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

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 recognitio...

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Published inIEEE sensors journal Vol. 21; no. 5; pp. 6869 - 6879
Main Authors Hu, Qisong, Tang, Xiaochen, Tang, Wei
Format Journal Article
LanguageEnglish
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
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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.
AbstractList 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.
Author Hu, Qisong
Tang, Wei
Tang, Xiaochen
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Snippet Sleeping is an indispensable activity of human beings. Sleeping postures have a significant effect on sleeping quality and health. A real-time low-cost...
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SubjectTerms 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
Title A Real-Time Patient-Specific Sleeping Posture Recognition System Using Pressure Sensitive Conductive Sheet and Transfer Learning
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