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As climate change intensifies and 2023 sets the record for the hottest year, the Amazonian region faces severe drought, leading to a hydroelectricity crisis. This study examines the effects of using environmentally harmful backup power solutions, which are all too common in developing countries—specifically, diesel-powered generators—on urban air quality in Quito, Ecuador, during the hydroelectric crisis of 2023/2024. The impact of the blackouts on air pollution is assessed by comparing it to a reference period before the crisis and to an earlier year with similar weather conditions. The findings indicate that extended blackouts (up to 8 h per day) considerably increased concentrations of SO2 (180%), CO (43%), NO2 (39%), and PM2.5 (20%) compared to ‘normal’ conditions. Conversely, O3 pollution levels decreased (−6%). Our findings suggest potential respiratory and cardiovascular health risks for the urban population, highlighting the urgent need for improved energy infrastructure and cleaner backup power solutions in the developing world. Addressing these challenges is critical for ensuring a healthier and more sustainable urban future amidst climate change projections.

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers.This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study.Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1

To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates.

Abstract Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall − 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Atmospheric pollution is one of the major causes of premature mortality and climate change, as nearly all urban areas fail to comply with the air quality guidelines of the World Health Organization (WHO) [...]

Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: −48.75%, for CO, −45.76%, for SO2, −42.17%, for PM2.5, and −63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.

In the recent years, software applications for medical assistance, including the tele-rehabilitation, have known a high and continuous presence in the medical field. Guaranteeing the correct use of these applications induces to incorporate a study of usability in the life cycle of any interactive system. This paper synthesizes the results obtained from a study of effectiveness, efficiency, and subjective user satisfaction conducted with ePHoRt. ePHoRt is a web-based platform for home motor rehabilitation; 39 participants tested the platform. We report the empirical results based on the subjective usability perception and self-reported feedback based on the IBM Computer System Usability Questionnaire. The results suggest that ePHoRt is useful, effective, efficient, easy to use, and its interfaces are acceptable. Overall, the participants are satisfied to use the platform. The main findings of this paper show that the user guidance is a critical aspect to ensure a good usability of the tele-rehabilitation platform.

In recent years, the software applications for medical assistance, including the tele-rehabilitation, have known a high and a continuous presence in the medical area. The ePHoRt is a Web-based platform for the remote home monitoring rehabilitation exercises in patients after hip replacement surgery. It involves a learning phase and a serious game scheme for the execution and evaluation of the exercises as part of a therapeutic program. Modular software architecture is proposed, under the patient perspective, to be used as a reference model for researchers or professionals who wish to carry out tele-rehabilitation platforms, and to guarantee security, flexibility, and scalability. The architecture incorporates two main components. The first one manages the patient' therapeutic programs taking into account two principles: 1) maintain loose coupling between the layers of the framework and 2) Don't Repeat Yourself (DRY). The second one evaluates the performed exercises in real time considering an independent acquisition mechanism for the patient movements and two artificial algorithms. The first algorithm allows evaluating the quality of the movements, while the second one allows assessing the levels of pain intensity by recognizing the patient' emotions when performing the movements. Details of the components and the meta-model of the architecture are presented and discussed considering their advantages and disadvantages.

Political and economic protests build-up due to the financial uncertainty and inequality spreading throughout the world. In 2019, Latin America took the main stage in a wave of protests. While the social side of protests is widely explored, the focus of this study is the evolution of gaseous urban air pollutants during and after one of these events. Changes in concentrations of NO2, CO, O3 and SO2 during and after the strike, were studied in Quito, Ecuador using two approaches: (i) inter-period observational analysis; and (ii) machine learning (ML) gradient boosting machine (GBM) developed business-as-usual (BAU) comparison to the observations. During the strike, both methods showed a large reduction in the concentrations of NO2 (31.5–32.36%) and CO (15.55–19.85%) and a slight reduction for O3 and SO2. The GBM approach showed an exclusive potential, especially for a lengthier period of predictions, to estimate strike impact on air quality even after the strike was over. This advocates for the use of machine learning techniques to estimate an extended effect of changes in human activities on urban gaseous pollution.

Daily activities are characterized by an increasing interaction with smart machines that present a certain level of autonomy. However, the intelligence of such electronic devices is not always transparent for the end user. This study is aimed at assessing the quality of the remote control of a mobile robot whether the artefact exhibits a human-like behavior or not. The bioinspired behavior implemented in the robot is the well-described two-thirds power law. The performance of participants who teleoperate the semiautonomous vehicle implementing the biological law is compared to a manual and nonbiological mode of control. The results show that the time required to complete the path and the number of collisions with obstacles are significantly lower in the biological condition than in the two other conditions. Also, the highest percentage of occurrences of curvilinear or smooth trajectories are obtained when the steering is assisted by an integration of the power law in the robot’s way of working. This advanced analysis of the performance based on the naturalness of the movement kinematics provides a refined evaluation of the quality of the Human-Machine Interaction (HMI). This finding is consistent with the hypothesis of a relationship between the power law and jerk minimization. In addition, the outcome of this study supports the theory of a CNS origin of the power law. The discussion addresses the implications of the anthropocentric approach to enhance the HMI.

