Open AccessAn Intelligent Signal Processing Method for Motional Vital Signs Detection System Based on Deep Learning
Author(s) -
Siyun Liu,
Qingjie Qi,
Huifeng Cheng,
Jingwen Zhang,
Wenhao Xian,
Tianfang Ma,
Yue Wang,
Yingjie Liu,
Dan Li,
Jiamei Chai
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3211976
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Detection of vital signs for motional human targets in complex environment has always been a major challenge in the field of remote detection, remote healthcare and emergency rescue, because polytropic and multimodal interferences make intelligent signal processing more difficult. In this paper, a systematic intelligent signal processing scheme which contains signal preprocessing, vital signs identification, motion trajectory estimation and respiratory signal and heartbeat signal extraction is established. Based on CNN (Convolutional Neural Networks) model, accurate identification of motional vital signs getting rid of the interference of harmonics and distortion can be realized. Then, the misidentified outliers are eliminated with K-means clustering algorithm. Next, the motion trajectory of human targets can be estimated with Kalman filtering algorithm. Finally, the SVD-EEMD algorithm is proposed for respiratory signal and heartbeat signal extraction of dynamic human targets. The introduction of deep learning algorithms makes the proposed method have good performance of high accuracy, good robustness, strong adaptability and high efficiency, which can be observed in actual detection tasks contrast experiments.