Computer Application for the Registration and Automation of the Correction of a Functional Skills Detection Scale in Early Care

Manzanares, María Consuelo Sáiz; Arnaiz‐González, Álvar; Díez-Pastor, José F.; Arribas, Sandra Rodríguez

doi:10.21125/inted.2019.1320

Cited by 7 publications

(7 citation statements)

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“…Furthermore, the use of distance map and dendrogram techniques will allow the study of the behaviour patterns among participants and the differences between them. In summary, the use of these techniques will allow the elaboration of similar programs as well as the development of these techniques in common spaces and/or schedules, which will enable the intervention to be more precise, sustainable, and effective [1][2][3][4][5]. The eEarlyCare computer application has been shown to be effective for the observational recording of the functional abilities of users with different degrees of cognitive disability [11][12][13][14].…”

Section: Discussionmentioning

confidence: 99%

“…However, very few studies have incorporated this methodology into their assessment to facilitate the intervention of professionals in the field of therapy related to this type of user. The results of this type of research will be very useful for professionals working with children at early ages (0-6 years) and those working with older affected users in functional areas [5].…”

Section: Functionalities Of the Use Of Machine Learning Techniques In The Evaluation Of Functional Skillsmentioning

confidence: 99%

“…This facilitates the detection of coincidences in assessed groups [4]. These aspects are very important, since they will help professionals with the development of differential diagnoses and the application of personalized therapeutic intervention programs [5].…”

Section: Introductionmentioning

confidence: 98%

“…All of these factors speed up the evaluation process and make the intervention process more profitable. However, very few studies have incorporated this methodology into the evaluation processes that facilitate the intervention of professionals in the field of therapy applied to users with special needs at an early age (0-6 years) [5,12].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Functional Abilities in 0–6 Year Olds: An Analysis with the eEarlyCare Computer Application

Manzanares

Arnaiz‐González

2020

IJERPH

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The application of Industry 4.0 to the field of Health Sciences facilitates precise diagnosis and therapy determination. In particular, its effectiveness has been proven in the development of personalized therapeutic intervention programs. The objectives of this study were (1) to develop a computer application that allows the recording of the observational assessment of users aged 0–6 years old with impairment in functional areas and (2) to assess the effectiveness of computer application. We worked with a sample of 22 users with different degrees of cognitive disability at ages 0–6. The eEarlyCare computer application was developed with the aim of allowing the recording of the results of an evaluation of functional abilities and the interpretation of the results by a comparison with "normal development". In addition, the Machine Learning techniques of supervised and unsupervised learning were applied. The most relevant functional areas were predicted. Furthermore, three clusters of functional development were found. These did not always correspond to the disability degree. These data were visualized with distance map techniques. The use of computer applications together with Machine Learning techniques was shown to facilitate accurate diagnosis and therapeutic intervention. Future studies will address research in other user cohorts and expand the functionality of their application to personalized therapeutic programs.

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Section: Discussionmentioning

confidence: 99%

Section: Functionalities Of the Use Of Machine Learning Techniques In The Evaluation Of Functional Skillsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Functional Abilities in 0–6 Year Olds: An Analysis with the eEarlyCare Computer Application

Manzanares

Arnaiz‐González

2020

IJERPH

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“…The production phase aims to implement the required functionality in the product by applying an incremental and iterative development cycle following the good practices of agile methodology [21]. The stages of the Productionize phase are presented in the following Fig.…”

Section: Productionizementioning

confidence: 99%

Quality in Use of an Android-based Mobile Application for Calculation of Bone Mineral Density with the Standard ISO/IEC 25022

Sulla-Torres¹,

Gutierrez-Quintanilla²,

Pinto-Rodriguez³

et al. 2020

IJACSA

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One of the most critical bone diseases is osteoporosis, which can be evaluated through measurements of bone mineral density. Though there is a lot of commercialized portable about health in general, few are oriented towards bone health and with a lack of user-friendly interface and data management system. This paper presents a mobile application development for the calculation of the Bone Mineral density, which integrates with Google technology. The Mobile-D methodology for the development of mobile applications, due to the sequentially in the processes or stages is used. Bone mineral density (BMD) was calculated using anthropometric regression equations, and an Android-based Mobile Application with Google technology was developed. By using Firebase Authentication and Firebase Storage provided by Google technology, it allows admin to have full control over database management. In short, this mobile application allows the calculation of the BMD of the students and data storage and data uploading to cloud storage for post-processing, online data management system with user authentication. In addition, the Internacional Organization for Standardization / International Electrotechnical Commission (ISO/IEC) 25022 standard was used to evaluate the quality in use of the mobile app, resulting in 93% of quality in use, this app being able to be used by health professionals for better decision-making.

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Past, present, and future directions of learning analytics research for students with disabilities

Baek¹,

Aguilar

2022

Journal of Research on Technology in Education

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Computer Application for the Registration and Automation of the Correction of a Functional Skills Detection Scale in Early Care

Cited by 7 publications

References 0 publications

Evaluation of Functional Abilities in 0–6 Year Olds: An Analysis with the eEarlyCare Computer Application

Evaluation of Functional Abilities in 0–6 Year Olds: An Analysis with the eEarlyCare Computer Application

Quality in Use of an Android-based Mobile Application for Calculation of Bone Mineral Density with the Standard ISO/IEC 25022

Past, present, and future directions of learning analytics research for students with disabilities

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