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Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond

Yuanyuan Liu, Ke Wang, Lin Wei, Jingying Chen, Yibing Zhan, Dapeng Tao, Zhe Chen

TL;DR

This paper surveys affective computing in healthcare over the past five years, offering a taxonomy of datasets and methods that splits research into behavior-based, physiology-based, and combined data paradigms. It maps key applications such as depression diagnosis, autism recognition/intervention, and pain level assessment to multimodal and transformer-era models, while critically assessing data privacy, bias, real-time deployment, and foundation-model implications. The authors highlight a trend toward multimodal fusion and large-scale architectures, catalog important datasets, and discuss practical challenges and future directions, including privacy-preserving learning, domain adaptation, and edge-enabled real-time analysis. Overall, the work provides a structured, actionable reference for researchers and clinicians aiming to advance emotion-aware healthcare solutions in real-world settings.

Abstract

Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient communication. However, there is a noticeable absence of a comprehensive summary of recent advancements in affective computing for healthcare, which could pose difficulties for researchers entering this field. To address this, our paper aims to provide an extensive literature review of related studies published in the last five years. We begin by analyzing trends, benefits, and limitations of recent datasets and affective computing methods devised for healthcare. Subsequently, we highlight several healthcare application hotspots of current technologies that could be promising for real-world deployment. Through our analysis, we identify and discuss some ongoing challenges in the field as evidenced by the literature. Concluding with a thorough review, we further offer potential future research directions and hope our findings and insights could guide related researchers to make better contributions to the evolution of affective computing in healthcare.

Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond

TL;DR

This paper surveys affective computing in healthcare over the past five years, offering a taxonomy of datasets and methods that splits research into behavior-based, physiology-based, and combined data paradigms. It maps key applications such as depression diagnosis, autism recognition/intervention, and pain level assessment to multimodal and transformer-era models, while critically assessing data privacy, bias, real-time deployment, and foundation-model implications. The authors highlight a trend toward multimodal fusion and large-scale architectures, catalog important datasets, and discuss practical challenges and future directions, including privacy-preserving learning, domain adaptation, and edge-enabled real-time analysis. Overall, the work provides a structured, actionable reference for researchers and clinicians aiming to advance emotion-aware healthcare solutions in real-world settings.

Abstract

Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient communication. However, there is a noticeable absence of a comprehensive summary of recent advancements in affective computing for healthcare, which could pose difficulties for researchers entering this field. To address this, our paper aims to provide an extensive literature review of related studies published in the last five years. We begin by analyzing trends, benefits, and limitations of recent datasets and affective computing methods devised for healthcare. Subsequently, we highlight several healthcare application hotspots of current technologies that could be promising for real-world deployment. Through our analysis, we identify and discuss some ongoing challenges in the field as evidenced by the literature. Concluding with a thorough review, we further offer potential future research directions and hope our findings and insights could guide related researchers to make better contributions to the evolution of affective computing in healthcare.
Paper Structure (36 sections, 2 figures, 2 tables)

This paper contains 36 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Datasets and Approaches
  3. Behavior Data-based Research
  4. Datasets
  5. Approaches
  6. Physiological Data-based Research
  7. Datasets
  8. Approaches
  9. Behavior-Physiological Data-based Research
  10. Datasets
  11. Approaches
  12. Discussion
  13. Tendency
  14. Limitation
  15. Applications on Healthcare
  16. ...and 21 more sections

Figures (2)

  • Figure 1: Framework of learning-based affective computing for healthcare.
  • Figure 2: The statistic distribution of publications on various healthcare applications of affective computing from 2019 to 2023 based on Keyword search on web of science. We emphasize the top three most frequently explored applications.