1,054 research outputs found
Flexible learning intinerary vs. linear learning itinerary
The latest video game and entertainment technology and other technologies are facilitating the development of new and powerful e-Learning systems. In this paper, we present a computer-based game for learning about five historical ages. The objective of the game is to reinforce the events that mark the transition from one historical age to another and the order of the historical ages. Our game incorporates natural human-computer interaction based on video game technology, Frontal Projection, and personalized learning. For personalized learning, a Flexible Learning Itinerary has been included, where the children can decide how to direct the flow of their own learning process. For comparison, a Linear Learning Itinerary has also been included, where the children follow a determined learning flow. A study to compare the two different learning itineraries was carried out. Twenty nine children from 8 to 9 years old participated in the study. The analysis of the pre-tests and the post-tests determined that children learned the contents of a game about historical ages. The results show that there were no statistically significant differences between the two learning itineraries. Therefore, our study reveals the potential of computer-based learning games as a tool in the learning process for both flexible and linear itinerariesThis work was funded by the Spanish APRENDRA project (TIN2009-14319-C02-01).Martín San José, JF.; Juan Lizandra, MC.; Gil Gómez, JA.; Rando, N. (2014). Flexible learning intinerary vs. linear learning itinerary. Science of Computer Programming. 88:3-21. https://doi.org/10.1016/j.scico.2013.12.009S3218
From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy
This chapter addresses the following question: What are the advantages of extending a fuzzy expert system (FES) to an artificial neural network (ANN), within a computer‐based speech therapy system (CBST)? We briefly describe the key concepts underlying the principles behind the FES and ANN and their applications in assisted speech therapy. We explain the importance of an intelligent system in order to design an appropriate model for real‐life situations. We present data from 1‐year application of these concepts in the field of assisted speech therapy. Using an artificial intelligent system for improving speech would allow designing a training program for pronunciation, which can be individualized based on specialty needs, previous experiences, and the child\u27s prior therapeutical progress. Neural networks add a great plus value when dealing with data that do not normally match our previous designed pattern. Using an integrated approach that combines FES and ANN allows our system to accomplish three main objectives: (1) develop a personalized therapy program; (2) gradually replace some human expert duties; (3) use “self‐learning” capabilities, a component traditionally reserved for humans. The results demonstrate the viability of the hybrid approach in the context of speech therapy that can be extended when designing similar applications
A State-of-the Art Survey on Chatbots Technology Developments and Applications in Primary Healthcare Domain
Chatbots, defined as artificial intelligence program able to simulate processes of human conversation via auditory or textual methods, are deployed by firms to automate customer service. In recent years, chatbots have received tremendous attention from scholars in numerous fields including e-health, e-learning, and e-commerce over many sectors. However, the technology developments and applications specifically in the primary healthcare domain are still insufficiently explored. The principal purpose of the study is to provide a broad review of the current technology developments and applications in primary healthcare domain and future directions in the research. First, we describe features of chatbots considering the healthcare domain. Next, we provide a classification of technology developments and applications in primary healthcare with a focus on recent advances. Then, we present a density map of applications in the primary healthcare domain. Furthermore, we introduce future directions in the core research technology. We expect this study to serve as a comprehensive resource for researchers in healthcare domain
Evaluation of learning outcomes using an educational iPhone game vs. traditional game
In this paper, we present an initial study to determine the subject preferences for educational computer
games for children, in which 150 education professionals participated. From the results of this
first study, we have developed an iPhone game for transmitting knowledge as part of multiculturalism,
solidarity and tolerance following established learning theories, several design principles, and the
objectives and competences of the Spanish law for primary education. We also report on a second
study to determine whether the iPhone game has better learning outcomes than a traditional game by
analyzing the participation of 84 children ranging in age from 8 to 10 years old. The frequency of
playing with consoles or computer games was also taken into account in this second study, and the
worldwide trend of previous studies has been corroborated. For learning outcomes, the results did not
show significant differences between the two groups. However, 96% of the children indicated that they
would like to play with the iPhone game again, and 90% indicated that they preferred the experience
with the iPhone game over the traditional one. From these results, we can conclude that the children
achieved similar knowledge improvements using both the autonomous game (iPhone game) and the
custom, guided game (traditional game). This could facilitate versatility in the learning process since
the learning activity could be performed at any place and time without requiring supervision.
Therefore, it could be a useful tool in the learning process and help teachers to fulfill students' training
needs.
