Smart analysis of anxiety people and their activities using heterogeneous quasiperiodic process

Abstract

AbstractThe increase in anxiety levels worldwide can be described as a serious global health threat. Around 500 million people suffer from mental disorders and are suffering from depression, and other mental‐oriented disabilities. The new technological paradigms such as the Internet of Things (IoT) were employed for detecting, and treating these disorders, which are being proposed, developed, and provide new capabilities to detect, assess and care for anxious people. These possibilities lead to several issues that are identified, which relate to patient privacy and confidentiality, security challenges such as data security, and the organization of IoT systems. To rectify these issues, we implement the Smart analysis of anxious people and their activities using Heterogeneous computing with virtual sensing. This system consists of the health application featuring the technique of the internet of things, heterogeneous computing, Cognitive Quasiperiodic motion, and Pentagonal tiling. This system introduces the internet of things‐assisted smart analysis of anxiety against people with their activities. It performs the optimization analysis for improving the levels. The observational results focus on how to deal with the issues which were overcome in the analysis levels of the anxiety disorders, which show that time spent using the heterogeneous computing with the virtual sense proves more accuracy (92.3%), specificity (Degrees of anxiety severity by 20%), precision (Analysis of anxiety stress level by 65%), and recall (Anxiety chances percentile by 65.34%). The proposed model is simulated, and the outcomes are compared with the prevailing methods for evaluating the parameters like accuracy, end‐end‐end delay, energy consumption, network lifetime, and throughput.