  Unlike the six basic emotions of happiness, sadness, fear, anger, disgust and
surprise, modelling and predicting dimensional affect in terms of valence
(positivity - negativity) and arousal (intensity) has proven to be more
flexible, applicable and useful for naturalistic and real-world settings. In
this paper, we aim to infer user facial affect when the user is engaged in
multiple work-like tasks under varying difficulty levels (baseline, easy, hard
and stressful conditions), including (i) an office-like setting where they
undertake a task that is less physically demanding but requires greater mental
strain; (ii) an assembly-line-like setting that requires the usage of fine
motor skills; and (iii) an office-like setting representing teleworking and
teleconferencing. In line with this aim, we first design a study with different
conditions and gather multimodal data from 12 subjects. We then perform several
experiments with various machine learning models and find that: (i) the display
and prediction of facial affect vary from non-working to working settings; (ii)
prediction capability can be boosted by using datasets captured in a work-like
context; and (iii) segment-level (spectral representation) information is
crucial in improving the facial affect prediction.
