# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 2 Related Works
### Emotion Recognition System  
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 human-computer. 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). ERS enables a system that accepts various modalities of data, learns, and allows the machine to recognise the emotion of human subjects. Previous researchers specified ERS as originally in the field of Affective Computing (AC), a field that was brought forward by [12]. According to [12], AC is an ability of a computer that relates to, arises from, and understands human behaviour [12]. 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]. Outcomes and expectations from the previous studies show that ERS can be an important technology in the making based on the advantages offered to individuals, society, organisations, businesses, and industry via various platforms and applications. For example, ERS in healthcare [17], ERS in driving assistance in the car [18], ERS in the classroom [19] and ERS in smartwatches as the latest addition due to the modalities of smartwatches being the same ERS modalities. 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]. One of the main highlights that benefit from ERS is in healthcare industries [17], developed the modalities of ERS and suggested their applications in healthcare. 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. Socialrobots work and interact with humans; therefore, features of emotion recognition technology might enhance the interaction between social robots and humans. From a marketing perspective from retail industries, ERS helped in advertising products. One of the main examples is text sentiment analysis. Ref. [29] suggested that text mining provides useful data in identifying emotional awareness through text. Text mining is a learning-based algorithm to describe characteristics of text, such as word expression based on human sentiments and emotions [29]. Previous research has focused extensively on the modalities and potential applications of ERS, but there has been limited study on the readiness of the technology for adoption. A recent paper by [30] examined the readiness of ERS from the perspective of university students to determine whether university students are aware of recent developments in ERS and whether national infrastructure should encourage the use of ERS.