Pain is a profoundly stressful experience that significantly impacts an individual’s daily life. In many situations, people can express the intensity of pain via some observable physical actions like crying or shouting. However, in cases where the patient is non-communicative, they cannot convey their feelings through these actions. In both scenarios, automatically monitoring pain intensity using technology presents a considerable challenge. In the literature, researchers have presented numerous techniques for automatic pain monitoring using multiple approaches. This technological survey paper aims to provide an overview of current advancements in the field of automatic pain monitoring. In this paper, we present a taxonomy that summarizes our survey on the utilization of technology areas for monitoring pain automatically. Those technologies are based on Internet of Things (IoT), computer vision, and multimodal techniques. These technologies utilize various modalities, including physiological signals, facial expressions, vocalizations, and behavioral patterns, to detect and quantify pain. The paper discusses the advantages and limitations of each modality, as well as the challenges faced in developing accurate and reliable pain monitoring systems. Additionally, the paper surveys the current state of research in this field, including the development of machine learning algorithms and wearable devices for pain monitoring. Overall, this paper provides a comprehensive overview of the current state of automatic pain monitoring technology and highlights areas for future research and development. This paper also creates a keyword map that will serve as a valuable resource for researchers, enabling them to refine their investigations by identifying frequently used terms and emerging trends within each domain.
Naseer et al. (Thu,) studied this question.