Sleep Posture Recognition Method Based on Sparse Body Pressure Features

Abstract

Non-visual techniques for identifying sleep postures have become essential for enhancing sleep health. Conventional methods depend on a costly professional medical apparatus that is challenging to adapt for domestic use. This study developed an economical airbag mattress and introduced a method for detecting sleeping positions via restricted body pressure data. The methodology relies on distributed body pressure data obtained from barometric pressure sensors positioned at various locations on the mattress. Two combinations of base learners were chosen based on the complementary attributes of the model, each of which can be amalgamated through a soft-voting strategy. Additionally, the architectures of Autoencoder and convolutional neural networks were integrated, collectively constituting the base learning layer of the model. Gradient enhancement was utilized in the meta-learner layer to amalgamate the output of the basic learning layer. The experimental findings indicate that the suggested holistic learning model has high classification accuracy of up to 95.95%, precision of up to 96.13%, and F1 index of up to 95.01% in sleep posture recognition assessments and possesses considerable merit. In the subsequent application, the sleep monitoring device identified the sleep posture and employed an air conditioner and an air purifier to create a more comfortable sleep environment. The user can utilize the sleep posture data to improve the quality of sleep and prevent related diseases.

Keywords

• sleeping position recognition
• ensemble learning
• mattress design
• soft voting
• stacking
• Ivy