Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

Unobtrusive monitoring of children's heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children's RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson's correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.

Analysis of Signal Processing Methods to Reject the DC Offset Contribution of Static Reflectors in FMCW Radar-Based Vital Signs Monitoring

Frequency-modulated continuous wave (FMCW) radars are currently being investigated for remote vital signs monitoring (measure of respiration and heart rates) as an innovative wireless solution for healthcare and ambient assisted living. However, static reflectors (furniture, objects, stationary body parts, etc.) within the range or range angular bin where the subject is present contribute in the Doppler signal to a direct current (DC) offset. The latter is added to the person's information, containing also a useful DC component, causing signal distortion and hence reducing the accuracy in measuring the vital sign parameters. Removing the sole contribution of the unwanted DC offset is fundamental to perform proper phase demodulation, so that accurate vital signs monitoring can be achieved. In this work, we analyzed different DC offset calibration methods to determine which one achieves the highest accuracy in measuring the physiological parameters as the transmitting frequency varies. More precisely, by using two FMCW radars, operating below 10 GHz and at millimeter wave (mmWave), we applied four DC offset calibration methods to the baseband radar signals originated by the cardiopulmonary activities. We experimentally determined the accuracy of the methods by measuring the respiration and the heart rates of different subjects in an office setting. It was found that the linear demodulation outperforms the other methods if operating below 10 GHz while the geometric fitting provides the best results at mmWave.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland-Altman consistency analysis statistical method with the algorithm's respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Non-Contact Breathing Monitoring Using Sleep Breathing Detection Algorithm (SBDA) Based on UWB Radar Sensors

Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.

High-Speed Continuous Wavelet Transform Processor for Vital Signal Measurement Using Frequency-Modulated Continuous Wave Radar

This paper proposes a high-speed continuous wavelet transform (CWT) processor to analyze vital signals extracted from a frequency-modulated continuous wave (FMCW) radar sensor. The proposed CWT processor consists of a fast Fourier transform (FFT) module, complex multiplier module, and inverse FFT (IFFT) module. For high-throughput processing, the FFT and IFFT modules are designed with the pipeline FFT architecture of radix-2 single-path delay feedback (R2SDF) and mixed-radix multipath delay commutator (MRMDC) architecture, respectively. In addition, the IFFT module and the complex multiplier module perform a four-channel operation to reduce the processing time from repeated operations. Simultaneously, the MRMDC IFFT module minimizes the circuit area by reducing the number of non-trivial multipliers by using a mixed-radix algorithm. In addition, the proposed CWT processor can support variable lengths of 8, 16, 32, 64, 128, 256, 512, and 1024 to analyze various vital signals. The proposed CWT processor was implemented in a field-programmable gate array (FPGA) device and verified through the measurement of heartbeat and respiration from an FMCW radar sensor. Experimental results showed that the proposed CWT processor can reduce the processing time by 48.4-fold and 40.7-fold compared to MATLAB software with Intel i7 CPU. Moreover, it can be confirmed that the proposed CWT processor can reduce the processing time by 73.3% compared to previous FPGA-based implementations.