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

Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages

Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (tabea) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (nlp) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous nlp nor online Machine Learning approaches to tabea.Xunta de Galicia | Ref. ED481B-2021-118Xunta de Galicia | Ref. ED481B-2022-093Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Demographic market segmentation on short banking movement descriptions applying Natural Language Processing

Xunta de Galicia | Ref. GRC2018/05

Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation

Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. They are typically written by market experts who describe stock market events within the context of social, economic and political change. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (NLP) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (LDA) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. Our solution outperformed a rule-based baseline system. We created an experimental data set composed of 2,158 financial news items that were manually labelled by NLP researchers to evaluate our solution. Inter-agreement Alpha-reliability and accuracy values, and ROUGE-L results endorse its potential as a valuable tool for busy investors. The ROUGE-L values for the identification of relevant text and predictions/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with LDA to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text. Our solution may have compelling applications in the financial field, including the possibility of extracting relevant statements on investment strategies to analyse authors’ reputations.Universidade de Vigo/CISUGXunta de Galicia | Ref. ED481B-2021-118Xunta de Galicia | Ref. ED481B-2022-09

Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing

Concerns about the effect of greenhouse gases have motivated the development of certification protocols to quantify the industrial carbon footprint (cf). These protocols are manual, work-intensive, and expensive. All of the above have led to a shift towards automatic data-driven approaches to estimate the cf, including Machine Learning (ml) solutions. Unfortunately, as in other sectors of interest, the decision-making processes involved in these solutions lack transparency from the end user’s point of view, who must blindly trust their outcomes compared to intelligible traditional manual approaches. In this research, manual and automatic methodologies for cf estimation were reviewed, taking into account their transparency limitations. This analysis led to the proposal of a new explainable ml solution for automatic cf calculations through bank transaction classification. Consideration should be given to the fact that no previous research has considered the explainability of bank transaction classification for this purpose. For classification, different ml models have been employed based on their promising performance in similar problems in the literature, such as Support Vector Machine, Random Forest, and Recursive Neural Networks. The results obtained were in the 90 % range for accuracy, precision, and recall evaluation metrics. From their decision paths, the proposed solution estimates the co2 emissions associated with bank transactions. The explainability methodology is based on an agnostic evaluation of the influence of the input terms extracted from the descriptions of transactions using locally interpretable models. The explainability terms were automatically validated using a similarity metric over the descriptions of the target categories. Conclusively, the explanation performance is satisfactory in terms of the proximity of the explanations to the associated activity sector descriptions, endorsing the trustworthiness of the process for a human operator and end users.Xunta de Galicia, Spain | Ref. ED481B-2021-118Xunta de Galicia, Spain | Ref. ED481B-2022-093Centro para el Desarrollo Tecnológico Industrial | Ref. EXP00146826/IDI-2022029

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

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-

Implementación e desenvolvemento de aulas de xeometría euclídea e diferencial en Sage

Este libro é continuación do manual "Implementación e desenvolvemento de aulas de matemáticas avanzadas en SAGE" (IDAMAS) publicado pola Universidade de Vigo en 2018 e dedícase ao tratamento en Sage da xeometría euclídea e xeometría diferencial, que son parte esencial na formación do enxeñeiro e, en particular, do enxeñeiro industrial. IDAMAS debe ser considerado unha lectura necesaria para extraer toda a utilidade a este segundo libr

Association of Candidate Gene Polymorphisms With Chronic Kidney Disease: Results of a Case-Control Analysis in the Nefrona Cohort

