Cargando…
HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications
The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support differ...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9514186/ https://www.ncbi.nlm.nih.gov/pubmed/36185751 http://dx.doi.org/10.1007/s10515-022-00363-9 |
_version_ | 1784798223410397184 |
---|---|
author | Mehrabi, Maryam Zamani, Bahman Hamou-Lhadj, Abdelwahab |
author_facet | Mehrabi, Maryam Zamani, Bahman Hamou-Lhadj, Abdelwahab |
author_sort | Mehrabi, Maryam |
collection | PubMed |
description | The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support different communication technologies. To address these problems, in this paper, we first analyze the domain of health monitoring mobile applications and then propose a framework based on model-driven engineering that accelerates the development of such systems. The proposed framework, called HealMA, includes a domain-specific modeling language, a graphical modeling editor, several validation rules, and a set of model-to-code transformations, all packed as an Eclipse plugin. We evaluated the framework to assess its applicability in generating various mobile health applications, as well as its impact on software productivity. To this end, four different health monitoring applications have been automatically generated. Then, we evaluated the productivity of software developers by comparing the time and effort it takes to use HealMA compared to a code-centric process. As part of the evaluation, we also evaluated the usability of HealMA-generated apps by conducting a user study. The results show that HealMA is both applicable and beneficial for automatic generation of usable IoT-based Android health monitoring apps. |
format | Online Article Text |
id | pubmed-9514186 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-95141862022-09-28 HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications Mehrabi, Maryam Zamani, Bahman Hamou-Lhadj, Abdelwahab Autom Softw Eng Article The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support different communication technologies. To address these problems, in this paper, we first analyze the domain of health monitoring mobile applications and then propose a framework based on model-driven engineering that accelerates the development of such systems. The proposed framework, called HealMA, includes a domain-specific modeling language, a graphical modeling editor, several validation rules, and a set of model-to-code transformations, all packed as an Eclipse plugin. We evaluated the framework to assess its applicability in generating various mobile health applications, as well as its impact on software productivity. To this end, four different health monitoring applications have been automatically generated. Then, we evaluated the productivity of software developers by comparing the time and effort it takes to use HealMA compared to a code-centric process. As part of the evaluation, we also evaluated the usability of HealMA-generated apps by conducting a user study. The results show that HealMA is both applicable and beneficial for automatic generation of usable IoT-based Android health monitoring apps. Springer US 2022-09-27 2022 /pmc/articles/PMC9514186/ /pubmed/36185751 http://dx.doi.org/10.1007/s10515-022-00363-9 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Mehrabi, Maryam Zamani, Bahman Hamou-Lhadj, Abdelwahab HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications |
title | HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications |
title_full | HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications |
title_fullStr | HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications |
title_full_unstemmed | HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications |
title_short | HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications |
title_sort | healma: a model-driven framework for automatic generation of iot-based android health monitoring applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9514186/ https://www.ncbi.nlm.nih.gov/pubmed/36185751 http://dx.doi.org/10.1007/s10515-022-00363-9 |
work_keys_str_mv | AT mehrabimaryam healmaamodeldrivenframeworkforautomaticgenerationofiotbasedandroidhealthmonitoringapplications AT zamanibahman healmaamodeldrivenframeworkforautomaticgenerationofiotbasedandroidhealthmonitoringapplications AT hamoulhadjabdelwahab healmaamodeldrivenframeworkforautomaticgenerationofiotbasedandroidhealthmonitoringapplications |