The Sitting and lying Posture Recognition via Pressure Information based on Unconstrained Flexible Pressure Sensor

Haiwen Zhao, Lei Li, Dexuan Qi, Shijie Guo, Yuekun Zhao, Jiakuo Wang

Abstract: For detecting sleeping and sitting postures, a system based on unrestrained flexible pressure sensor is designed and proposed. The large-area capacitive pressure sensor, which composited by 64-row and 32-column, is designed to capture posture pressure signals. A total of 30 participants were recruited for 10 postures data acquisition, and the acquired images were normalized and bilinearly interpolated. The processed data images were randomly grouped by 80% of the training set and 20% of the test set. And the deep learning neural network (YOLOv5) was used for training. The final recognition accuracy was 99.3%. Accurate division in the posture categories of sleeping and sitting postures is achieved, which is important for realizing posture monitoring.