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 829 papers from 10 well-known databases, systematically screened them and analysed the final 62 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. This paper, therefore, provides a structured overview of recent advancements and remaining challenges towards developing a robust empathy detection system that could meaningfully contribute to enhancing human well-being.

翻译：共情反映了个体理解他人的能力。过去几年中，共情研究已引起包括但不限于情感计算、认知科学与心理学等多个学科的关注。共情检测在社会、医疗及教育领域具有潜在应用价值。尽管这是一个广泛且存在交叉的研究主题，但从系统文献综述的视角来看，利用机器学习进行共情检测的研究路径仍未得到充分探索。我们从10个知名数据库中收集了829篇文献，经系统筛选后对最终62篇论文进行了分析。我们的分析揭示了若干突出的任务构建形式——包括针对局部话语或整体表达的共情、单向或平行共情以及情绪感染——这些形式涵盖单主体、双主体及群体交互场景。本文根据四种输入模态——文本、视听、音频与生理信号——对共情检测方法进行了归纳，从而呈现了针对特定模态的网络架构设计范式。我们讨论了基于情感计算的共情领域所面临的挑战、研究空白及潜在应用方向，这些讨论可为新的探索路径提供参考。我们进一步整理了公开可用的数据集与代码资源。因此，本文通过系统梳理近期进展与现存挑战，为开发能够切实促进人类福祉的鲁棒性共情检测系统提供了结构化综述。