities considers the physical constraints of the delivery process. The DAO problem is usually modelled as a Mixed-Integer optimisation problem and aims to determine the aperture shapes and their corresponding radiation intensities, considering the physical constraints imposed by the Multi-Leaf Collimator device. In solving the DAO problem, generating clinically acceptable treatments without additional sequencing steps to deliver to the patients is possible. In this work, we propose to solve the DAO problem using the well-known Particle Swarm Optimisation (PSO) algorithm. Our approach integrates the use of mathematical programming to optimise the intensities and utilizes PSO to optimise the aperture shapes. Additionally, we introduce a reparation heuristic to enhance aperture shapes with minimal impact on the treatment plan. We apply our proposed algorithm to prostate cancer cases and compare our results with those obtained in the sequential approach. Results show that the PSO obtains competitive results compared to the sequential approach, receiving less radiation time (beam on time) and using the available apertures with major efficiency.

Monkeypox is a smallpox-like disease that was declared a global health emergency in July 2022. Because of this resemblance, it is not easy to distinguish a monkeypox rash from other similar diseases; however, due to the novelty of this disease, there are no widely used databases for this purpose with which to develop image-based classification algorithms. Therefore, three significant contributions are proposed in this work: first, the development of a publicly available dataset of monkeypox images; second, the development of a classification system based on convolutional neural networks in order to automatically distinguish monkeypox marks from those produced by other diseases; and, finally, the use of explainable AI tools for ensemble networks. For point 1, free images of monkeypox cases and other diseases have been searched in government databases and processed until we are left with only a section of the skin of the patients in each case. For point 2, various pre-trained models were used as classifiers and, in the second instance, combinations of these were used to form ensembles. And, for point 3, this is the first documented time that an explainable AI technique (like GradCAM) is applied to the results of ensemble networks. Among all the tests, the accuracy reaches 93% in the case of single pre-trained networks, and up to 98% using an ensemble of three networks (ResNet50, EfficientNetB0, and MobileNetV2). Comparing these results with previous work, a substantial improvement in classification accuracy is observed.

Extremely low-frequency electromagnetic stimulation (ELF-EMS) was demonstrated to be significantly beneficial in rodent models of permanent stroke. The mechanism involved enhanced cerebrovascular perfusion and endothelial cell nitric oxide production. However, the possible effect on the neuroinflammatory response and its efficacy in reperfusion stroke models remains unclear. To evaluate ELF-EMS effectiveness and possible immunomodulatory response, we studied neurological outcome, behavior, neuronal survival, and glial reactivity in a rodent model of global transient stroke treated with 13.5 mT/60 Hz. Next, we studied microglial cells migration and, in organotypic hippocampal brain slices, we assessed neuronal survival and microglia reactivity. ELF-EMS improved the neurological score and behavior in the ischemia-reperfusion model. It also improved neuronal survival and decreased glia reactivity in the hippocampus, with microglia showing the first signs of treatment effect. In vitro ELF-EMS decreased (Lipopolysaccharide) LPS and ATP-induced microglia migration in both scratch and transwell assay. Additionally, in hippocampal brain slices, reduced microglial reactivity, improved neuronal survival, and modulation of inflammation-related markers was observed. Our study is the first to show that an EMF treatment has a direct impact on microglial migration. Furthermore, ELF-EMS has beneficial effects in an ischemia/reperfusion model, which indicates that this treatment has clinical potential as a new treatment against ischemic stroke.

