# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
###### Abstract  
Emotion monitoring can play a vital role in investigating mental health disorders that contribute to 14% of global diseases. Currently, the mental healthcare system is struggling to cope with the increasing demand. Robot-assisted mental health monitoring tools can take the enormous strain off the system. The current study explored existing state-of-art machine learning (ML) models and signal data from different bio-sensors assessed the suitability of robotic devices for surveilling different physiological and physical traits related to human emotions and discussed their potential applicability for mental health monitoring. Among the selected 80 articles, we subdivided our findings in terms of two different emotional categories, namely--discrete and valence-arousal (VA). By examining two different types of signals (physical and physiological) from 10 different signal sources, we found that RGB images and CNN models outperformed all other data sources and models, respectively, in both categories. Out of the 27 investigated discrete imaging signals, 25 reached higher than 80% accuracy, while the highest accuracy was observed from facial imaging signals (99.90%). Besides imaging signals, brain signals showed better potentiality than other data sources in both emotional categories, with accuracies of 99.40% and 96.88%. For both discrete and valence-arousal categories, neural network-based models illustrated superior performances. The majority of the neural network models achieved accuracies of over 80%, ranging from 80.14% to 99.90% in discrete, 83.79% to 96.88% in arousal, and 83.79% to 99.40% in valence. We also found that the performances of fusion signals (a combination of two or more signals) surpassed that of the individual ones in most cases, showing the importance of combining different signals for future model development. Overall, the potential implications of the survey are discussed, considering both human computing and mental health monitoring. The current study will definitely serve as the base for research in the field of human emotion recognition, with a particular focus on developing different robotic tools for mental health monitoring. emotion monitoring; mental health; machine learning; human-robot interaction; signals; robots 2022020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202202020202022  
loneliness is not limited to adolescents either. Many elderly people are getting less familial support and ending up living alone. Hence, our society is on the verge of brimming with loneliness, and mental health is sure to deteriorate if nothing is done to remedy the situation. Socially assistive robots can be useful in dealing with loneliness as they can also function as companions [6; 7]. Assistive robots have already seen widespread success in the healthcare and medicine sectors [8]. Their versatile contributions in these sectors, such as surgeries [9], radiation therapy, cancer treatment, and animal therapies [10], also led us to believe that robots can play a crucial role in coping with the current mental health situation worldwide. One possible use is to monitor patients' mental health and refer them to a professional neuro-therapist. The traditional approach to mental health monitoring is wholly based on patients recounting their days. In the end, professionals have to rely on the patients to give a true and accurate recount of their health. However, people often face difficulties remembering events accurately. Further, sadness is highly correlated with depression among patients and is a prime component of clinical diagnoses [11]. Therefore, robots can prove highly beneficial in monitoring mental health through emotion monitoring. Currently, numerous ML methods and literature reviews have shown the potentiality of different sensors to monitor human emotions, but there is still a research gap between identifying suitable signal sources and ML models for robotic applications.