Chronic kidney disease (CKD) is a major risk factor for end-stage renal disease, cardiovascular disease and premature death. Despite classical clinical risk factors for CKD and some genetic risk factors have been identified, the residual risk observed in prediction models is still high. Therefore, new risk factors need to be identified in order to better predict the risk of CKD in the population. Here, we analyzed the genetic association of 79 SNPs of proteins associated with mineral metabolism disturbances with CKD in a cohort that includes 2, 445 CKD cases and 559 controls. Genotyping was performed with matrix assisted laser desorption ionizationtime of flight mass spectrometry. We used logistic regression models considering different genetic inheritance models to assess the association of the SNPs with the prevalence of CKD, adjusting for known risk factors. Eight SNPs (rs1126616, rs35068180, rs2238135, rs1800247, rs385564, rs4236, rs2248359, and rs1564858) were associated with CKD even after adjusting by sex, age and race. A model containing five of these SNPs (rs1126616, rs35068180, rs1800247, rs4236, and rs2248359), diabetes and hypertension showed better performance than models considering only clinical risk factors, significantly increasing the area under the curve of the model without polymorphisms. Furthermore, one of the SNPs (the rs2248359) showed an interaction with hypertension, being the risk genotype affecting only hypertensive patients. We conclude that 5 SNPs related to proteins implicated in mineral metabolism disturbances (Osteopontin, osteocalcin, matrix gla protein, matrix metalloprotease 3 and 24 hydroxylase) are associated to an increased risk of suffering CKD

Seguimiento de las guías españolas para el manejo del asma por el médico de atención primaria: un estudio observacional ambispectivo

Objetivo Evaluar el grado de seguimiento de las recomendaciones de las versiones de la Guía española para el manejo del asma (GEMA 2009 y 2015) y su repercusión en el control de la enfermedad. Material y métodos Estudio observacional y ambispectivo realizado entre septiembre del 2015 y abril del 2016, en el que participaron 314 médicos de atención primaria y 2.864 pacientes. Resultados Utilizando datos retrospectivos, 81 de los 314 médicos (25, 8% [IC del 95%, 21, 3 a 30, 9]) comunicaron seguir las recomendaciones de la GEMA 2009. Al inicio del estudio, 88 de los 314 médicos (28, 0% [IC del 95%, 23, 4 a 33, 2]) seguían las recomendaciones de la GEMA 2015. El tener un asma mal controlada (OR 0, 19, IC del 95%, 0, 13 a 0, 28) y presentar un asma persistente grave al inicio del estudio (OR 0, 20, IC del 95%, 0, 12 a 0, 34) se asociaron negativamente con tener un asma bien controlada al final del seguimiento. Por el contrario, el seguimiento de las recomendaciones de la GEMA 2015 se asoció de manera positiva con una mayor posibilidad de que el paciente tuviera un asma bien controlada al final del periodo de seguimiento (OR 1, 70, IC del 95%, 1, 40 a 2, 06). Conclusiones El escaso seguimiento de las guías clínicas para el manejo del asma constituye un problema común entre los médicos de atención primaria. Un seguimiento de estas guías se asocia con un control mejor del asma. Existe la necesidad de actuaciones que puedan mejorar el seguimiento por parte de los médicos de atención primaria de las guías para el manejo del asma. Objective: To assess the degree of compliance with the recommendations of the 2009 and 2015 versions of the Spanish guidelines for managing asthma (Guía Española para el Manejo del Asma [GEMA]) and the effect of this compliance on controlling the disease. Material and methods: We conducted an observational ambispective study between September 2015 and April 2016 in which 314 primary care physicians and 2864 patients participated. Results: Using retrospective data, we found that 81 of the 314 physicians (25.8%; 95% CI 21.3–30.9) stated that they complied with the GEMA2009 recommendations. At the start of the study, 88 of the 314 physicians (28.0%; 95% CI 23.4–33.2) complied with the GEMA2015 recommendations. Poorly controlled asthma (OR, 0.19; 95% CI 0.13–0.28) and persistent severe asthma at the start of the study (OR, 0.20; 95% CI 0.12–0.34) were negatively associated with having well-controlled asthma by the end of the follow-up. In contrast, compliance with the GEMA2015 recommendations was positively associated with a greater likelihood that the patient would have well-controlled asthma by the end of the follow-up (OR, 1.70; 95% CI 1.40–2.06). Conclusions: Low compliance with the clinical guidelines for managing asthma is a common problem among primary care physicians. Compliance with these guidelines is associated with better asthma control. Actions need to be taken to improve primary care physician compliance with the asthma management guidelines