Multimodal emotion recognition implies the use of different resources and techniques for identifying and recognizing human emotions. A variety of data sources such as faces, speeches, voices, texts and others have to be processed simultaneously for this recognition task. However, most of the techniques, which are based mainly on Deep Learning, are trained using datasets designed and built in controlled conditions, making their applicability in real contexts with real conditions more difficult. For this reason, the aim of this work is to assess a set of in-the-wild datasets to show their strengths and weaknesses for multimodal emotion recognition. Four in-the-wild datasets are evaluated: AFEW, SFEW, MELD and AffWild2. A multimodal architecture previously designed is used to perform the evaluation and classical metrics such as accuracy and F1-Score are used to measure performance in training and to validate quantitative results. However, strengths and weaknesses of these datasets for various uses indicate that by themselves they are not appropriate for multimodal recognition due to their original purpose, e.g., face or speech recognition. Therefore, we recommend a combination of multiple datasets in order to obtain better results when new samples are being processed and a good balance in the number of samples by class.

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.

Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current "smart everything" scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ execution of highly complex state-of-the-art deep neural networks (DNNs), despite the resource-constrained nature of such infrastructures. In particular, the review discusses the most salient approaches conceived along those lines, elaborating on the specificities of the partitioning schemes and the parallelism paradigms explored, providing an organized and schematic discussion of the underlying workflows and associated communication patterns, as well as the architectural aspects of the DNNs that have driven the design of such techniques, while also highlighting both the primary challenges encountered at the design and operational levels and the specific adjustments or enhancements explored in response to them.

In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they are also usually recorded in very short time intervals. On the other hand, routes are often generated so that they do not correspond to reality, due to artifacts such as buildings, bridges, or sensor failures and where, due to the large amount of data, visual analysis of human expert is unable to detect genuinely anomalous routes. The presence of such abnormalities can lead to faulty sensors being detected which may allow sensor replacement to reliably track the vehicle. However, given the reliability of the available sensors, there are very few examples of such anomalies, which can make it difficult to apply supervised learning techniques. In this work we propose the use of unsupervised deep neural network models based on stacked autoencoders to detect anomalous routes in vehicles within Santiago de Chile. The results show that the proposed model is capable of effectively detecting anomalous paths in real data considering validation given by an expert user, reaching a performance of 82.1% on average. As future work, we propose to incorporate the use of Long Short-Term Memory (LSTM) and attention-based networks in order to improve the detection of anomalous trajectories.

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Nowadays, the study of the gait by analyzing the distribution of plantar pressure is a well-established technique. The use of intelligent insoles allows real-time monitoring of the user. Thus, collecting and analyzing information is a more accurate process than consultations in so-called gait laboratories. Most of the previous published studies consider the composition and operation of these insoles based on resistive sensors. However, the use of capacitive sensors could provide better results, in terms of linear behavior under the pressure exerted. This behavior depends on the properties of the dielectric used. In this work, the design and implementation of an intelligent plantar insole composed of capacitive sensors is proposed. The dielectric used is a polydimethylsiloxane (PDMS)-based composition. The sensorized plantar insole developed achieves its purpose as a tool for collecting pressure in different areas of the sole of the foot. The fundamentals and details of the composition, manufacture, and implementation of the insole and the system used to collect data, as well as the data samples, are shown. Finally, a comparison of the behavior of both insoles, resistive and capacitive sensor-equipped, is made. The prototype presented lays the foundation for the development of a tool to support the diagnosis of gait abnormalities.

Schizophrenia (SZ) is a serious mental disorder that is typically treated with antipsychotic medication. Treatment-resistant schizophrenia (TRS) is the condition where symptoms remain after pharmacological intervention, resulting in long-lasting functional and social impairments. As the identification and treatment of a TRS patient requires previous failed treatments, early mechanisms of detection are needed in order to quicken the access to effective therapy, as well as improve treatment adherence. In this study, we aim to find a microRNA (miRNA) signature for TRS, as well as to shed some light on the molecular pathways potentially involved in this severe condition. To do this, we compared the blood miRNAs of schizophrenia patients that respond to medication and TRS patients, thus obtaining a 16-miRNA TRS profile. Then, we assessed the ability of this signature to separate responders and TRS patients using hierarchical clustering, observing that most of them are grouped correctly (~70% accuracy). We also conducted a network, pathway analysis, and bibliography search to spot molecular pathways potentially altered in TRS. We found that the response to stress seems to be a key factor in TRS and that proteins p53, SIRT1, MDM2, and TRIM28 could be the potential mediators of such responses. Finally, we suggest a molecular pathway potentially regulated by the miRNAs of the TRS profile.

