Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign

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Authors

NOVAK Rok PETRIDIS Ioannis KOCMAN David ROBINSON Johanna Amalia KANDUC Tjasa CHAPIZANIS Dimitris KARAKITSIOS Spyros FLUCKIGER Benjamin VIENNEAU Danielle MIKEŠ Ondřej DEGRENDELE Céline SÁŇKA Ondřej DOS SANTOS-ALVES Saul Garcia MAGGOS Thomas PARDALI Demetra STAMATELOPOULOU Asimina SARAGA Dikaia PERSICO Marco Giovanni VISAVE Jaideep GOTTI Alberto SARIGIANNIS Dimosthenis

Year of publication 2021
Type Article in Periodical
Magazine / Source International Journal of Environmental Research and Public Health
MU Faculty or unit

Faculty of Science

Citation
Web https://www.mdpi.com/1660-4601/18/21/11614
Doi http://dx.doi.org/10.3390/ijerph182111614
Keywords data fusion; multi-sensor; data visualization; data treatment; participant reports; air quality; exposure assessment
Attached files
Description Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended.
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