Objective. To analyse the effect of the manual ischemic compression (IC) on the upper limb motor performance (MP) in patients with LTrPs. Materials and Methods. A quasiexperimental study was performed in twenty subjects allocated to either patients group with LTrPs (PG, n=10) or healthy group with no symptoms (HG, n=10). Subjective pain and linear MP (movement time and Fitts’ Law) were assessed before and after a linear tapping task. Data were analysed with mixed factorial ANOVA for intergroup linear motor performance differences and dependent t-student test for intragroup pain differences. Results. PG had a linear MP lower than the HG before treatment (p < 0.05). After IC, the PG showed a significant decrease of pain (4.07 ± 1.91 p < 0.001). Furthermore, the movement time (15.70 ± 2.05 p < 0.001) and the Fitts’ Law coefficient (0.80 ± 0.53 p < 0.001) were significantly reduced. However, one IC session did not allow the PG to get the same MP than the HG (p < 0.05). Conclusion. The results suggest the IC effectiveness on pain and MP impairment in subjects with LTrPs. However, the MP of these patients is only partially improved after the IC application.

As global urbanization, industrialization, and motorization keep worsening air quality, a continuous rise in health problems is projected. Limited spatial resolution of the information on air quality inhibits full comprehension of urban population exposure. Therefore, we propose a method to predict urban air pollution from traffic by extracting data from Web-based applications (Google Traffic). We apply a machine learning approach by training a decision tree algorithm (C4.8) to predict the concentration of PM2.5 during the morning pollution peak from: (i) an interpolation (inverse distance weighting) of the value registered at the monitoring stations, (ii) traffic flow, and (iii) traffic flow + time of the day. The results show that the prediction from traffic outperforms the one provided by the monitoring network (average of 65.5% for the former vs. 57% for the latter). Adding the time of day increases the accuracy by an average of 6.5%. Considering the good accuracy on different days, the proposed method seems to be robust enough to create general models able to predict air pollution from traffic conditions. This affordable method, although beneficial for any city, is particularly relevant for low-income countries, because it offers an economically sustainable technique to address air quality issues faced by the developing world.

This study aims to develop a telemedicine platform for self-motor rehabilitation and remote monitoring by health professionals, in order to enhance recovery in patients after hip replacement. The implementation of such a technology is justified by medical (improvement of the recovery process by the possibility to perform rehabilitation exercises more frequently), economic (reduction of the number of medical appointments and the time patients spend at the hospital), mobility (diminution of the transportation to and from the hospital) and ethics (healthcare democratization and increased empowerment of the patient) purposes. The Kinect camera is used as a Natural User Interface to capture the physical exercises performed at home by the patients. The quality of the movement is evaluated in real-time by an assessment module implemented according to a Hidden-Markov Model approach. The results show a high accuracy in the evaluation of the movements (92% of correct classification). Finally, the usability of the platform is tested through the System Usability Scale (SUS). The overall SUS score is 81 out of 100, which suggests a good usability of the Web application. Further work will focus on the development of additional functionalities and an evaluation of the impact of the platform on the recovery process.

The evolution of technology has risen new possibilities in the so called Natural User Interfaces research area. Among distinct initiatives, several researchers are working with the existing sensors towards improving the support to gesture languages. This article tackles the recognition of gestures, using the Kinect sensor, in order to create a gesture library and support the gesture recognition processes afterwards.

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3) and low (

The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a telerehabilitation platform. To improve the level of usability, the researchers developed and proposed an iterative process. The platform uses a digital representation of the patient which duplicates the therapeutic exercise being executed by the patient; this is detected by a Kinect camera and sensors in real time. This study used inspection methods to perform a usability evaluation of an exploratory prototype of a telerehabilitation platform. In addition, a cognitive workload assessment was performed to complement the usability evaluation. Users were involved through all the stages of the iterative refinement process. Usability issues were progressively reduced from the first iteration to the fourth iteration according to improvements which were developed and applied by the experts. Usability issues originally cataloged as catastrophic were reduced to zero, major usability problems were reduced to ten (2.75%) and minor usability problems were decreased to 141 (38.74%). This study also intends to serve as a guide to improve the usability of e-Health systems in alignment with the software development cycle.

Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

The enhancement of ubiquitous and pervasive computing brings new perspectives in medical rehabilitation. In that sense, the present study proposes a smart, web-based platform to promote the reeducation of patients after hip replacement surgery. This project focuses on two fundamental aspects in the development of a suitable tele-rehabilitation application, which are: (i) being based on an affordable technology, and (ii) providing the patients with a real-time assessment of the correctness of their movements. A probabilistic approach based on the development and training of ten Hidden Markov Models (HMMs) is used to discriminate in real time the main faults in the execution of the therapeutic exercises. Two experiments are designed to evaluate the precision of the algorithm for classifying movements performed in the laboratory and clinical settings, respectively. A comparative analysis of the data shows that the models are as reliable as the physiotherapists to discriminate and identify the motion errors. The results are discussed in terms of the required setup for a successful application in the field and further implementations to improve the accuracy and usability of the system.