Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep

被引:9
|
作者
Choi, Sang Ho [1 ]
Yoon, Heenam [2 ]
机构
[1] Kwangwoon Univ, Sch Comp & Informat Engn, Seoul 01897, South Korea
[2] Sangmyung Univ, Dept Human Ctr Artificial Intelligence, Seoul 03016, South Korea
基金
新加坡国家研究基金会;
关键词
IR-UWB radar; noncontact; vital-sign monitoring; real time; deep learning; UWB; GESTURES;
D O I
10.3390/s23063116
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Real-time Vital Signs Monitoring and Data Management Using a Low-Cost IoT-based Health Monitoring System
    Mishra, Antim Dev
    Thakral, Bindu
    Jijja, Alpana
    Sharma, Nitin
    JOURNAL OF HEALTH MANAGEMENT, 2024, 26 (03) : 449 - 459
  • [42] WiFi-based Real-time Breathing and Heart Rate Monitoring during Sleep
    Gu, Yu
    Zhang, Xiang
    Liu, Zhi
    Ren, Fuji
    2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,
  • [43] Non-contact and Real-time Pulse-based radar with Sensitivity Improvement for Vital-sign Monitoring
    Huang, Jian-Yu
    Hsu, Chia-Chin
    Chang, Chia-Hung
    Hu, Wei-Wen
    2018 ASIA-PACIFIC MICROWAVE CONFERENCE PROCEEDINGS (APMC), 2018, : 812 - 814
  • [44] Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients
    Park, Jinho
    Nguyen, Thien
    Park, Soongho
    Hill, Brian
    Shadgan, Babak
    Gandjbakhche, Amir
    BIOENGINEERING-BASEL, 2024, 11 (07):
  • [45] FT-RCNN: Real-time Visual Face Tracking with Region-based Convolutional Neural Networks
    Lin, Yiming
    Shen, Jie
    Cheng, Shiyang
    Pantic, Maja
    2020 15TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2020), 2020, : 61 - 68
  • [46] A Real-Time Polygonal Wheel-Rail Force Identification Method Based on Convolutional Neural Networks (CNN)
    Zhang, Zeteng
    Wang, Jinhai
    Yang, Jianwei
    Yao, Dechen
    URBAN RAIL TRANSIT, 2025,
  • [47] REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS
    Milioto, Andres
    Lottes, Philipp
    Stachniss, Cyrill
    INTERNATIONAL CONFERENCE ON UNMANNED AERIAL VEHICLES IN GEOMATICS (VOLUME IV-2/W3), 2017, 4-2 (W3): : 41 - 48
  • [48] A Novel Denoising Approach Based on Improved Invertible Neural Networks for Real-Time Conveyor Belt Monitoring
    Guo, Xiaoqiang
    Liu, Xinhua
    Zhang, Xu
    Krolczyk, Grzegorz M.
    Gardoni, Paolo
    Li, Zhixiong
    IEEE SENSORS JOURNAL, 2023, 23 (03) : 3194 - 3203
  • [49] A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks
    Xie, Xiaoyue
    Ma, Yuan
    Liu, Bin
    He, Jinrong
    Li, Shuqin
    Wang, Hongyan
    FRONTIERS IN PLANT SCIENCE, 2020, 11
  • [50] Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
    Abdeljaber, Osama
    Avci, Onur
    Kiranyaz, Serkan
    Gabbouj, Moncef
    Inman, Daniel J.
    JOURNAL OF SOUND AND VIBRATION, 2017, 388 : 154 - 170