Empathy is a social skill that 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. Empathy is a context-dependent term; thus, detecting or recognising empathy has potential applications in society, healthcare and education. Despite being a broad and overlapping topic, the avenue of empathy detection studies leveraging Machine Learning remains underexplored from a holistic literature perspective. To this end, we systematically collect and screen 801 papers from 10 well-known databases and analyse the selected 54 papers. We group the papers based on input modalities of empathy detection systems, i.e., text, audiovisual, audio and physiological signals. We examine modality-specific pre-processing and network architecture design protocols, popular dataset descriptions and availability details, and evaluation protocols. We further discuss the potential applications, deployment challenges and research gaps in the Affective Computing-based empathy domain, which can facilitate new avenues of exploration. We believe that our work is a stepping stone to developing a privacy-preserving and unbiased empathic system inclusive of culture, diversity and multilingualism that can be deployed in practice to enhance the overall well-being of human life.

翻译：共情是一种社会技能，表明个体理解他人的能力。近年来，共情引起了包括但不限于情感计算、认知科学和心理学等多个学科的关注。共情是一个依赖于上下文的术语；因此，检测或识别共情在社会、医疗保健和教育领域具有潜在应用。尽管是一个广泛且交叉的课题，但从整体文献视角来看，利用机器学习进行共情检测的研究途径仍未被充分探索。为此，我们系统地从10个知名数据库中收集和筛选了801篇论文，并对选定的54篇论文进行了分析。我们根据共情检测系统的输入模态（即文本、视听、音频和生理信号）对论文进行分组。我们研究了特定模态的预处理和网络架构设计协议、常用数据集描述及可用性细节，以及评估协议。我们进一步讨论了基于情感计算的共情领域的潜在应用、部署挑战和研究空白，这可以为新的探索途径提供便利。我们相信，我们的工作是开发一个保护隐私、无偏见、包容文化、多样性和多语言性的共情系统的基石，该系统可实际部署以提升人类生活的整体福祉。