AI-Powered Wristband for Accurate BAC Monitoring Using Smart Data Fusion
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Abstract: Blood Alcohol Content (BAC) measurement is critical for individual safety, law enforcement, and public health, but conventional approaches such as breath analysers and blood tests are frequently invasive, periodic, and prone to error as a result of motion artifacts, temperature changes, and intersubject metabolic variations. To overcome these issues, the research in this paper suggests an AI-based wearable system that combines multi-sensor data fusion with machine learning (ML) algorithms to improve BAC estimation accuracy. The system utilizes Photoplethysmography (PPG), Transdermal Alcohol Sensing (TAS), pulse rate, Galvanic Skin Response (GSR), and Infrared (IR) temperature sensors, with real-time processing of data via a cloud-based framework. A dataset of varied sensor readings was gathered in a controlled setting to provide robust BAC estimation under different conditions. Sophisticated machine learning (ML) algorithms such as Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and other algorithms were used for BAC prediction, utilizing multi-sensor data fusion to enhance resistance to external interferences. Results show that MLP had the best accuracy (98.99%), surpassing XGBoost and other traditional methods, with lower Root Mean Square Error (RMSE) (0.045), Mean Squared Error (MSE) (0.0020), Mean Absolute Error (MAE) (0.031), and Mean Absolute Percentage Error (MAPE) (3.2%), guaranteeing accurate BAC prediction. Pareto analysis shows Transdermal Alcohol Sensing (TAS) and pulse rate as the most significant parameters in BAC estimation. Competition analysis with prevalent models establishes the superiority of the system in reliability, accuracy, and real-time adaptation, setting it up for efficiency in alcohol monitoring, regulatory adherence, and health-related application.
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