Automated Anomaly Detection in WBAN Smartwatch-Generated Vital Signs Using TPOT Classifier

Information sharing between patients and medical officers is the core of wireless communication in the medical field. Wireless technology and the Internet of Things (IoT) form the Wireless Body Area Network (WBAN). The WBAN sensors can thoroughly track the patient’s biological factors from head to toe and communicate with the destination, such as doctors, caretakers, family, etc., for remote diagnosis and prediction. Maintaining data integrity is crucial for precise diagnosis, necessitating robust protection of patient’s biological data against malicious threats. Automated anomaly detection of WBAN data is required to ensure data integrity in real-world scenarios efficiently for high-speed diagnosis. The contribution of the work lies in creating a wearable WBAN model, performing anomaly detection using Machine Learning (ML) and automated ML (AutoML) models, and analyzing their performance for real-world data. The smartwatch wearable device is deployed in this work to estimate vital signs like heartbeat, pulse, oxygen intake/exhalation, blood pressure, and oxygen saturation. The dataset generated with these essential signs is trained and tested with the ML classifiers to diagnose whether the generated WBAN data is healthy (normal) or unhealthy (abnormal). Traditional and auto ML are used to evaluate the biological dataset for anomaly detection. The novelty of the work is the real-time data used for anomaly detection using the AutoML Tree-based Pipeline Optimization Tool (TPOT) classifier algorithm, which generates a pipeline for future data classification. The complexity and processing overhead of anomaly detection using TPOT is reduced compared to traditional ML. The various performance metrics, such as precision, accuracy, recall, and F1 score, are compared between conventional and auto ML classifiers. TPOT is executed, resulting in the best XGB classifier pipeline with a test accuracy of 98.91