A Real-Time Patient-Specific Sleeping Posture Recognition System Using Pressure Sensitive Conductive Sheet and Transfer Learning

Abstract

Sleeping is an indispensable activity of human beings. Sleeping postures have a significant effect on sleeping quality and health. A real-time low-cost sleeping posture recognition system with high privacy and good user experience is desired. In this article, we propose a sleeping posture recognition system based on a low-cost pressure sensor array which consists of conductive fabric and conductive wires. The sensor array is deployed as a bedsheet with 32 rows and 32 columns resulting in 1024 nodes. An Arduino Nano performs data collection using a 10-bit Analog to Digital Converter (ADC). The sampling rate of the overall sensor array is 0.4 frame/sec. Six health-related sleeping postures of five participants can be recognized by a shallow Convolutional Neural Network (CNN) deployed on a Personal Computer (PC). The system accuracy achieved 84.80% using the standard training-test method and 91.24% using the transfer learning-based subject-specific method. The real-time processing speed achieved 434 us/frame.