Random Body Motion Research Articles

A new method for vital sign detection using FMCW radar based on random body motion cancellation.

In this study, we present a new method for acquiring human vital signs using a Range-Doppler matrix (RDM) of FMCW radar data and a Gaussian interpolation algorithm (GIA). First, the RDM is derived by applying a two-dimensional fast Fourier transform (2D-FFT) to the radar data, and the GIA is applied in the Doppler dimension to estimate the target velocity signal. Subsequently, a robust enhanced trend filtering (RETF) algorithm is used to eliminate the large-scale body motion from the vital signs. Finally, the time-varying filter-based empirical mode decomposition (TVF-EMD) algorithm is employed to extract the respiratory and heartbeat intrinsic mode functions (IMFs), which are filtered according to their respective spectral power to obtain the respiratory and heartbeat frequencies. The proposed method was evaluated using vital signs data collected from seven volunteers (4 males and 3 females) with Texas Instrument's AWR1642, and the results were compared with data from a reference monitor. The experiments showed that the method had an accuracy of 93 % for respiration and 95 % for heart rate in the presence of random body movements. Unlike traditional radar-based vital signs detection methods, this approach does not rely on range bin selection of the range profile matrix (RPM), thereby avoiding phase wrap problems and producing more accurate results. Currently, research in this field is limited.

Robust Radar-Based Vital Sensing With Adaptive Sinc Filtering and Random Body Motion Rejections

In this paper, we propose a non-contact vital sign monitoring algorithm based on 60GHz Frequency Modulated Continuous Wave (FMCW) radar. At the heart of the algorithm is a bank of sinc filters covering the heart and respiratory frequencies of an healthy adult. The filter bank is adapted, in successive iterations, to the specific cardio-respiratory frequencies of the monitored subject. The adaptability of the algorithm ensures an accurate estimate of Heart Rate (HR) and Respiratory Rate(RR) for a subject sitting in front of the radar at a distance between 0.3m to 1.1m while being robust to minor body movements and harmonics. The efficiency of the solution is demonstrated on an experimental data set including recordings of 10 subjects, where we obtain an Root Mean Square Error (RMSE) of 6.16 bpm for HR and 2.09 rpm for RR.

Real-Time Heart Rate Detection Method Based on 77 GHz FMCW Radar.

This paper proposes a real-time heart rate detection method based on 77 GHz FMCW radar. Firstly, the method establishes a new motion model according to respiratory and heartbeat rules, and extracts the motion signals of the chest and the abdomen; then, the random body motion (RBM) signal is eliminated by a combination of polynomial fitting and recursive least squares (RLS) adaptive filtering; lastly, multi-detection-point adaptive harmonics cancellation (AHC) is used to eliminate respiratory harmonics. In addition, the method introduces a spectrum analysis algorithm based on linear predictive coding (LPC). The experimental results show that the method can effectively eliminate the RBM signal and respiratory harmonics, and that the average real-time heart rate detection error rate is 2.925%.

Flexible Triboelectric Nanogenerators Based on Electrospun Poly(vinylidene fluoride) with MoS2/Carbon Nanotube Composite Nanofibers.

With the miniaturization of wearable smart devices, the demand for portable and sustainable power sources is increasing. Herein, a flexible and lightweight triboelectric nanogenerator (PMC-TENG) was fabricated with MoS2/carbon nanotube (MC)-doped PVDF as the friction substrate based on electrospinning for harvesting random body motion energy under complex mechanical deformations. The charge density on the friction surface of PVDF nanofibers was found to increase significantly as the introduced electron acceptor of the MC composite, and nylon as a clothing material for another friction layer simplifies the structure of the device. Upon optimization of the electrospinning preparation process, the output voltage of the prepared PMC-TENG can reach >300 V and the instantaneous power can reach 0.484 mW (∼6 cm × 6 cm). At the same time, the PMC-TENG remains stable over 3000 cycles and has the ability to charge a capacitor. The flexible device demonstrates an excellent capability of converting mechanical energy to electrical energy. Therefore, this study has good prospects for application in the field of power supply for portable electronic devices and others.

