CNN Based Sensory Seat-Belt Design for Posture Recognition and Heart Rate Monitoring

Posture recognition and Heart-Rate measurement towards passenger safety without disturbing the vehicular ergonomics is much appreciated. In this work, we present a novel integrated system utilizing Velostat Pressure Sensitive Conductive (PSC) sensors and an accelerometer designed on a seat-belt. This system captures spatio-temporal data and employs a Convolutional Neural Network (CNN) model for the classification of five distinct sitting postures and non-wearing seat-belt detection. The accelerometer data is processed to estimate Heart-Rate using an algorithm, ensuring accurate and reliable measurements. The non-skin-contact accelerom- eter unit integrated to the seat-belt and recording heart- rate is preferred over other designs owing to the commuters convenience. The inclusion of a diverse subject pool to generate a comprehensive dataset, featuring an unoccupied seat state, ensures robust and realistic performance. The CNN model, interfaced with an edge computing system, with its ability to extract hidden features, achieved high accuracy in posture recognition. Additionally, the camera-less approach preserves user privacy, making it suitable for real-world applications. Our compact seat-belt incorporated sensory system represents a significant advancement in passenger monitoring technology, offering a practical and privacy-conscious solution for improv- ing vehicle safety and ergonomics. The sensory dataset, along with design files, and source-codes are made freely available for further usage to designers and researchers community.