A Survey on smoking and Alcohol Tracking System: State of the Art, Analysis, and Performance Evaluation

Addiction to alcohol and tobacco is still a major worldwide health issue that requires innovative approaches to early detection and monitoring. This review of the literature provides a thorough summary of recent research projects in the area of tracking systems for alcohol and tobacco use that are both intrusive and non-intrusive for correct classification. The main goal of this research is to investigate how different IOT-based models might improve the precision and effectiveness of alcohol and smoking detection. The measurement of several IoT models used in tracking systems for alcohol and smoking makes up a sizeable section of this work. The study assesses the benefits and drawbacks of each model. Additionally, how effectively it can identify individuals who smoke or drink and how well it can work with various datasets. This research intends to direct future research efforts and encourage the development of more precise and effective tracking systems for smoking and alcohol use, ultimately resulting in improved public health outcomes.