Particle Swarm Optimisation (PSO) is a popular technique in the field of Swarm Intelligence (SI) that focuses on optimisation. Researchers have explored multiple algorithms and applications of PSO, including exciting new technologies, such as Emotion Recognition Systems (ERS), which enable computers or machines to understand human emotions. This paper aims to review previous studies related to PSO findings for ERS and identify modalities that can be used to achieve better results through PSO. To achieve a comprehensive understanding of previous studies, this paper will adopt a Systematic Literature Review (SLR) process to filter related studies and examine papers that contribute to the field of PSO in ERS. The paper’s primary objective is to provide better insights into previous studies on PSO algorithms and techniques, which can help future researchers develop more accurate and sustainable ERS technologies. By analysing previous studies over the past decade, the paper aims to identify gaps and limitations in the current research and suggest potential areas for future research. Overall, this paper’s contribution is twofold: first, it provides an overview of the use of PSO in ERS and its potential applications. Second, it offers insights into the contributions and limitations of previous studies and suggests avenues for future research. This can lead to the development of more effective and sustainable ERS technologies, with potential applications in a wide range of fields, including healthcare, gaming, and customer service.

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.

An emotion recognition system or ERS is an emerging technology due to its potential use in various platforms, applications and sectors. ERS is a key technology arising from the fourth industrial revolution (IR 4.0) and arguably it is becoming a key technology in enabling the fifth industrial revolution (IR 5.0). This is so because IR 5.0 main feature is personalization. The body of knowledge on ERS consists largely of works on exploring the modalities or modes to recognise or determine emotion; the development of the system itself; and the creation of applications utilising the emotion recognition functions. However, to enable the researchers and innovators to develop better and more impactful ERS, they need to understand the factors shaping usage and determining adoption amongst the intended users. This particular aspect relating to ERS is not yet fully explored and understood. Plus, there is yet to be a definitive framework or model on determinants for ERS adoption. This research intends to address this important need. Specifically, this study will look into the key adopters that will be using ERS, and propose a conceptual framework for ERS adoption. The theories underpinning the proposed conceptual framework are the Theory of Planned Behaviour (TPB), Unified Theory of Acceptance and Use of Technology (UTAUT) and Diffusion of Innovations (DOI). It is envisioned that this research will; confirm the validity of the proposed framework and generate new findings that will benefit ERS practitioners as well as adopters.