A Convolutional Transformer Network for Anomaly Detection in Wireless Body Area Networks

The wireless body area network (WBAN) integrates wearable devices and Internet of Things (IoT) sensors in the human body, enabling real-time monitoring of physiological parameters for improved healthcare. Ensuring accurate and reliable data transmission is crucial to maintain system performance. To address this, we propose a novel anomaly detection framework that uses a two-stage convolutional transformer network (ConvTransformer) architecture, specifically designed to handle both point anomalies and contextual anomalies. In the first stage, we trained a ConvTransformer model to distinguish between human data and point anomalies. These out-of-range values may indicate abrupt irregularities in individual sensor readings. After identifying and filtering out point anomalies, the second stage applies another ConvTransformer model to the remaining data to detect contextual anomalies. These are more complex and involve simultaneous irregularities in multiple physiological signals (for example, heart rate, body temperature, and electrocardiogram), which may suggest more significant health concerns. This two-stage detection approach ensures more precise and robust anomaly detection. The first model achieved 99.66% accuracy in detecting point anomalies, while the second model reached nearly 99.76% accuracy in identifying contextual anomalies, showcasing the efficiency of the ConvTransformer architecture in WBAN applications for detecting anomalies.