Artificial intelligence for stress monitoring and prediction using wearable sensors in internet of things

The internet of medical things (IoMT) is considered as a middle platform between medical systems and communication systems. Machine learning (ML) is used as a new innovation prediction method for supporting stress monitoring factors in safety-critical information in IoMT environments. Therefore, ML approaches can support safety, accuracy and security for sensitive personal information in medical systems and healthcare applications. This paper presents a new literature management for main concept of ML methodologies on the IoMT ecosystem. Hence, the optimality of Stress monitoring is the primary research area in wearable sensors. Several contemporary papers exist on this important subject. The research gap in reviewing all these ML algorithms has motivated us for their presentation in the form of a detailed technical analysis in this paper. Architectures of IoMT are introduced prior to the development of the factors governing the stress monitoring decision making process and their reviews.