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

• Published in: IEEE sensors journal Vol. 21; no. 5; pp. 6869 - 6879
• Main Authors: Hu, Qisong, Tang, Xiaochen, Tang, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analog to digital converters; Artificial neural network; Artificial neural networks; Conductivity; Data collection; Learning; Low cost; Monitoring; Personal computers; Posture; Pressure sensors; Real time; realtime classification; Recognition; Resistance; sensor array; Sensor arrays; Sensors; Sleep apnea; sleeping postures; smart bed; Voltage measurement
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.3043416

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.