Couples generally manage chronic diseases together and the management takes
an emotional toll on both patients and their romantic partners. Consequently,
recognizing the emotions of each partner in daily life could provide an insight
into their emotional well-being in chronic disease management. The emotions of
partners are currently inferred in the lab and daily life using self-reports
which are not practical for continuous emotion assessment or observer reports
which are manual, time-intensive, and costly. Currently, there exists no
comprehensive overview of works on emotion recognition among couples.
Furthermore, approaches for emotion recognition among couples have (1) focused
on English-speaking couples in the U.S., (2) used data collected from the lab,
and (3) performed recognition using observer ratings rather than partner's
self-reported / subjective emotions. In this body of work contained in this
thesis (8 papers - 5 published and 3 currently under review in various
journals), we fill the current literature gap on couples' emotion recognition,
develop emotion recognition systems using 161 hours of data from a total of
1,051 individuals, and make contributions towards taking couples' emotion
recognition from the lab which is the status quo, to daily life. This thesis
contributes toward building automated emotion recognition systems that would
eventually enable partners to monitor their emotions in daily life and enable
the delivery of interventions to improve their emotional well-being.Comment: PhD Thesis, 2022 - ETH Zuric
