A computational model for adaptive recording of vital signs through context histories

Aranda, Jorge; Bavaresco, Rodrigo Simon; Carvalho, Juliano Varella de; Yamin, Adenauer; Tavares, Maurício Campelo; Barbosa, Jorge Luís Victória

doi:10.1007/s12652-021-03126-8

Cited by 29 publications

(27 citation statements)

References 54 publications

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“…Recently, ubiquitous computing has been empowered with the use of temporal series of contexts to organize and analyze the data. This new research area received the name of Context Histories [37], [38], [39], [40] or Trails [41], [42]. This kind of data organization allows the exploration of advance strategies to data analysis, such as, profile management [43], [44], [45], [46], [47], pattern analysis [48], context prediction [49] and similarity analysis [50], [51].…”

Section: Context-aware Ubiquitous Learningmentioning

confidence: 99%

ULearnEnglish: An open Ubiquitous System for Assisting in the English Vocabulary Learning

Silva¹,

EGdA²,

Rodríguez‐Sánchez³

et al. 2021

Preprint

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Language learners often face communication problems when they need to express themselves and do not have this ability. On the other hand, continuous advances in technology create new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents the ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate the proposal, 15 learners used the system developed, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the use of the learner context to assist them in their learning. The ULearnEnglish achieved an acceptance of 78.66% for the perception of the utility, 96% for the perception of ease of use, 86% for user context assessment, and 88% for ubiquity. This study presented a positive response in using the location of users to assist their learning. Among the main contributions, this study demonstrates an opportunity for ubiquity use in future research in language learning. Also, furthers studies can use the source available to evolve the model and system.

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Section: Context-aware Ubiquitous Learningmentioning

confidence: 99%

ULearnEnglish: An open Ubiquitous System for Assisting in the English Vocabulary Learning

Silva¹,

EGdA²,

Rodríguez‐Sánchez³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, in recent years Ambient Intelligence [60] and Smart Environments [55] have used time series of Contexts [56] to organize and analyze data. This type of data organization is called Context Histories [57][58][59][60]. Future work will explore the use of Context Histories to organize the data produced during the workshops, allowing the use of these data to include intelligence in patient care environments.…”

Section: Final Remarksmentioning

confidence: 99%

Learning and Well-being in Educational Practices with Children and Adolescents Undergoing Cancer Treatment

Santos¹,

Barbosa²,

Neto³

et al. 2021

Preprint

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The use of Information Communication Technologies (ICT) in education brings up new possibilities of promoting the learning and health experiences. In this sense, education contexts of 21st century must consider these two areas of knowledge, especially their integration. This article presents learning practices developed with mobile devices and games, in order to improve learning and well-being in children and adolescents undergoing cancer treatment in non-formal educational setting. The methodology is based on qualitative case studies with content-based data analysis, involving informal interviews and observation methods. The study considers data from 5 patients who participated in the research between 2015 and 2019. The results demonstrate a positive influence of the practices with mobile technologies and games in terms of learning and in the well-being feeling of patients during the treatment.

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“…Finally, future work will explore the use of Context Histories [46][47][48] to organize the data, allowing pattern analysis [49], context prediction [50] and similarity analysis [51]. These strategies for handling context histories will improve the analysis of the data, mainly allowing the prediction and recommendation oriented to the safety of drivers on the highways.…”

Section: Final Considerationsmentioning

confidence: 99%

RoadLytics: Road Accidents Analytics Using Artificial Intelligence to Support Deaths’ Prevention on Highways

K¹,

JEdR²,

Barbosa³

et al. 2021

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Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.

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A computational model for adaptive recording of vital signs through context histories

Cited by 29 publications

References 54 publications

ULearnEnglish: An open Ubiquitous System for Assisting in the English Vocabulary Learning

ULearnEnglish: An open Ubiquitous System for Assisting in the English Vocabulary Learning

Learning and Well-being in Educational Practices with Children and Adolescents Undergoing Cancer Treatment

RoadLytics: Road Accidents Analytics Using Artificial Intelligence to Support Deaths’ Prevention on Highways

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