In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system.

A key factor for successfully implementing gamified learning platforms is making students interact with the system from multiple digital platforms. Learning platforms that try to accomplish all their objectives by concentrating all the interactions from users with them are less effective than initially believed. Conversational bots are ideal solutions for cross-platform user interaction. In this paper, an open student-player model is presented. The model includes the use of machine learning techniques for online adaptation. Then, an architecture for the solution is described, including the open model. Finally, the chatbot design is addressed. The chatbot architecture ensures that its reactive nature fits into our defined architecture. The approach's implementation and validation aim to create a tool to encourage kids to practice multiplication tables playfully.

This paper proposes a novel method for the calibration of a stereo camera system used to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose purpose is to eliminate or at least minimize said distortion, machine learning techniques have been used, and more specifically, regression algorithms. These algorithms are trained with a large number of vectors of input features with their respective outputs, since, in view of the application of the procedure proposed, it is important that the training set be sufficiently representative of the variety that can occur in a real scene, which includes the different orientations that the pitch angle can take, the error in said angle and the effect that all this has on the reconstruction process. The most efficient regression algorithms for estimating the error in the pitch angle are derived from decision trees and certain neural network configurations. Once estimated, the error can be corrected, thus making the reconstructed scene appear more like the real one. Although the authors base their method on U-V disparity and employ this same technique to completely reconstruct the 3D scene, one of the most interesting features of the method proposed is that it can be applied regardless of the technique used to carry out said reconstruction.

Power-quality events and operation transients in power systems (PS) with isolated neutral can saturate inductive voltage transformers (IVT), which, when interacting with the overhead and underground cable capacitances, can cause ferroresonance events in the local PS. This abnormal operating mode can partially or totally damage the transformers and switchgears within the affected PS. Distribution system operators (DSO) can minimize these effects by detecting ferroresonance events accurately and fast enough and changing the mode of operation accordingly. Direct detection methods, i.e., based on voltage measurements, are reliable, but the massive deployment of this solution is relatively expensive; i.e., power quality analyzers cost thousands of USD. Alternatively, indirect detection methods are also available, e.g., IVT vibration measurements with accelerometers costing hundreds of USD, but their reliability depends on the installation method used. This manuscript proposes using the acoustic noise caused by magnetostriction forces within the IVT core during ferroresonance events to detect their occurrence. Compared to other indirect methods, electret condenser microphones with preamplifying stage cost less than USD 10 and are less sensitive to the installation procedure. The proposed method is validated experimentally, and its performance compared to IVT vibration measurements one by using the same detection methodology.

The tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. "Trajectory tracking" is the most common method, with the controller aiming to move the robot toward a moving target point, like in a real-time servosystem. In the case of complex systems or systems under perturbations or unmodeled effects, such as UAVs (Unmanned Aerial Vehicles), other tracking methods can offer additional benefits. In this paper, methods that consider the dynamics of the path's descriptor parameter (which can be called "error adaptive tracking") are contrasted with trajectory tracking. A formal description of tracking methods is first presented, showing that two types of error adaptive tracking can be used with the same controller in any system. Then, it is shown that the selection of an appropriate tracking rate improves error convergence and robustness for a UAV system, which is illustrated by simulation experiments. It is concluded that error adaptive tracking methods outperform trajectory tracking ones, producing a faster and more robust convergence tracking, while preserving, if required, the same tracking rate when convergence is achieved.

