The 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.

中文翻译：

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使用异质准周期过程智能分析焦虑人群及其活动

全球范围内焦虑水平的上升可以说是对全球健康的严重威胁。大约有 5 亿人患有精神障碍，患有抑郁症和其他精神障碍。物联网 (IoT) 等新技术范式被用来检测和治疗这些疾病，这些技术范式正在被提出、开发，并提供检测、评估和护理焦虑人群的新功能。这些可能性导致了几个已确定的问题，这些问题涉及患者隐私和机密性、数据安全等安全挑战以及物联网系统的组织。为了解决这些问题，我们使用异构计算和虚拟传感来对焦虑的人及其活动进行智能分析。该系统由具有物联网、异构计算、认知准周期运动、五边形平铺技术的健康应用组成。该系统引入了物联网辅助智能分析人们对其活动的焦虑情绪。它执行优化分析以提高级别。观察结果集中于如何处理焦虑症分析层面所克服的问题，这表明使用虚拟感觉的异构计算所花费的时间证明了更高的准确性（92.3%）、特异性（焦虑严重程度）提高 20%）、精确度（焦虑压力水平分析提高 65%）和回忆（焦虑几率百分位提高 65.34%）。对所提出的模型进行了仿真，并将结果与​​评估精度、端到端延迟、能耗、网络寿命和吞吐量等参数的流行方法进行了比较。