Sitting Posture Detection System Based on Keras Framework

Abstract

Aiming at a low-power embedded real-time sitting posture detection system, a real-time sitting posture detection system based on deep learning is designed. The system obtains the pressure of the sitting posture of the human body through a thin-film pressure sensor and the human body pressure in different sitting postures is collected and analyzed, and an analysis model is established under the Keras framework. Burn the model into STM32 through cubemax to realize real-time collection, analysis and detection of human sitting posture. Finally, the communication between the STM32 and the Android application is realized through the MQTT protocol, which realizes the real-time detection and discrimination of the sitting posture and gives the relevant sitting posture correction prompts.