CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms

Nurgazy, Meruyert; Zaslavsky, Arkady; Jayaraman, Prem Prakash; Kubler, Sylvain; Mitra, Karan; Saguna, Saguna

doi:10.1109/hpcs48598.2019.9188167

“…Nurgazy et al [39] introduced a novel approach to visualize a personalized air pollution map based on the user context. Concentrations of Nitrogen dioxide (NO 2 ), ozone (O 3 ), and particulate matter (PM 2.5 ) were displayed, taking into account the user's location, time, pollutant sensitivity levels, and any color vision impairments they may have.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

Alharbi,

Cherif,

Nadeem

2023

Smart Cities

3

0

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Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

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“…Nurgazy et al [37] introduced a novel approach to visualize a personalized air pollution map based on the user context. The concentrations of Nitrogen dioxide (NO 2 ), ozone (O 3 ), and particulate matter (PM 2 .5) were presented based on the user's location, time, pollutant sensitivity levels, and color vision impairments.…”

Section: Related Workmentioning

confidence: 99%

Smart eHealth System for Asthma Attack Prevention and Environmental Factors Monitoring

Alharbi,

Cherif,

Nadeem

2023

Preprint

0

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Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

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“…A variety of attempts have been made in this field, many without relying on 3d visualizations as well. Nurgazy et al [2019] in their paper, present a context-aware system CAVisAP for outdoor air pollution visualization. The system provides context-aware visualization taking into account location and time.…”

Section: Related Workmentioning

confidence: 99%

AiR -- An Augmented Reality Application for Visualizing Air Pollution

Mathews,

Chimalakonda,

Jain

2020

Preprint

0

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Air quality is a term used to describe the concentration levels of various pollutants in the air we breathe. The air quality, which is degrading rapidly across the globe, has been a source of great concern. Across the globe, governments are taking various measures to reduce air pollution. Bringing awareness about environmental pollution among the public plays a major role in controlling air pollution, as the programs proposed by governments require the support of the public. Though information on air quality is present on multiple portals such as the Central Pollution Control Board (CPCB ), which provides Air Quality Index, that could be accessed by the public. However, such portals are scarcely visited by the general public. Visualizing air quality in the location where an individual resides could help in bringing awareness among the public. This visualization could be rendered using Augmented Reality techniques. Considering the widespread usage of Android based mobile devices in India, and the importance of air quality visualization, we present AiR, as an Android based mobile application. AiR considers the air quality measured by CPCB, in a locality that is detected by the users' GPS or in a locality of users' choice, and visualizes various air pollutants (P M10, P M2.5, NO2, SO2, CO, O3 and NH3) present in the locality and displays them in the users' surroundings. AiR also creates awareness in an interactive manner about the different pollutants, sources, and their impacts on health .

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CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms

Cited by 11 publications

References 11 publications

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

Smart eHealth System for Asthma Attack Prevention and Environmental Factors Monitoring

AiR -- An Augmented Reality Application for Visualizing Air Pollution

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