Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar
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 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 then subtraction of the mean from the original signal yields , where is the first component presenting difference between the signal and .
- Verifying whether or not satisfies the conditions for being an IMF. If is not the first IMF, treating as the original signal , steps 1–3 are repeated to yield mean and testing whether or not satisfies the two conditions for being an IMF again, if is not an IMF, steps 1–3 are repeated times to yield mean and until satisfies the two conditions. The first IMF is generated.
- Subtraction of the from the original signal to yield , where is the residue, treating as the original signal , steps 1–4 are repeated to yield the second IMF ; repeating this step, the rest of the IMFs of the original signal are generated, this process can be represented by the following formula:
3.3. Separation Method Based on the Continuous-Wavelet Transform
4. Radar System and Experimental Setup
4.1. Radar System
4.2. Experimental Setup
5.1. SNR Comparison of FIR Filter and Proposed Method
5.2. Detection Performance of Proposed Method
- 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 .
- To recognize emotions, we must measure minute variations in each individual heartbeat’s length . 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.
- 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 . Weak heartbeat signals are so weak that using the previous methods cannot extract them accurately.
Conflicts of Interest
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|Center Frequency||6.8 GHz|
|Target’s stance||Sitting on a chair|
|Power consumption||120 mW|
|Mean output power||55 W|
|Peak-to-peak output amplitude||0.69 V|
|Respiration SNR||4.44 dB||12.03 dB|
|Heartbeat SNR||−53.52 dB||−48.70 dB|
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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
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/s16122025Chicago/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