Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi

By Jian Liu, Yan Wang, Yingying Chen, Jie Yang, Xu Chen, Jerry Q. Cheng. ACM Interational Symposium on Mobile Ad Hoc Networking and Computing, 2015

496 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Physiology

View Paper: https://www.semanticscholar.org/paper/d0a82f206119cbbae90b27a4153e0d882b14a0c3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Gang Zhou
2. Chen Wang
3. Hongbo Liu
4. Chen Chen
5. Jian Liu
6. Youfang Lin
7. Yan Wang
8. Yingying Chen

In Electrical and Electronic Engineering

1. Shiwen Mao
2. Xuyu Wang
3. Daqing Zhang
4. Tao Gu
5. Gang Zhou
6. Tolga Soyata
7. Chen Wang
8. Hongbo Liu

Citations in the corpus

• A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “ing toward the measurement of multiple biomarkers, such as respiration [31], [32], heartbeat [33], and motion detection [34], [35], although practical designs are still an active”
• RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices (2017, background): “Index Terms—Fall detection, activity recognition, channel state information (CSI), WiFi, phase difference Ç”
• WiFi Sensing with Channel State Information (2019, background): “Usually a sliding window and multiplying factors are used to give different weights, e.g., Weighted Moving Average (WMA) and Exponentially Weighted Moving Average (EWMA). low-pass filters (LPF) can also remove high frequency noises assisted by signal transform methods, e.g., Fast Fourier Transform (”
• PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices (2017, methodology): “Recently, the authors in [13] exploit the amplitudes of CSI data of WiFi to track vital signs.”
• Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022, methodology): “In addition, extra predictors, including modeling-based and learning-based algorithms, are indispensable to final detection, recognition, and estimation [21, 24, 27, 30, 34, 35, 38, 42, 45, 52, 53, 59, 65, 70, 90, 109, 112, 118, 120, 125, 127].”
• Human respiration detection with commodity wifi devices: do user location and body orientation matter? (2016, methodology): “For single-user respiration detection , to capture the periodic changes caused by chest movements during inhaling and exhaling, we use a Fake Peak Removal algorithm as in [21] to remove peaks which are too close to each other.”
• Wireless Sensing for Human Activity: A Survey (2020, background): “[49], [50] re-use existing WiFi network to track the breathing and heartbeat concurrently without requiring dedicated/wearable sensors or additional wireless infrastructure.”