A Systematic Literature Review on Emotion Recognition System In Malaysia

Abstract

Artificial intelligence (AI) is an important technology that evolved from theories into tangibility with significant impacts and applications across sectors as well as borders. It is also one of the key technologies that gave rise to the fourth industrial revolution (IR 4.0). One key subcategory of AI is the automated emotion recognition system (ERS); the application of AI to recognize human emotional states. ERS is seen as an embedded technology that can be used in our daily lives and environment including the workplace. The importance of ERS will become more significant as we move towards the fifth industrial revolution (IR 5.0), where one of the key aspects identified is the enhancements in human-computer interaction (HCI). ERS has the potential to enable smart HCI, i.e. ERS can be seen as a technology to bridge us from IR 4.0 into IR 5.0. Crucial for this is good adoption or diffusion levels of ERS amongst society. Therefore, there is a need to understand the factors that affect the adoption of ERS. Specifically, this paper seeks to establish and discuss the current ERS research landscape in Malaysia by reporting findings from the systematic literature review covering works over a decade; from the year 2011 to 2022.

Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature

“…With that being said, AC led to further findings of emotion recognition, and it has been suggested by the previous researcher that AI learning can signifies for further findings of ERS as embedded technology in various applications [17]. Moreover, ERS has emerged as one of the attractive areas in the field of AC and AI with the promises of what the technology can achieve [26], such as developing robots that can interact and communicate with humans, equipped with functions for both understanding human emotions and expressing human emotions [26].…”

“…Over the last decade, researchers have identified the subpart of AI that can be enhanced further using the capabilities of machine learning. One of the main highlights that have been suggested is machines and computers identify or learn human emotions [26] to improve the interaction between human-computer and increase potential future applications for humancomputer. Furthermore, for machines and computers to learn about human emotions, it needs a system that enables the required process to learn human emotion [13] and is specified as an emotion recognition system (ERS).…”

“…In existing research on ERS, researchers adopted AI algorithms, such as convolutional neural network (CNN) and deep neural network (DNN) for emotion recognition based on various data modalities, such as facial expression, voice intonation, heart signals (ECG), brain signal (EEG), and many others. It is important to look into the relationship between ERS and its significance in society and industry, specifically in the industrial revolution [26].…”

“…Researchers used facial recognition combined with ECG to identify the six basic emotions [27]. Furthermore, ERS has been suggested to provide better health services quality assurance by facilitating decision-making throughout the COVID-19 pandemic by allowing safe monitoring and higher emotional awareness among the practitioners [26,27]. Furthermore, according to [28], social robots have risen to facilitate limitations due to COVID restrictions.…”

“…The findings presented by [3] establish a solid foundation for understanding the potential applications of PSO across various industries and highlight its significance. In recent years, there has been increased attention on ERS, with various modalities being explored, including physiological modalities (e.g., EEG and ECG) and psychological modalities (e.g., facial expression and speech recognition) [26]. Previous research has also identified potential applications of ERS in industries, such as healthcare [17], education [19], and entertainment [16,38].…”