Wireless Perception-Based Monitoring Technology for Human Vital Signs Status
DOI:
https://doi.org/10.61173/t3c0v390Keywords:
Wireless perception, vital sign monitoring, millimeter-wave radar, non-contact sensing, deep learningAbstract
With the rapid advancement of 5G communication and computing technologies, wireless sensing has demonstrated extensive application prospects in human vital sign monitoring. This article systematically reviews noncontact vital sign monitoring technologies based on Wi-Fi and millimeter-wave radar, covering their fundamental principles, typical applications, and key challenges. First, the fundamental mechanisms of wireless sensing are introduced, including the use of Channel State Information (CSI) in multipath environments and the micro-Doppler effect in Frequency Modulated Continuous Wave (FMCW) radar. Then, key technologies and developments in typical applications—such as driver behavior recognition, gesture interaction, and heart rate monitoring—are examined in detail. Key challenges in practical applications are summarized, including weak signal detection, clutter suppression, and model generalization. Finally, future development trends are discussed, including multimodal fusion, intelligent algorithm optimization, and device miniaturization. This review aims to provide technical references and insights for future development to researchers in related fields.
References
[1] Soto J C H, Galdino I, Caballero E, et al. A survey on vital signs monitoring based on Wi-Fi CSI data. Computer Communications, 2022, 195: 99-110.
[2] Wang Honglin, Lu Lin, Liu Pengran, et al. Millimeter waves in medical applications: status and prospects. Intelligent Medicine, 2024, 4(1): 16-21.
[3] Chen Kaiyu, Diao Yue, Wang Yucheng, et al. MCT-CNN- LSTM: A Driver Behavior Wireless Perception Method Based on an Improved Multi-Scale Domain-Adversarial Neural Network. Sensors, 2025, 25(7): 2268.
[4] Ji Qiang, Zhu Zhiwei, Lan Peilin. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technology, 2004, 53(4): 1052-1068.
[5] Li Zuo, Li Shengbo Eben, Li Renjie, et al. Online detection of driver fatigue using steering wheel angles for real driving conditions. Sensors, 2017, 17(3): 495.
[6] Chen Honghong, Han Xinyu, Hao Zhanjun, et al. Noncontact monitoring of fatigue driving using FMCW millimeter wave radar. ACM Transactions on Internet of Things, 2023, 5(1): 1-18.
[7] Hou Junjian, Zhang Bingyu, Zhong Yudong, et al. Research progress of dangerous driving behavior recognition methods based on deep learning. World Electric Vehicle Journal, 2025, 16(2): 62.
[8] Ganin Yaroslav, Ustinova Evgeniya, Ajakan Hana, et al. Domain-adversarial training of neural networks. Journal of Machine Learning Research, 2016, 17(59): 1-35.
[9] Wang Limin, Xiong Yuanjun, Wang Zhe, et al. Temporal segment networks: Towards good practices for deep action recognition. European Conference on Computer Vision, Cham: Springer International Publishing, 2016: 20-36.
[10] Lin Ji, Gan Chuang, Han Song, et al. TSM: Temporal shift module for efficient video understanding. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 7083-7093.
[11] Adib F, Mao H, Kabelac Z, et al. Smart homes that monitor breathing and heart rate. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), 2015: 837-846.
[12] Chowdhury Farhana Ahmed, Hosain Md Kamal, Islam Md Sakib Bin, et al. ECG waveform generation from radar signals: A deep learning perspective. Computers in Biology and Medicine, 2024, 176: 108555.
[13] Kim T W, Kwak K S. End-to-end electrocardiogram signal transformation from continuous-wave radar signal using deep learning with wavelet transform and neuro-fuzzy network. Applied Sciences, 2024, 14(19): 8730.
[14] Xu Dan, Xu Yiming, Xu Kaijie, et al. WaveGRU-Net: Robust non-contact ECG reconstruction via MIMO millimeterwave radar and multi-scale semantic analysis. Signal Processing, 2025: 110108.
[15] Zhao Langcheng, Lyu Rui, Lei Hang, et al. AirECG: Contactless electrocardiogram for cardiac disease monitoring via mmWave sensing and cross-domain diffusion model. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2024, 8(3): 1-27.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.

This work is licensed under a Creative Commons Attribution 4.0 International License.
