A Novel Flexible Vital Signs and Sleep Monitoring Belt Based on a MEMS IMU and Pressure Sensors for EICU Airbed Application

Monitoring the vital signs and sleep qualities of patients lying on the airbed in Emergency Intensive Care Unit (EICU) is of great importance. In this paper, a novel flexible vital signs and sleep monitoring belt based on a Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) and pressure sensors is proposed. The key sensor unit structure and hardware system architecture are described in detail. Simulation analysis is conducted with ANSYS software, and the experimental platform is set up for data acquisition. Wavelet packet transform is adopted for obtaining the respiration signal and heartbeat signal, and then the respiration rate and heart rate can be computed by Fast Fourier Transform (FFT) analysis. Experimental results indicate that the measured error of Heart Rate (HR) is less than 1.5bpm, while that of Respiration Rate (RR) is less than 0.5bpm. Furthermore, an accuracy of about 80% in identifying the sleep stages is achieved by the sleep stage recognition method.

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Published
2025-06-29
DOI
https://doi.org/10.1109/transducers61432.2025.11110786
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

A Novel Flexible Vital Signs and Sleep Monitoring Belt Based on a MEMS IMU and Pressure Sensors for EICU Airbed Application

Zhengfei Yang, Chunhua He, Xin Fang, Heng Wu et al.
Context-Aware Activity Recognition Systems
article

A Novel Flexible Vital Signs and Sleep Monitoring Belt Based on a MEMS IMU and Pressure Sensors for EICU Airbed Application

Zhengfei Yang, Chunhua He, Xin Fang, Heng Wu, Jianming Zhan, Songqing Deng, Maojin Liang
article en

Abstract

Monitoring the vital signs and sleep qualities of patients lying on the airbed in Emergency Intensive Care Unit (EICU) is of great importance. In this paper, a novel flexible vital signs and sleep monitoring belt based on a Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) and pressure sensors is proposed. The key sensor unit structure and hardware system architecture are described in detail. Simulation analysis is conducted with ANSYS software, and the experimental platform is set up for data acquisition. Wavelet packet transform is adopted for obtaining the respiration signal and heartbeat signal, and then the respiration rate and heart rate can be computed by Fast Fourier Transform (FFT) analysis. Experimental results indicate that the measured error of Heart Rate (HR) is less than 1.5bpm, while that of Respiration Rate (RR) is less than 0.5bpm. Furthermore, an accuracy of about 80% in identifying the sleep stages is achieved by the sleep stage recognition method.

Guangdong University of Technology (CN), Sun Yat-sen University (CN), Sun Yat-sen Memorial Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 80%
Context-Aware Activity Recognition Systems
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