A Robust Sitting Posture Recognition System Using Acoustic Signals

With increasing computer-based work burden, prolonged poor sitting posture can result in health issues such as scoliosis. However, current sitting posture recognition systems often require the purchase of additional hardware. The camera-based system can compromise user privacy and be affected by varying lighting conditions. In this paper, we propose a solution to realize a sitting posture recognition system derived from acoustic signals generated by smartphones. Firstly, acoustic signals corresponding to various sitting postures are acquired via the built-in speaker and microphone of the smartphone. Subsequently, an innovative signal segmentation technique based on the adaptive threshold is designed to extract the signals, followed by the creation of a deep learning model for posture recognition. To meet the demands of lightweight deployment, a knowledge distillation compression technique is introduced to compress the model while maintaining its accuracy. The experimental results validate that our sitting posture recognition system has good effectiveness and robustness, making it more universal.