Imagine a world where your smartphone could determine your mood based on the way you text or the tone of your voice. Imagine a car that adjusts its music playlist during rush hour to match your stress level. These scenarios are not just futuristic fantasies. A brief look at the rapidly evolving field of emotional computing.

Performance computing is a multidisciplinary field that combines computer science, engineering, psychology, neuroscience, and other related disciplines. Recently in the journal Intelligent Computing A new comprehensive review of efficient computing has been published. Explains recent developments, challenges and future trends.

Scope and application of efficient computing

Efficient computing enables machines to detect, recognize, understand and respond to human emotions. It has various applications in various sectors such as education, healthcare, business services, and the integration of science and art. Emotional intelligence plays an important role in human-machine interaction, and emotional computing can significantly improve this interaction.

According to the review, research in this field covers five main areas: basic emotion theory, emotion signal collection, emotion analysis, multimodal fusion, and emotion generation and expression.

Research and growth methods

To improve the general understanding of the theory, technical methods, and applications of emotional computing, the researchers conducted a statistical analysis using a bibliometric method. Bibliometrics applies quantitative methods such as mathematics and statistics to scientific or other literature and processes statistical data based on computer science theory.

The keywords assigned to the articles by the authors in the field of emotional computing were analyzed in terms of frequency and similarity, and the main keywords among them were grouped into five clusters. Image credit: Guanxiong Pei et al.

According to data collected in a bibliometric study, the number of papers published in the field of efficient computing has increased significantly since 1997, with a steady increase in publications until 2009, followed by a rapid increase from 2010 to 2019 due to advances in deep learning. . However, growth has slowed since 2019, likely due to a slowdown in deep learning innovations and the impact of the COVID-19 pandemic on academia. China is the world leader in terms of publications, followed by the USA, India, the UK and Germany.

Research in this field is published in many journals, with influential IT academics choosing high-profile journals such as: IEEE Transactions on Affective Computing, Expertise systems with applications And Knowledge Based Systems.

Interdisciplinary research and innovation

Effective computational research spans disciplines such as computer science, engineering, neuroscience, and other disciplines. The main research topics in this field, determined by keyword frequency analysis, are natural language processing, facial expression recognition, human-computer interaction, affective disorder analysis and multimodal emotion analysis.

Top institutions in this field include Nanyang Technological University, Imperial College London and Tsinghua University. International cooperation is quite common, especially between China and the United States. Scientists in this field are mostly concentrated in Asia and North America.

Ethical considerations and future trends

Performance computing, a field that combines technology with a nuanced understanding of human emotions, is experiencing an explosion of innovation and associated ethical considerations. Innovations identified in the review include emotion generation methods that increase the naturalness of human-computer interaction by increasing the realism of facial expressions and body movements of avatars and robots.

The development of detailed emotion classification models is also advancing, allowing for a better understanding of the complex emotions that arise in daily life. Cognitive neuroscience provides valuable insight into the neural basis of emotion, which is critical for the development of systems that can reliably mimic emotional responses.

Meanwhile, the industry is reportedly expanding its focus from individual emotions to the group level, recognizing the importance of collective influences on conditions such as consumer behavior and the workplace. Additionally, researchers are addressing the issue of cultural diversity by developing models that can adapt to different emotional displays in different cultures. Such work includes taking into account ethical and privacy concerns and supporting the establishment of international standards regulating the use of emotional data.

Researchers have identified several current trends that are likely to continue. For example, the creation of large-scale multimodal datasets is essential for training powerful emotional computing systems, and there is a growing trend towards collecting and analyzing data that reflects natural emotional expressions. Multimodal synthesis technology is being developed to effectively integrate information from different sources such as facial expressions, voice, and text to improve the accuracy of emotion recognition. In addition, the industry is moving towards a knowledge-driven approach that integrates expert knowledge to compensate for data limitations and improve computational efficiency.

Applications and future potential

Emotional calculation is applied to political speeches, music, drama, and visual arts to predict emotional responses and enhance emotional expression. This interdisciplinary approach not only expands the possibilities of efficient computing but also opens new avenues for research and application.

Future applications of emotional computing in various fields hold great promise, particularly due to advances in emotional brain-computer interfaces, human-computer empathic dialogue, emotion-based decision making, and emotional virtual reality. Effective brain-computer interfaces that use neurological signals are effective in measuring and regulating emotional states, facilitating clinical diagnosis, therapy, and military applications. Empathetic human-computer dialogue aims to create a more human-like interaction through the use of multimodal emotional signals, offering the potential for communication with the elderly and intelligent customer service.

Emotion-based decision making incorporates emotional variables into machine decision-making processes, potentially optimizing outcomes in areas such as production safety and assisted driving. Effective virtual reality plays a critical role in creating immersive metauniverses by using emotionally rich avatars to engage users, thereby facilitating applications in virtual socialization, interaction, and marketing.