2013 Elsevier Ltd. All rights reserved.This work was funded by the Spanish APRENDRA project (TIN2009-14319-C02).Furió Ferri, D.; González Gancedo, S.; Juan, M.; Seguí, I.; Rando, N. (2013). Evaluation of learning outcomes using an educational iPhone game vs. traditional game. Computers and Education. 64:1-23. https://doi.org/10.1016/j.compedu.2012.12.001S1236
The Home Program Adherence Tackle Box: A Fishing-Themed Toolkit for Rehabilitation Clinicians
Background: Home programs (HPs) are an important part of the rehabilitation experience and are regularly recommended by occupational therapy practitioners (OTPs) and other rehabilitation clinicians for continuation and supplementation of care, to address a client’s continual needs at home and in the community (DeForge et al., 2008; Donoso Brown, & Fichter, 2017; Emmerson et al., 2017; Picha, & Howell, 2018; Proffitt, 2016). Home programs created by rehabilitation professionals for clients typically include therapeutic exercises, activities, and lifestyle behavior modifications to compliment treatment and/or discharge recommendations. Issuing home programs is an established standard of care to help clients meet targeted client goals and outcomes (Proffitt, 2016). The data in the literature suggests that rates of adherence to rehabilitation home programs are lower than acceptable ranging from 40-70% across various populations (DeForge et al., 2008; Donoso Brown, & Fichter, 2017; Emmerson et al., 2017; Picha, & Howell, 2018; Proffitt, 2016). According to the World Health Organization (2003), adherence is considered a key factor influencing treatment effectiveness and optimal client outcomes, especially when considering lifestyle interventions. Adherence is a complex and multifactorial issue, which may explain why it goes largely unaddressed by practitioners due to the many associated barriers of healthcare systems, providers, and clients (WHO, 2016). Currently, literature is limited regarding occupational therapy’s role in assessing and addressing barriers to home program adherence.
Purpose: The purpose of this scholarly project was to develop a user-friendly guide and toolkit designed for rehabilitation practitioners to therapeutically “tackle” the complex, multifactorial challenges and barriers associated with a client’s adherence to HPs, many of which are potentially modifiable with targeted interventions (Picha, & Howell, 2018).
Methods: The contributing developers of this product conducted an extensive literature review to determine: (1) current use and prescription of HPs in rehabilitation; (2) barriers and facilitators of adherence to HPs; (3) current use of adherence tools used in rehabilitation; and (4) best practice principles for promoting adherence for HPs.
Conclusion: The results of the literature review guided the development of the product, the Home Program Adherence Tackle Box. The Home Program Adherence Tackle Box contains client centered strategies and critical guiding resources that OTPs, and other clinical rehabilitation enthusiasts, can use to skillfully facilitate client adherence to home program recommendations to enhance one\u27s function and occupations in life. This themed booklet includes evidence-based strategies and intervention resources to help efficiently guide rehabilitation professionals in holistically promoting HP adherence. It includes the development, collection, and organization of a multitude of relevant tackle tools
Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine.
BACKGROUND: The last decade has seen a dramatic increase in the availability of scientific data, where human-related biological databases have grown not only in count but also in volume, posing unprecedented challenges in data storage, processing, analysis, exchange, and curation. Next generation sequencing (NGS) advancements have facilitated and accelerated the process of identifying genetic variations. Adopting NGS with Whole-Genome and RNA sequencing in a diagnostic context has the potential to improve disease-risk detection in support of precision medicine and drug discovery. Several bioinformatics pipelines have been developed to strengthen variant interpretation by efficiently processing and analyzing sequence data, whereas many published results show how genomics data can be proactively incorporated into medical practices and improve utilization of clinical information. To utilize the wealth of genomics and health, there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern technology.
RESULTS: Our focus here is to create a comprehensive database with mobile access to actionable genes and classified diseases, considered the foundation for clinical genomics and precision medicine. We present a publicly available iOS app, PAS-Gen, which invites global users to freely download it on iPhone and iPad devices, quickly adopt its easy to use interface, and search for genes and related diseases. PAS-Gen was developed using Swift, XCODE, and PHP scripting that uses Web and MySQL database servers, which includes over 59,000 protein-coding and non-coding genes, and over 90,000 classified gene-disease associations. PAS-Gen is founded on the clinical and scientific premise that easier healthcare and genomics data sharing will accelerate future medical discoveries.
