# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 5 Discussion
### Future Directions  
Over the past decade, computer-vision-based techniques for human activity recognition have made significant progress and led to mature applications [65]. PSO algorithms have been proposed as effective due to their convergence and ease of implementation, but specifying certain parameters is necessary to maintain the solutions' quality, particularly in enabling ERS [65]. Previous research has suggested potential PSO applications in healthcare, industry, commerce, and smart cities. Still, future research is needed to explore trend applications, such as image registration with 3D images from computer-assisted tomography (CAT) and MRI, as most previous research used 2D images [3; 47]. Additionally, PSO can be utilised to develop optimal recommendations based on algorithms, but it ischallenging to apply this approach to real-life scenarios that lack explicit feedback data, particularly when dealing with human emotions, which are not easily interpreted. Previous researchers have suggested for future researchers explore the enhancement of PSO through Neuro-Fuzzy Network (NFN) for Colour Image Processing (CIP) as this is seen as having strong potential for various commercial, healthcare, and environmental applications [3].  
PSO is also reported as a robust and fast optimiser that can solve complex real-world problems; thus, for better outcomes, it is recommended for more research works be completed on adapting PSO for ERS [7]. In future research related to PSO for ERS, there are several potential areas of exploration. First, diverse search strategies, such as the firefly algorithm and cuckoo search, can be investigated to enhance the diversity of the swarm for subdimensions exploration, as suggested by [46]. Second, multi-objective evolutionary algorithms can be explored to equip the current algorithm to handle complex optimisation problems containing multiple criteria. Moreover, research towards BCI technology should focus on designing dependable systems with stable performance, adaptable to a wide range of users with varying mental states and environmental conditions, as highlighted by [49]. To create viable ERS products that have general appeal, intuitiveness, usability, reliability, and cost-effectiveness, the standard requirements for enabling PSO on ERS should also be considered in future works [3]. A study by [66] proposed the potential for investigating real-time movie-induced emotions, specifically to classify between happy and unhappy emotions. Previous studies also highlighted the limitations of PSO due to the complexity and memory consumption needed to stabilise the outputs [67].  
Finally, the paper from [68] suggests that the quality of solutions generated by PSO is dependent on its parameters, indicating that the PSO parameter settings may not be optimal for every problem. Furthermore, PSO on the modalities of ERS may be susceptible to local optima when dealing with complex objective functions. Although many papers focus on the fundamental design of PSO systems for ERS optimisation, follow-up research is needed to establish the systems' applicability in real-world settings. In addition, the literature retrieved for this study is based on Lens.org, although there were more recent papers that need to be explored from PSO and ERS. This study focused on ERS in modalities and PSO as optimisation for the ERS modalities; however, a variety of PSO methods can achieve more PSO applications and enable ERS results better. This will enable ERS researchers and practitioners to create more effective and practical applications of ERS.