scholarly journals A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

2021 ◽  
Vol 8 ◽  
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden ◽  
Rubiel Vargas-Cañas
Keyword(s):  
Emotional Responses ◽  
Main Tool ◽  
Data Driven ◽  
Experiment Planning ◽  
Musical Structures ◽  
Musical Perception ◽  
Different Types ◽  
Data Driven Approach ◽  
Physical Diseases ◽  
The Impact

Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

La orientación al dato, nuevos desafíos

Tábula ◽  
2021 ◽  
Author(s):  
Miguel Ángel Amutio Gómez
Keyword(s):  
European Union ◽  
Digital Transformation ◽  
Data Driven ◽  
The European Union ◽  
Data Preservation ◽  
The Future ◽  
Data Driven Approach ◽  
New Challenges ◽  
The Impact

La orientación al dato en el contexto de la transformación digital lleva aparejada la aparición de nuevas regulaciones, dinámicas de gobernanza y roles, y servicios, junto con las correspondientes prácticas, instrumentos y estándares. A la vez se suscitan retos en relación con la ciberseguridad y la preservación de los datos. En este artículo se exponen la transformación digital y la orientación al dato, la proyección de lo anterior en la administración digital, el contexto de la Unión Europea, trayectoria y su orientación, aspectos de la interoperabilidad, ciberseguridad y preservación de los datos, cuestiones de gobernanza y roles en la orientación al dato y, finalmente, unas conclusiones. The data-driven approach in the context of digital transformation entails the appearance of new regulations, governance dynamics and roles, and services, together with the corresponding practices, instruments and standards. At the same time new challenges appear in relation to cybersecurity and data preservation. This article presents the digital transformation and data-driven approach, the impact in digital administration, the context of the European Union, trajectory and orientation towards the future, along with aspects of interoperability, cybersecurity and data preservation, as well as issues of governance and roles in data orientation and finally some conclusions.

scholarly journals A Review of Explicit and Implicit Assumptions When Providing Personalized Feedback Based on Self-Report EMA Data

2021 ◽  
Vol 12 ◽  
Author(s):  
IJsbrand Leertouwer ◽  
Angélique O. J. Cramer ◽  
Jeroen K. Vermunt ◽  
Noémi K. Schuurman
Keyword(s):  
Self Report ◽  
Data Driven ◽  
Evidence Based ◽  
Personalized Feedback ◽  
Retrospective Assessment ◽  
Different Types ◽  
Ecological Momentary ◽  
Ema Data ◽  
The Impact ◽  
Momentary Assessment

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure.

scholarly journals A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow

2020 ◽  
pp. 1-9
Author(s):  
Amir Bahador Parsa ◽  
Ramin Shabanpour ◽  
Abolfazl (Kouros) Mohammadian ◽  
Joshua Auld ◽  
Thomas Stephens
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Data Driven ◽  
Data Driven Approach ◽  
The Impact

scholarly journals Built Environment Evaluation in Virtual Reality Environments—A Cognitive Neuroscience Approach

Urban Science ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 48
Author(s):  
Ming Hu ◽  
Jennifer Roberts
Keyword(s):  
Virtual Reality ◽  
Built Environment ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Emotional Responses ◽  
Data Driven ◽  
Environment Quality ◽  
Immersive Virtual Environment ◽  
Preliminary Validation ◽  
Data Driven Approach

To date, the predominant tools for the evaluation of built environment quality and impact have been surveys, scorecards, or verbal comments—approaches that rely upon user-reported responses. The goal of this research project is to develop, test, and validate a data-driven approach for built environment quality evaluation/validation based upon measurement of real-time emotional responses to simulated environments. This paper presents an experiment that was conducted by combining an immersive virtual environment (virtual reality) and electroencephalogram (EEG) as a tool to evaluate Pre and Post Purple Line development. More precisely, the objective was to (a) develop a data-driven approach for built environment quality evaluation and (b) understand the correlation between the built environment characters and emotional state. The preliminary validation of the proposed evaluation method identified discrepancies between traditional evaluation results and emotion response indications through EEG signals. The validation and findings have laid a foundation for further investigation of relations between people’s general cognitive and emotional responses in evaluating built environment quality and characters.