CONCLUSIONS: We present a cutting-edge gene-disease database with a smart phone application, integrating information on classified diseases and related genes. The PAS-Gen app will assist researchers, medical practitioners, and pharmacists by providing a broad and view of genes that may be implicated in the likelihood of developing certain diseases. This tool with accelerate users\u27 abilities to understand the genetic basis of human complex diseases and by assimilating genomic and phenotypic data will support future work to identify gene-specific designer drugs, target precise molecular fingerprints for tumors, suggest appropriate drug therapies, predict individual susceptibility to disease, and diagnose and treat rare illnesses
Neuromechanical Biomarkers for Robotic Neurorehabilitation
: One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the "biomarkers" that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the "Rehabilomics" has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective
Health State Estimation
Life's most valuable asset is health. Continuously understanding the state of
our health and modeling how it evolves is essential if we wish to improve it.
Given the opportunity that people live with more data about their life today
than any other time in history, the challenge rests in interweaving this data
with the growing body of knowledge to compute and model the health state of an
individual continually. This dissertation presents an approach to build a
personal model and dynamically estimate the health state of an individual by
fusing multi-modal data and domain knowledge. The system is stitched together
from four essential abstraction elements: 1. the events in our life, 2. the
layers of our biological systems (from molecular to an organism), 3. the
functional utilities that arise from biological underpinnings, and 4. how we
interact with these utilities in the reality of daily life. Connecting these
four elements via graph network blocks forms the backbone by which we
instantiate a digital twin of an individual. Edges and nodes in this graph
structure are then regularly updated with learning techniques as data is
continuously digested. Experiments demonstrate the use of dense and
heterogeneous real-world data from a variety of personal and environmental
sensors to monitor individual cardiovascular health state. State estimation and
individual modeling is the fundamental basis to depart from disease-oriented
approaches to a total health continuum paradigm. Precision in predicting health
requires understanding state trajectory. By encasing this estimation within a
navigational approach, a systematic guidance framework can plan actions to
transition a current state towards a desired one. This work concludes by
presenting this framework of combining the health state and personal graph
model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin
Effective behavior change techniques in digital health interventions targeting non-communicable diseases: an umbrella review
Background: Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of common non-communicable diseases (NCDs), it is unclear what specific components make an intervention effective in changing human behavior.Purpose: The aim of this umbrella review was to identify the most effective behavior change techniques (BCTs) in DHIs that address the most common NCDs. Methods: Five electronic databases were searched for articles published in English between 1st January 2007 and 24th January 2021. Studies were included if they were systematic reviews or meta-analyses of e- or mHealth interventions targeting the modification of one or more NCD-related risk factors in adults. Study quality was assessed using AMSTAR 2. Sixty-one articles, spanning 10 health domains and comprising over half a million individual participants, were included in the review. Results: DHIs are favorably associated with improved health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest education, communication with a professional, tailored reminders, goals and planning, feedback and monitoring, and personalization components increase the effectiveness of DHIs targeting NCDs and lifestyle behaviors. Conclusions: Common BCTs across health domains, such as ‘goals and planning’, increase DHI effectiveness and should be prioritized for inclusion within future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines
Sustainable technologies for older adults
: The exponential evolution of technology and the growth of the elderly population are two
phenomena that will inevitably interact with increasing frequency in the future. This paper analyses
scientific literature as a means of furthering progress in sustainable technology for senior living.
We carried out a bibliometric analysis of papers published in this area and compiled by the Web of
Science (WOS) and Scopus, examining the main participants and advances in the field from 2000 to
the first quarter of 2021. The study describes some interesting research projects addressing three
different aspects of older adults’ daily lives—health, daily activities and wellbeing—and policies
to promote healthy aging and improve the sustainability of the healthcare system. It also looks at
lines of research into transversal characteristics of technology. Our analysis showed that publications
mentioning sustainability technologies for older adults have been growing progressively since the
2000s, but that the big increase in the number of research works in this area took place during the
period 2016–2021. These more recent works show a tendency to study those factors that improve
healthy aging, ensure the social inclusion of the elderly through technology and prolong the time in
which they can live independent lives thanks to smart environments. Current research gaps in the
literature are also discussed.: This work was funded by the Spanish Ministry of Economy, Industry and Competitiveness, (CSO2017-86747-R) and supported in part by the FEDER/Ministerio de Ciencia, Innovación
y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learn Projects under Grants TIN2017-85179-C3-1-R and PID2020-112584RB-C31, and in part by the Madrid Regional
Government through the e-Madrid-CM Project under Grant S2018/TCS-4307
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