APA
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Ameko, M. K., Cai, L., Boukhechba, M., Daros, A., Chow, P. I., Teachman, B. A., … Barnes, L. E. (2018). Cluster-based approach to improve affect recognition from passively sensed data. 2018 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI), 434–437. https://doi.org/10.1109/BHI.2018.8333461
Chicago/Turabian
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Ameko, M. K., L. Cai, M. Boukhechba, A. Daros, P. I. Chow, B. A. Teachman, M. Gerber, and L. E. Barnes. “Cluster-Based Approach to Improve Affect Recognition from Passively Sensed Data.” 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (2018): 434–437.
MLA
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Ameko, M. K., et al. “Cluster-Based Approach to Improve Affect Recognition from Passively Sensed Data.” 2018 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI), 2018, pp. 434–37, doi:10.1109/BHI.2018.8333461.
BibTeX Click to copy
@article{m2018a,
title = {Cluster-based approach to improve affect recognition from passively sensed data},
year = {2018},
journal = {2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)},
pages = {434-437},
doi = {10.1109/BHI.2018.8333461},
author = {Ameko, M. K. and Cai, L. and Boukhechba, M. and Daros, A. and Chow, P. I. and Teachman, B. A. and Gerber, M. and Barnes, L. E.}
}
Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.
