eHealth Context Inference - A Review of Open Source Frameworks Initiatives

Arsénio Reis, Dennis Paulino, Paulo Martins, Hugo Paredes, João Barroso

Abstract

The collection of health and fitness longitudinal data can be used to model disease progression and shape new algorithms to diagnose and predict health hazards. Continuously tracking vital signs, in particular heart rate and skin temperature, can be very informative by using models and algorithms to predict and notify the user about when he might be falling ill. With the current wearable devices and the proper algorithms, the individual can be permanently monitored, which might be much more interesting than a one-off reading comparison with the population average, made by a doctor. It would be possible to intervene earlier and to prevent somebody from becoming seriously ill. From a broader perspective, the knowledge about a user’s health can be considered as an element of that user’s context and be used by context aware applications to provide higher value to the user. After the trivialization of the data acquisition sensors, wearable devices, and raw data, the next logical step is the development of contained software components that can infer and produce knowledge from the raw data. These components and the knowledge they produce can be used by all sorts of applications in order to further customize their usage by a specific user. Customization and context awareness, in regard to health, is a wide field for research and there are a multitude of proposals for models and algorithms. In this review work we searched for software components (frameworks, software libraries, etc.), freely available and that can be used as building blocks for other computer systems by software developers.

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Paper Citation


in Harvard Style

Reis A., Paulino D., Martins P., Paredes H. and Barroso J. (2018). eHealth Context Inference - A Review of Open Source Frameworks Initiatives.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health, ISBN 978-989-758-281-3, pages 707-714. DOI: 10.5220/0006752707070714


in Bibtex Style

@conference{ai4health18,
author={Arsénio Reis and Dennis Paulino and Paulo Martins and Hugo Paredes and João Barroso},
title={eHealth Context Inference - A Review of Open Source Frameworks Initiatives},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health,},
year={2018},
pages={707-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006752707070714},
isbn={978-989-758-281-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health,
TI - eHealth Context Inference - A Review of Open Source Frameworks Initiatives
SN - 978-989-758-281-3
AU - Reis A.
AU - Paulino D.
AU - Martins P.
AU - Paredes H.
AU - Barroso J.
PY - 2018
SP - 707
EP - 714
DO - 10.5220/0006752707070714