A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Sensor Fusion and The City: Visualisation and Aggregation of Environmental & Wellbeing Data
The proliferation of miniaturized electronics has fuelled a shift toward environmental sensing technologies ranging from pollution to weather monitoring at higher granularity. However, little consideration has been given around the relationship between environmental stressors (e.g. air pollution) and mental wellbeing. In this paper, we aim at capturing fluctuations in momentary wellbeing and behaviour in response to changes in the ambient environment. This is achieved using a multisensor fusion approach that simultaneously collected urban environmental factors (e.g. including PM10, PM2.5, PM1.0, Noise, Reducing gases, NH3), body reactions (physiological reactions including EDA, HR and HRV) and users perceived responses (e.g. self-reported geo-tagged valence). Our approach leverages an exploratory data visualisation along with geometrical and spatial data analysis algorithms, allowing spatial and temporal comparisons of data clusters in relation to people's wellbeing. The effectiveness of our approach is demonstrated through a positive correlation between environmental factors and physiology reactions. By implementing spatial visualisation with our real-world data shows the potential opportunities to understand how the environment can effect mental wellbeing.
Sensor Fusion and The City: Visualisation and Aggregation of Environmental & Wellbeing Data
The proliferation of miniaturized electronics has fuelled a shift toward environmental sensing technologies ranging from pollution to weather monitoring at higher granularity. However, little consideration has been given around the relationship between environmental stressors (e.g. air pollution) and mental wellbeing. In this paper, we aim at capturing fluctuations in momentary wellbeing and behaviour in response to changes in the ambient environment. This is achieved using a multisensor fusion approach that simultaneously collected urban environmental factors (e.g. including PM10, PM2.5, PM1.0, Noise, Reducing gases, NH3), body reactions (physiological reactions including EDA, HR and HRV) and users perceived responses (e.g. self-reported geo-tagged valence). Our approach leverages an exploratory data visualisation along with geometrical and spatial data analysis algorithms, allowing spatial and temporal comparisons of data clusters in relation to people's wellbeing. The effectiveness of our approach is demonstrated through a positive correlation between environmental factors and physiology reactions. By implementing spatial visualisation with our real-world data shows the potential opportunities to understand how the environment can effect mental wellbeing.
Sensor Fusion and The City: Visualisation and Aggregation of Environmental & Wellbeing Data
Johnson, Thomas (author) / Kanjo, Eiman (author)
2021-09-07
5830205 byte
Conference paper
Electronic Resource
English
Smart City Data in Urban Wellbeing Estimation
| IEEE | 2022
3D-visualisation of NDT-data fusion technique
| Tema Archive | 2003
Visualisation: architecture and the city
| British Library Conference Proceedings | 1999
2D- and 3D-visualisation of NDT-data using data fusion technique
| Springer Verlag | 2005
2D- and 3D-visualisation of NDT-data using data fusion technique
| Online Contents | 2005