Achieving accurate STR genotyping by using next-generation sequencing data has been challenging. To provide the forensic genetics community with a reliable open-access STR database, we conducted a comprehensive genotyping analysis of a set of STRs of broad forensic interest obtained from 1000 Genome populations. We analyzed 22 STR markers using files of the high-coverage dataset of Phase 3 of the 1000 Genomes Project. We used HipSTR to call genotypes from 2504 samples obtained from 26 populations. We were not able to detect the D21S11 marker. The Hardy-Weinberg equilibrium analysis coupled with a comprehensive analysis of allele frequencies revealed that HipSTR was not able to identify longer alleles, which resulted in heterozygote deficiency. Nevertheless, AMOVA, a clustering analysis that uses STRUCTURE, and a Principal Coordinates Analysis showed a clear-cut separation between the four major ancestries sampled by the 1000 Genomes Consortium. Except for larger Penta D and Penta E alleles, and two very small Penta D alleles (2.2 and 3.2) usually observed in African populations, our analyses revealed that allele frequencies and genotypes offered as an open-access database are consistent and reliable.

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized buildings. Unfortunately, not all buildings are intelligent, making it difficult to obtain such data from energy measurement devices and appliances or access such information. The internet of things (IoT) opens new opportunities to support real-time monitoring and control to achieve future smart buildings. This work proposes an IoT platform for remote monitoring and control of smart buildings, which consists of four-layer architecture: power layer, data acquisition layer, communication network layer, and application layer. The proposed platform allows data collection for energy consumption, data storage, and visualization. Various sensor nodes and measurement devices are considered to collect information on energy use from different building spaces. The proposed solution has been designed, implemented, and tested on a university campus considering three scenarios: an office, a classroom, and a laboratory. This work provides a guideline for future implementation of intelligent buildings using low-cost open-source solutions to enable building automation, minimize power consumption costs, and guarantee end-user comfort., Competing Interests: The authors declare no conflict of interest.

There is a need to ensure comfortable conditions for hospital staff and patients from the point of view of thermal comfort and air quality so that they do not affect their performance. We consider the need for hospital employees and patients to enjoy conditions of greater well-being during their stay. This is understood as a comfortable thermal sensation and adequate air quality, depending on the task they are performing. The contribution of this article is the formulation of the fundamentals of a system and platform for monitoring thermal comfort and Indoor Air Quality (IAQ) in hospitals, based on an Internet of Things platform composed of a low-cost sensor node network that is capable of measuring critical variables such as humidity, temperature, and Carbon Dioxide (CO 2 ). As part of the platform, a multidimensional data model with an On-Line Analytical Processing (OLAP) approach is presented that offers query flexibility, data volume reduction, as well as a significant reduction in query response times. The experimental results confirm the suitability of the platform's data model, which facilitates operational and strategic decision making in complex hospitals.

Nature-inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well-known population-based bio-inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal's performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.

Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0-6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users' developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs.

The mechanism of cerebral blood flow autoregulation can be of great importance in diagnosing and controlling a diversity of cerebrovascular pathologies such as vascular dementia, brain injury, and neurodegenerative diseases. To assess it, there are several methods that use changing postures, such as sit-stand or squat-stand maneuvers. However, the evaluation of the dynamic cerebral blood flow autoregulation (dCA) in these postures has not been adequately studied using more complex models, such as non-linear ones. Moreover, dCA can be considered part of a more complex mechanism called cerebral hemodynamics, where others (CO 2 reactivity and neurovascular-coupling) that affect cerebral blood flow (BF) are included. In this work, we analyzed postural influences using non-linear machine learning models of dCA and studied characteristics of cerebral hemodynamics under statistical complexity using eighteen young adult subjects, aged 27 ± 6.29 years, who took the systemic or arterial blood pressure (BP) and cerebral blood flow velocity (BFV) for five minutes in three different postures: stand, sit, and lay. With models of a Support Vector Machine (SVM) through time, we used an AutoRegulatory Index (ARI) to compare the dCA in different postures. Using wavelet entropy, we estimated the statistical complexity of BFV for three postures. Repeated measures ANOVA showed that only the complexity of lay-sit had significant differences.

