Bardoutsos, Andreas, Matzarapis, Giorgos, Nikoletseas, Sotiris, Spirakis, Paul G 
ORCID: 0000-0001-5396-3749 and Tzamalis, Pantelis
(2021)
A Complementary Sensing Platform for a holistic approach to Allergic Rhinitis monitoring.
In: 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2021-7-14 - 2021-7-16.
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Abstract
Allergic diseases and, in particular, allergic rhinitis are among the most common chronic diseases, inducing disturbances in daily activities. They are caused primarily by the pollens of allergenic plants and symptoms can deteriorate due to various ambient conditions which work as irritants, such as humidity. In this paper, we present the development of an eHealth/mHealth holistic platform that utilizes the technologies of Internet of Things (IoT), Mobile Crowdsensing (MCS), Social Networking Services, Natural Language Processing (NLP), and Machine Learning (ML), in order to work as a sentinel and disease prevention tool for patients with allergic rhinitis symptoms. By efficiently combining human with machine intelligence, we provide a complementary sensing method for the comprehensive and large-scale monitoring of the disease in broad regions, and in real-time. Moreover, the users of our platform are encouraged to engage in the sensing process through a personalized health monitoring system in order to keep a constant awareness of their symptoms and, thus, deliver a successful adherence to their treatment. As an important use case, we adapted our platform to the USA region, but it can be easily extended to any other area with minor modifications. The design and complete implementation of our platform has been performed and validated in close cooperation with well-recognized academic medical doctors based in Greece who specialize in the control of allergic diseases (and rhinitis in particular) and provided valuable insights and detailed requirements analysis about the functionality and usability of the platform. To the best of our knowledge, this is the first study that examines allergic rhinitis monitoring in a complementary manner and on large scale, with the utilization of hybrid data sources.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Uncontrolled Keywords: | allergic rhinitis, ehealth, mhealth, IoT, sensors, social media, mobile crowdsensing, machine learning, natural language processing |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 21 Nov 2022 14:41 |
| Last Modified: | 17 Mar 2024 13:27 |
| DOI: | 10.1109/DCOSS52077.2021.00039 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3166305 |
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