A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions

• URL: http://arxiv.org/abs/2305.07665v1
• Date: Mon, 8 May 2023 10:42:46 GMT
• Title: A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions
• Authors: Sitara Afzal, Haseeb Ali Khan, Imran Ullah Khan, Md. Jalil Piran, Jong Weon Lee
• Abstract summary: affective computing aims to recognize human emotions, sentiments, and feelings. No research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together.
• Score: 3.8370454072401685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together. This paper discusses the significance of affective computing, as well as its ideas, conceptions, methods, and outcomes. By using approaches of ML and XR, we survey and discuss recent methodologies in affective computing. We survey the state-of-the-art approaches along with current affective data resources. Further, we discuss various applications where affective computing has a significant impact, which will aid future scholars in gaining a better understanding of its significance and practical relevance.

Related papers

• Recent Advancement of Emotion Cognition in Large Language Models [40.23093997384297]
  Emotion cognition in large language models (LLMs) is crucial for enhancing performance across various applications. We explore the current landscape of research, which primarily revolves around emotion classification, emotionally rich response generation, and Theory of Mind assessments.
  arXiv  Detail & Related papers  (2024-09-20T09:34:58Z)
• Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
  Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• Towards affective computing that works for everyone [0.1450405446885067]
  Missing diversity, equity, and inclusion elements in affective computing datasets directly affect the accuracy and fairness of emotion recognition algorithms across different groups. Our work analyzes existing affective computing datasets and highlights a disconcerting lack of diversity in current affective computing datasets regarding race, sex/gender, age, and (mental) health representation.
  arXiv  Detail & Related papers  (2023-09-19T17:31:29Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework [83.21732533130846]
  The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2. It presents the design of two classes of deep neural networks trained with these databases. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
  arXiv  Detail & Related papers  (2021-03-29T17:36:20Z)
• Language Models as Emotional Classifiers for Textual Conversations [3.04585143845864]
  We present a novel methodology for classifying emotion in a conversation. At the backbone of our proposed methodology is a pre-trained Language Model (LM) We apply our proposed methodology on the IEMOCAP and Friends data sets.
  arXiv  Detail & Related papers  (2020-08-27T20:04:30Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)
• SensAI+Expanse Emotional Valence Prediction Studies with Cognition and Memory Integration [0.0]
  This work contributes with an artificial intelligent agent able to assist on cognitive science studies. The developed artificial agent system (SensAI+Expanse) includes machine learning algorithms, empathetic algorithms, and memory. Results of the present study show evidence of significant emotional behaviour differences between some age ranges and gender combinations.
  arXiv  Detail & Related papers  (2020-01-03T18:17:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.