Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries

Autor: Lakmal Meegahapola, William Droz, Peter Kun, Amalia de Götzen, Chaitanya Nutakki, Shyam Diwakar, Salvador Ruiz Correa, Donglei Song, Hao Xu, Miriam Bidoglia, George Gaskell, Altangerel Chagnaa, Amarsanaa Ganbold, Tsolmon Zundui, Carlo Caprini, Daniele Miorandi, Alethia Hume, Jose Luis Zarza, Luca Cernuzzi, Ivano Bison, Marcelo Rodas Britez, Matteo Busso, Ronald Chenu-Abente, Can Günel, Fausto Giunchiglia, Laura Schelenz, Daniel Gatica-Perez

Publikováno v: Meegahapola, L, Droz, W, Kun, P, de Götzen, A, Nutakki, C, Diwakar, S, Ruiz-Correa, S, Song, D, Xu, H, Bidoglia, M, Gaskell, G, Chagnaa, A, Ganbold, A, Zundui, T, Caprini, C, Miorandi, D, Hume, A, Zarza, J L, Cernuzzi, L, Bison, I, Rodas Britez, M, Busso, M, Chenu-Abente, R, Günel, C, Giunchiglia, F, Schelenz, L & Gatica-Perez, D 2022, ' Generalization and Personalization of Mobile Sensing-Based Mood Inference Models : An Analysis of College Students in Eight Countries ', Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 6, no. 4, 176, pp. 1-32 . https://doi.org/10.1145/3569483

Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile healt

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