Abstract
The ability to capture data from our surrounding environment while learning user preferences has the potential to make our everyday decisions more straightforward and informed. Based on the idea of capturing weather data, we present our current design of a weather sensor network using several Raspberry Pi’s, combined with external resources, that presents recommendations based on personalized affect data. The goal of the system is to learn user preferences in combination with providing emotive output utilizing a neural network. The system has visualization capabilities that can interface with the web, along with other features based on preferences set by the user. This paper presents an overview of the system prototype.
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Richard, J., Braman, J., Colclough, M., Bishwakarma, S. (2020). A Neural Affective Approach to an Intelligent Weather Sensor System. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2020 – Late Breaking Posters. HCII 2020. Communications in Computer and Information Science, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-60700-5_46
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DOI: https://doi.org/10.1007/978-3-030-60700-5_46
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