How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators

This piece of research is situated in the domain of multi-modal analytics. New commercial off the shelf wearables, such as smartwatches or wristbands, are becoming popular and increasingly used for fitness and wellness in a new trend known as the quantified-self movement. The sensors included in these devices (e.g. accelerometer, heart rate) in conjunction with data analytics algorithms are used to provide information such as steps walked, calories consumed, etc. The main goal of this piece of research is to check if new wearable technologies could be used to estimate sleep indicators in an automatic way. The available medical literature proposes several sleep-related features and methods to calculate them involving direct user observation, interviews or specific medical instrumentation. Off the shelf wearable vendors also provide some sleep indicators, such as the sleep duration, the number of awakes or the time to fall asleep. Taking as a reference the results and methods described in the medical literature and the data available in commercial off the shelf wearables, we propose new sleep indicators offering a greater interpretative value: sleep quality, sleepiness level, chronotype. The results obtained after initial experiments demonstrate the feasibility of this approach to be applied in real contexts. Eventually, we plan to apply these solutions to support educational scenarios related to self-regulated learning and teaching support.Agencia Estatal de Investigación | Ref. TIN2016-80515-RXunta de Galicia | Ref. GRC2013-006Universidade de Vig

Evaluation of commercial-off-the-shelf wrist wearables to estimate stress on students

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. These devices include sensors to gather data on physiological signals such as heart rate, skin temperature or galvanic skin response. By applying data analytics techniques to these kinds of signals, it is possible to obtain estimations of higher-level aspects of human behavior. In the literature, there are several works describing the use of physiological data collected using clinical devices to obtain information on sleep patterns or stress. However, it is still an open question whether data captured using COTS wrist wearables is sufficient to characterize the learners' psychological state in educational settings. This paper discusses a protocol to evaluate stress estimation from data obtained using COTS wrist wearables. The protocol is carried out in two phases. The first stage consists of a controlled laboratory experiment, where a mobile app is used to induce different stress levels in a student by means of a relaxing video, a Stroop Color and Word test, a Paced Auditory Serial Addition test, and a hyperventilation test. The second phase is carried out in the classroom, where stress is analyzed while performing several academic activities, namely attending to theoretical lectures, doing exercises and other individual activities, and taking short tests and exams. In both cases, both quantitative data obtained from COTS wrist wearables and qualitative data gathered by means of questionnaires are considered. This protocol involves a simple and consistent method with a stress induction app and questionnaires, requiring a limited participation of support staff.Agencia Estatal de Investigación | Ref. TIN2016-80515-

Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables

This paper discusses the possibility of detecting personal stress making use of popular wearable devices available in the market. Different instruments found in the literature to measure stress-related features are reviewed, distinguishing between subjective tests and mechanisms supported by the analysis of physiological signals from clinical devices. Taking them as a reference, a solution to estimate stress based on the use of commercial-off-the-shelf wrist wearables and machine learning techniques is described. A mobile app was developed to induce stress in a uniform and systematic way. The app implements well-known stress inducers, such as the Paced Auditory Serial Addition Test, the Stroop Color-Word Interference Test, and a hyperventilation activity. Wearables are used to collect physiological data used to train classifiers that provide estimations on personal stress levels. The solution has been validated in an experiment involving 19 subjects, offering an average accuracy and F-measures close to 0.99 in an individual model and an accuracy and F-measure close to 0.85 in a global 2-level classifier model. Stress can be a worrying problem in different scenarios, such as in educational settings. Thus, the last part of the paper describes the proposal of a set of stress related indicators aimed to support the management of stress over time in such settings.Agencia Estatal de Investigación | Ref. TIN2016-80515-RUniversidade de Vig

Practical approach to designing and implementing a recommendation system for healthy challenges

Background: The COVID-19 pandemic has worsened sedentary lifestyles and unhealthy eating habits. It is crucial to promote proper training and healthy habits for all to prevent physical and cognitive decline. This should be a priority in health and education initiatives to reduce deaths and noncommunicable diseases. Guidelines for nutrition, physical activity, and sleep emphasize the importance of healthy habits. The goal is to develop a recommendation tool with a diverse range of challenges to positively impact users’ health. Methods: The process involves thoroughly obtaining precise user profiles through widely used questionnaires such as the Short-Form Health survey, the short Healthy Eating Index, and the Oviedo Sleep Questionnaire, and characterizing the challenges. Then, an algorithm will be developed to identify and prioritize the most suitable challenges for each user, ensuring personalized recommendations. Results: A pool of 30 health challenges was created based on reputable recommendations and experts. The system underwent validation by external experts and received positive user feedback, confirming its effectiveness. The panel of experts and users validated the personalized and reliable recommendations. Conclusions: Simple lifestyle interventions have shown promise for primary prevention in developed countries. A prototype system has been created to evaluate the individual weakness of users and suggest evidence-based lifestyle challenges. The system conducts a thorough health assessment and ensures feasibility for preventive purposes. Validation has proven the system’s effectiveness in recommending health-enhancing challenges with no adverse effects. The design of the model supports the seamless addition of new challenges by eventual third parties, ensuring interoperability and scalability.Agencia Estatal de Investigación | Ref. PID2020-115137RB-I00Xunta de Galicia | Ref. ED481A-2021/35

Arquitectura de ordenadores: ejercicios prácticos de ARM-Thumb

La arquitectura ARM, desarrollada hace más de 35 años, está presente en la actualidad en miles de millones de dispositivos, desde Fugaku, el superordenador japonés líder del ranking de los ordenadores más potentes del mundo en 2020, hasta el Catweazle Mini, un femtocomputador de poco más de 0,25 centímetros cúbicos de volumen. Thumb es una versión reducida de ARM en la que la mayoría de las instrucciones ocupan 16 bits, y está orientada fundamentalmente al desarrollo de dispositivos embebidos, como por ejemplo los dispositivos para controlar un ascensor, un electrodoméstico, determinadas funciones de un automóvil, y en general cualquier dispositivo del ámbito del Internet de las Cosas. El presente libro está basado en las clases prácticas y exámenes prácticos que los autores han ido preparando a lo largo de más de veinte años de experiencia impartiendo las asignaturas de arquitectura de ordenadores en los diversos planes de estudio de la Escuela de Ingeniería de Telecomunicación de la Universidade de Vigo

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe