  Empathy indicates an individual's ability to understand others. Over the past
few years, empathy has drawn attention from various disciplines, including but
not limited to Affective Computing, Cognitive Science and Psychology. Detecting
empathy has potential applications in society, healthcare and education.
Despite being a broad and overlapping topic, the avenue of empathy detection
leveraging Machine Learning remains underexplored from a systematic literature
review perspective. We collected 828 papers from 10 well-known databases,
systematically screened them and analysed the final 61 papers. Our analyses
reveal several prominent task formulations $-$ including empathy on localised
utterances or overall expressions, unidirectional or parallel empathy, and
emotional contagion $-$ in monadic, dyadic and group interactions. Empathy
detection methods are summarised based on four input modalities $-$ text,
audiovisual, audio and physiological signals $-$ thereby presenting
modality-specific network architecture design protocols. We discuss challenges,
research gaps and potential applications in the Affective Computing-based
empathy domain, which can facilitate new avenues of exploration. We further
enlist the public availability of datasets and codes. We believe that our work
is a stepping stone to developing a robust empathy detection system that can be
deployed in practice to enhance the overall well-being of human life.
