Summary
This interdisciplinary research project contributes to understanding and implementing familiarity in location-based systems through theoretical, methodological and applied advancements. The proposed research adds significantly to our knowledge of how to conceptualize familiarity with different environmental features (e.g., landmarks, routes, regions) and provides novel ways to assess it in-situ based on behavioural data. It provides important theoretical insights by disentangling and interrelating the different conceptualizations and measurements of familiarity. The project’s key methodological advancement is the unique combination of three different sensors to behaviourally assess familiarity during in-situ travel and spatial learning: Mobile eye tracking, high precision GNSS positioning, and head/body-worn Inertial Measurement Unit (e.g. providing acceleration data etc.) sensors are fused to study familiarity behaviourally in-situ. Machine learning and deep learning experiments on the behavioural data, singly and in combination, will be used to classify different levels of familiarity reflected in participants’ activities. The outcomes of this investigation form a basis for future research studies on how smart cities can adapt to its citizens' needs, based on their current state of spatial cognition – for which familiarity is a key example. These results contribute to the European Commission's policy “Smart Cities - Smart Living” and interplay with future programmes of the Horizon Europe cluster “Climate, Energy and Mobility” and the mission area “Climate-neutral and smart cities”.
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| Web resources: | https://cordis.europa.eu/project/id/101026774 |
| Start date: | 01-03-2022 |
| End date: | 31-08-2024 |
| Total budget - Public funding: | 226 032,96 Euro - 226 032,00 Euro |
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Original description
This interdisciplinary research project contributes to understanding and implementing familiarity in location-based systems through theoretical, methodological and applied advancements. The proposed research adds significantly to our knowledge of how to conceptualize familiarity with different environmental features (e.g., landmarks, routes, regions) and provides novel ways to assess it in-situ based on behavioural data. It provides important theoretical insights by disentangling and interrelating the different conceptualizations and measurements of familiarity. The project’s key methodological advancement is the unique combination of three different sensors to behaviourally assess familiarity during in-situ travel and spatial learning: Mobile eye tracking, high precision GNSS positioning, and head/body-worn Inertial Measurement Unit (e.g. providing acceleration data etc.) sensors are fused to study familiarity behaviourally in-situ. Machine learning and deep learning experiments on the behavioural data, singly and in combination, will be used to classify different levels of familiarity reflected in participants’ activities. The outcomes of this investigation form a basis for future research studies on how smart cities can adapt to its citizens' needs, based on their current state of spatial cognition – for which familiarity is a key example. These results contribute to the European Commission's policy “Smart Cities - Smart Living” and interplay with future programmes of the Horizon Europe cluster “Climate, Energy and Mobility” and the mission area “Climate-neutral and smart cities”.Status
SIGNEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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