The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.

People with intellectual disabilities have more sedentary lifestyles than the general population. Regular physical activity is of both medical and social importance, reducing the risk of cardiovascular disease and promoting functioning in everyday life. Exergames have been envisioned for promoting physical activity; however, most of them are not user-friendly for individuals with intellectual disabilities. In this paper, we report the design, development, and user acceptance of a mobile health solution connected to sensors to motivate physical activity. The system is mounted on an indoor stationary bicycle and an ergometer bike tailored for people with intellectual disabilities. The development process involved the application of user-centered design principles to customize the system for this group. The system was pilot-tested in an institutional house involving six end-users (intervention group) and demonstrated/self-tested to relatives of persons with ID and staff (supervision group). A System Usability Scale and open-ended interview in the supervision group were used to assess the user acceptance and perceived usefulness. Results indicate that the users with an intellectual disability enjoyed using the system, and that respondents believed it was a useful tool to promote physical activity for the users at the institution. The results of this study provide valuable information on beneficial technological interventions to promote regular physical activity for individuals with intellectual disabilities.

Pressure injuries are wounds caused by reduced blood circulation for regular and repetitive periods when pressing the human body against a hard surface. It is a pathology that needs daily preventive care and health promotion to avoid incidences. Thus, this article aims to validate a platform based on the gamified collaborative practices model to prevent pressure injuries. The methodological contribution is Design Science, and the software was evaluated by 16 wheelchair users who aimed at usability (SUS), user experience (UEQ), and ability to promote the system (NPS). In addition to creating a collaborative network using the interactions that occurred during the platform's use. Wheelchair users rated 73.28 for SUS; on the UEQ benchmark scales, they classified: excellent (efficiency, dependability, stimulation, and novelty), good (attractiveness), and above-average (perspicuity) and in NPS obtained 62.5%. Moreover, they provided feedback to improve and legitimize that gamification positively interfered in the execution of collaborative practices. In the end, it is possible to suppose that the prototype contributes to the prevention of pressure injuries. In addition, it is possible to adapt it to apply to other pathologies that require continuous health care such as diabetes, mental illness, heart disease, addictions, multiple sclerosis, cancer, among others, maximizing its purpose.

In this research, a compact electronic nose (e-nose) based on a shear horizontal surface acoustic wave (SH-SAW) sensor array is proposed for the NO 2 detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH 3 ), benzene (C 6 H 6 ) and acetone (C 3 H 6 O). Carbon-based nanostructured materials (CBNm), such as mesoporous carbon (MC), reduced graphene oxide (rGO), graphene oxide (GO) and polydopamine/reduced graphene oxide (PDA/rGO) are deposited as a sensitive layer with controlled spray and Langmuir-Blodgett techniques. We show the potential of the mass loading and elastic effects of the CBNm to enhance the detection, the classification and the discrimination of NO 2 among different gases by using Machine Learning (ML) techniques (e.g., PCA, LDA and KNN). The small dimensions and low cost make this analytical system a promising candidate for the on-site discrimination of sub-ppm NO 2 .

The chemical composition and surface topography of titanium implants are essential to improve implant osseointegration. The present work studies a non-invasive alternative of electrical impedance spectroscopy for the characterization of the macroporosity inherent to the manufacturing process and the effect of the surface treatment with femtosecond laser of titanium discs. Osteoblasts cell culture growths on the titanium surfaces of the laser-treated discs were also studied with this method. The measurements obtained showed that the femtosecond laser treatment of the samples and cell culture produced a significant increase (around 50%) in the absolute value of the electrical impedance module, which could be characterized in a wide range of frequencies (being more relevant at 500 MHz). Results have revealed the potential of this measurement technique, in terms of advantages, in comparison to tiresome and expensive techniques, allowing semi-quantitatively relating impedance measurements to porosity content, as well as detecting the effect of surface modification, generated by laser treatment and cell culture.