ArticleSensors (Basel, Switzerland)2022
Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022).
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology.Sensors (Basel, Switzerland) · 2024Pooled it
- Non-Contact Measurement of Human Vital Signs in Dynamic Conditions Using Microwave Techniques: A Review.Sensors (Basel, Switzerland) · 2026Review
- Heart Rate Estimation Using FMCW Radar: A Two-Stage Method Evaluated for In-Vehicle Applications.Biomimetics (Basel, Switzerland) · 2025Article
- Tidal Volume Monitoring via Surface Motions of the Upper Body-A Pilot Study of an Artificial Intelligence Approach.Sensors (Basel, Switzerland) · 2025Article
- Respiration and Heart Rate Monitoring in Smart Homes: An Angular-Free Approach with an FMCW Radar.Sensors (Basel, Switzerland) · 2024Article
- Modeling the distribution of jet fuel price returns based on fat-tail stable Paretian distribution.PloS one · 2024Article
- Article
- Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression.Sensors (Basel, Switzerland) · 2023Article
- Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals.Sensors (Basel, Switzerland) · 2023Article
- Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition.Frontiers in physiology · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m~2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate.
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Registered trials
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