Will the relevance of review language and destination attractions be helpful? A data-driven approach

2020 ◽  
pp. 135676672095035
Author(s):  
Sunyoung Hlee ◽  
Hyunae Lee ◽  
Chulmo Koo ◽  
Namho Chung
Keyword(s):  
Online Reviews ◽  
Data Driven ◽  
Online Review ◽  
Cognitive Fit ◽  
Theoretical Contribution ◽  
Language Style ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Tourism Destinations ◽  
The Impact

Because tourism destinations are difficult to assess in certain standard aspects, the factors that contribute to the helpfulness of reviews remain largely unknown. Moreover, the helpfulness of online reviews has not been explored in terms of the interaction between language style (high- vs. low-cognitive) and attraction type (hedonic vs. utilitarian). Hence, this study examines the impact of language style on the helpfulness of an online review of an attraction, depending on the type of attraction and the meaning of the destination. This study’s data included 8,032 reviews of four attractions (2 hedonic x 2 utilitarian), drawn from TripAdvisor in two different meanings of destinations. Specifically, our findings indicate that when a reviewer posts a utilitarian attraction of the destination, high-cognitive language is perceived to be more helpful. First, we discuss the theoretical contribution of our study using cognitive fit theory, and then provide the study’s managerial implications.

scholarly journals Discovery of the Environmental Factors Affecting Urban Dwellers’ Mental Health: A Data-Driven Approach

2020 ◽  
Vol 17 (21) ◽  
pp. 8167
Author(s):  
Chao Wu ◽  
Pei Zheng ◽  
Xinyuan Xu ◽  
Shuhan Chen ◽  
Nasi Wang ◽  
...  
Keyword(s):  
Mental Health ◽  
Environmental Factors ◽  
Mental States ◽  
Data Driven ◽  
Neural Network Modeling ◽  
Starting Point ◽  
Data Driven Approach ◽  
Linear And Nonlinear ◽  
The Impact ◽  
Maximal Information Coefficient

Mental health is the foundation of health and happiness as well as the basis for an individual’s meaningful life. The environmental and social health of a city can measure the mental state of people living in a certain areas, and exploring urban dwellers’ mental states is an important factor in understanding and better managing cities. New dynamic and granular urban data provide us with a way to determine the environmental factors that affect the mental states of urban dwellers. The characteristics of the maximal information coefficient can identify the linear and nonlinear relationships so that we can fully identify the physical and social environmental factors that affect urban dwellers’ mental states and further test these relationships through linear and nonlinear modeling. Taking the Greater London as an example, we used data from the London Datastore to discover the environmental factors that had the highest correlation with urban mental health from 2015 to 2017 and to prove that they had a high nonlinear correlation through neural network modeling. This paper aimed to use a data-driven approach to find environmental factors that had not yet received enough attention and to provide a starting point for research by establishing hypotheses for further exploration of the impact of environmental factors on mental health.

scholarly journals Immunodietica: A data-driven approach to investigate interactions between diet and autoimmune disorders

2019 ◽  
Author(s):  
Iosif M. Gershteyn ◽  
Leonardo M.R. Ferreira
Keyword(s):  
Risk Factor ◽  
Autoimmune Disease ◽  
Autoimmune Disorders ◽  
Data Driven ◽  
Human Autoimmune Disease ◽  
Future Studies ◽  
Peptide Epitopes ◽  
Data Driven Approach ◽  
The Impact ◽  
Pork Consumption

AbstractAutoimmunity is on the rise around the globe. Diet has been proposed as a risk factor for autoimmunity and shown to modulate the severity of several autoimmune disorders. Yet, the interaction between diet and autoimmunity in humans remains largely unstudied. Here, we systematically interrogated commonly consumed animals and plants for peptide epitopes previously implicated in human autoimmune disease. A total of fourteen species investigated could be divided into three broad categories regarding their content in human autoimmune epitopes, which we represented using a new metric, the Gershteyn-Ferreira index (GF index). Strikingly, pig contains a disproportionately high number of unique autoimmune epitopes compared to all other species analyzed. This work uncovers a potential new link between pork consumption and autoimmunity in humans and lays the foundation for future studies on the impact of diet on the pathogenesis and progression of autoimmune disorders.

scholarly journals Measuring sign complexity: Comparing a theory-driven and a data-driven approach

2022 ◽  
Author(s):  
Justine Mertz ◽  
Chiara Annucci ◽  
Valentina Aristodemo ◽  
Beatrice Giustolisi ◽  
Doriane Gras ◽  
...  
Keyword(s):  
Error Rates ◽  
Data Driven ◽  
Sign Languages ◽  
Fine Grained ◽  
Geometry Model ◽  
Feature Geometry ◽  
Data Driven Approach ◽  
Two Measures ◽  
Orientation Movement ◽  
The Impact

The study of articulatory complexity has proven to yield useful insights into the phonological mechanisms of spoken languages. In sign languages, this type of knowledge is scarcely documented. The current study compares a data-driven measure and a theory-driven measure of complexity for signs in French Sign Language (LSF). The former measure is based on error rates of handshape, location, orientation, movement and sign fluidity in a repetition task administered to non-signers; the latter measure is derived by applying a feature-geometry model of sign description on the same set of signs. A significant correlation is found between the two measures for the overall complexity. When looking at the impact of individual phonemic classes on complexity, a significant correlation is found for handshape and location but not for movement. We discuss how these results indicate that a fine-grained theoretical model of sign phonology/phonetics reflects the degree of complexity as resulting from the perceptual and articulatory properties of signs.

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