# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 8 Future Directions  
Human engagement is an actively explored research area. Yet, conception of engagement in different contexts, measurement of social/task/multi-party engagement, and realizing impact of engagement in real-world applications have huge room for exploration and improvement. The era of big data and rapid growth of sensor technologies engender bright hopes of applying human engagement estimation for positive changes in the near future. * [PERSON] and [PERSON] [1] concluded that their work conceptualized engagement, and from then on the focus should be how to measure it and how to realize the value it provides. * In future research directions, [PERSON] et al. [151] predicted a pervasive integration of HRI solutions in industries and emphasized the need for robots to develop cognitive skills to take on more complex tasks. * [PERSON] et al. [74] called for constructs of speech and voice recognition, emotion detection, and gaze control systems, that is, a multimodal interaction approach to achieving a better engagement-aware robot design. Capturing and computationally deriving engagement is not easy. Perceiving human agents' engagement in real-world environments may entail lots of logging and signal processing, sophisticated synthesis of where a person is gazing by reading the eyes, understanding who is initiating a conversation with whom via speech analysis, and being vigilant to other environmental stimuli. The same vision is shared that multi-modality sensing [12; 74; 151] will play a key role in gathering cues of human behaviour and affect by all means. As a result, the choice of sensors and their pros and cons will be crucial, for which we hope this review provides insights. Also, there is vast space for further innovations to allow data-driven approaches to exploit abundant datasets and develop enhanced paradigms for information fusion. * [PERSON]. [PERSON] et al. [7] share interesting thoughts about future research directions. One is to investigate whether engagement features learned from in situ data, such as image/video/multimodal datasets in specific settings (Table 2) still hold in the progression, i.e., robustness in real time. The paper also describes how to replace human annotation of engagement with automated labelling. This resonates with the initiatives of self-supervised and unsupervised learning in machine learning. The motivation is to exploit the large amount of unlabelled data in the public space to improve training. Essentially, both ideas revolve around generalization. Generalization is fundamental for many engagement estimation systems to adapt to diverse use cases in various applications. We agree that, just like the rapid evolution of Large Language Models (LLMs), more cross-domain data and superior ways of representation learning are the keys. Computational cost is often disregarded. As demands rise for deep models of greater accuracy and real-time processing, efficiency will become prominent. Moreover, engagement estimation is expected to become more modularized allowing easy integration into different scenarios [2]. Engagement as a Service (EaaS) will be trending, especially in real-time systems where measuring engagement is critical for safety, well-being, acquiring knowledge, and productivity. This will endow seamless embodiment of engagement enlightening a broad spectrum of applications. In vehicles, personal computers, and robots, EaaS will turn engagement into a beacon of \"wake words\", making tons of applications not only easy to use but cheaper to run. * In [PERSON] and [PERSON]'s [3] review, they predict addressing individual differences in personalized engagement will be the next challenge because \"engagement cues may take different forms depending on the age range, gender, ethnicity, education\" and even \"cultures and backgrounds\". * [PERSON] et al. (2018) remarked physiological sensors were \"still not in commercial use due to the invasive nature of biofeedback sensors\". * [PERSON] et al.