# Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Mathematical Model of Vital Signs

## 3. Detection Algorithm

#### 3.1. Clutter Suppression Algorithm

#### 3.2. Noise Reduction Method Based on Improved EEMD Algorithm

- The maxima and minima of signal $x\left(t\right)$ are identified.
- The upper and lower envelops are obtained respectively by interpolating the set of maximal and minimal points using cubic spines.
- Computing the mean of the two envelops the mean is designated as ${m}_{1}$ then subtraction of the mean from the original signal yields ${h}_{1}=x\left(t\right)-{m}_{1}$, where ${h}_{1}$ is the first component presenting difference between the signal $x\left(t\right)$ and ${m}_{1}$.
- Verifying whether or not ${h}_{1}$ satisfies the conditions for being an IMF. If ${h}_{1}$ is not the first IMF, treating ${h}_{1}$ as the original signal $x\left(t\right)$, steps 1–3 are repeated to yield mean ${m}_{11}$ and ${h}_{11}=x\left(t\right)-{m}_{11},$ testing whether or not ${h}_{11}$ satisfies the two conditions for being an IMF again, if ${h}_{11}$ is not an IMF, steps 1–3 are repeated $k$ times to yield mean ${m}_{1k}$ and ${h}_{1k}=x\left(t\right)-{m}_{1k}$ until ${h}_{1k}$ satisfies the two conditions. The first IMF ${c}_{1}={h}_{1k}$ is generated.
- Subtraction of the ${c}_{1}$ from the original signal to yield ${r}_{1}=x\left(t\right)-{c}_{1}$, where ${r}_{1}$ is the residue, treating ${r}_{1}$ as the original signal $x\left(t\right)$, steps 1–4 are repeated to yield the second IMF ${c}_{2}$; repeating this step, the rest of the IMFs of the original signal $x\left(t\right)$ are generated, this process can be represented by the following formula:$$\begin{array}{c}{r}_{1}-{c}_{2}={r}_{2}\\ {r}_{2}-{c}_{3}={r}_{3}\\ \vdots \\ {r}_{n-1}-{c}_{n}={r}_{n}\end{array}$$

#### 3.3. Separation Method Based on the Continuous-Wavelet Transform

## 4. Radar System and Experimental Setup

#### 4.1. Radar System

#### 4.2. Experimental Setup

## 5. Results

#### 5.1. SNR Comparison of FIR Filter and Proposed Method

#### 5.2. Detection Performance of Proposed Method

## 6. Conclusions

- 1
- Sleep monitoring places higher requirements for real-time signal processing. Additionally, the influence of the orientation of a non-stationary human body with changeable sleeping positions must be considered, which is of vital significance for long-term monitoring. Therefore, further work will include an improved algorithm based on the proposed one, enabling it to adjust to non-stationary human subjects [39].
- 2
- To recognize emotions, we must measure minute variations in each individual heartbeat’s length [40]. However, extracting individual heartbeats from radar signals involves multiple challenges. Obtaining such accuracy is particularly difficult in the absence of sharp features that identify the beginning or end of a heartbeat.
- 3
- When faced with a non-metallic wall, a fraction of the radar signal travels into the wall, reflects off objects and humans, and returns to the detector imprinted with the signature of what is inside a closed room. By capturing these reflections, we can estimate vital signs like breathing and heartbeats. However, this is difficult because the signal power after traversing the wall twice (into and out of the room) is reduced by three to five orders of magnitude [41]. Weak heartbeat signals are so weak that using the previous methods cannot extract them accurately.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 1.**Schematic map of received signal with one respiratory motion and no static targets. $t$ and $\tau $ represent the slow-time and fast-time, respectively.

**Figure 4.**Noise reduction method based on improved EEMD algorithm [28].

**Figure 5.**Normalized amplitudes and SNRs of different scales in terms of corresponding peak frequency based on prior knowledge.

**Figure 7.**Transmitted signal in the time domain and frequency domain. (

**a**) Pulse generator time domain output; (

**b**) pulse generator output spectra.

**Figure 10.**Comparison of results using the FIR filter and the proposed method. (

**a**) The original waveforms obtained after clutter suppression; (

**b**) respiration waveforms obtained via the FIR band-pass filter; (

**c**) respiration waveforms obtained via the proposed method; (

**d**) heartbeat waveforms obtained via the FIR band-pass filter; (

**e**) heartbeat waveforms obtained via the proposed method.

**Figure 11.**Performance of noise reduction. (

**a**) Comparison of original vital sign signal with the denoised signal; (

**b**) noise removed after denoising processing.

**Figure 13.**Extracted signals compared with reference signals. (

**a**) Extracted heartbeat signal and ECG reference signal; (

**b**) extracted respiration signal and respiration reference signal.

**Figure 14.**Comparison of results using the FIR filter and the proposed method to recover heartbeat waveforms 5 m away from the detector. (

**a**) Original signal waveform and the denoised waveform; (

**b**) heartbeat signal waveform extracted using the FIR filter and its spectrum; (

**c**) heartbeat waveform extracted using the proposed method and its spectrum.

**Figure 15.**Detection results with different subjects at different distances. (

**a**) Extracted respiration signals and frequency spectra; (

**b**) extracted heartbeat signals and frequency spectra.

Parameters | Specifications |
---|---|

Center Frequency | 6.8 GHz |

Bandwidth | 2.3 GHz |

Target’s stance | Sitting on a chair |

Power consumption | 120 mW |

Mean output power | 55 $\mathsf{\mu}$W |

Peak-to-peak output amplitude | 0.69 V |

Parameters | FIR | Proposed Method |
---|---|---|

Respiration SNR | 4.44 dB | 12.03 dB |

Heartbeat SNR | −53.52 dB | −48.70 dB |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Hu, X.; Jin, T.
Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar. *Sensors* **2016**, *16*, 2025.
https://doi.org/10.3390/s16122025

**AMA Style**

Hu X, Jin T.
Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar. *Sensors*. 2016; 16(12):2025.
https://doi.org/10.3390/s16122025

**Chicago/Turabian Style**

Hu, Xikun, and Tian Jin.
2016. "Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar" *Sensors* 16, no. 12: 2025.
https://doi.org/10.3390/s16122025