Non-Contact Heart Rate Monitoring Based on Millimeter Wave Radar

Millimeter Wave(mmWave) radar has been widely used in vital sign monitoring with non-contact and privacy protection. To reduce the effect of random body motion, respiration, and its harmonics on heart rate estimation, the heart rate monitoring based on frequency-modulated continuous-wave (FMCW) radar is researched in this paper. First, a vital sign signal extraction algorithm is designed for the Range Bin variation caused by random body motion and heavy breathing. Second, a heartbeat signal extraction algorithm is designed based on Adaptive Notch Filter (ANF) and Empirical Wavelet Transform (EWT). The harmonic of respiration will be suppressed, and the heartbeat signal will be separated. Finally, the weighted estimation is performed according to the relationship of harmonics of the heartbeat signal to obtain the heart rate. Twenty subjects are invited to the experiment. The experimental results show that the proposed method can improve the signal-to-noise ratio (SNR), reduce the harmonic interference, and estimate the heart rate with a mean absolute error less than 4BPM.

Bio-Radar Cardiac Signal Model Used for HRV Assessment and Evaluation Using Adaptive Filtering

The ability to remotely assess vital signs using radar systems may potentialize the healthcare systems, since it does not require any direct interaction with the subject. Nonetheless, these systems present inherent challenges that could compromise the accuracy of the vital signs parameters. For instance, there is a lack of resolution on the cardiac signal, since the radar only detects the chest micro displacement. Low cardiac resolution prevents the exact localization of signal peaks, compromising the Heart Rate Variability (HRV) assessment. In this work, we use a customized band-pass filter obtained through adaptive filtering, applied to the Electrocardiography (ECG) signal to generate the corresponding synthetic radar signal. This provides an easy way to extract a cardiac radar signal model, free of issues that arise from random body motion or from an eventual decrease in signals quality, which are inherent issues of long-term acquisitions. This model was further used to verify if the signal resolution provided by lower carrier radars is suitable to determine the HRV parameters accurately.

Radar detection of multi-target vital signs based on blind source separation

Radar non-contact monitoring of vital signs has a broad application prospect in clinical monitoring. Aiming at the problem of strong interference in non-contact vital signs detection (Such as multi-target, random body motion), a blind source separation (BSS) signal detection method based on Fast-ICA is proposed to reduce the interference of multi-target. In this algorithm, entropy is used to evaluate the non Gaussian property, and the appropriate transformation matrix is selected, according to the statistical independence of the signals, the source signals are separated from the observed mixed signals. On this basis, the traditional blind source separation process is improved, and the wavelet transform preprocessing algorithm based on translation invariant is added to suppress the interference of static clutter. The feasibility of this method is verified by simulation experiments.

Estimation of Human Body Vital Signs Based on 60 GHz Doppler Radar Using a Bound-Constrained Optimization Algorithm.

In this study, a bound-constrained optimization algorithm is applied for estimating physiological data (pulse and breathing rate) of human body using 60 GHz Doppler radar, by detecting displacements induced by breathing and the heartbeat of a human subject. The influence of mutual phasing between the two movements is analyzed in a theoretical framework and the application of optimization algorithms is proved to be able to accurately detect both breathing and heartbeat rates, despite intermodulation effects between them. Different optimization procedures are compared and shown to be more robust to receiver noise and artifacts of random body motion than a direct spectrum analysis. In case of a large-scale constrained bound, a parallel optimization procedure executed in subranges is proposed to realize accurate detection in a reduced span of time.

A New Ka-Band Doppler Radar in Robust and Precise Cardiopulmonary Remote Sensing

In this paper, a new Ka-band Doppler radar with an ability to remotely monitor respiration rate, heartbeat rate (HR), and heart rate variability (HRV) is presented. Exploiting the advantage of high sensitivity of Ka-band radar in sensing of chest-wall movement, we make more accurate reconstruction of cardiopulmonary activities from low-quality received signal. The advantage of using Ka-band radar is obtained by paying a penalty, which is harmonic interferences that make measuring of cardiopulmonary details infeasible in lots of real scenarios. In this paper, by using a more complete model of return signal, a new routine is introduced for the robust detection of breathing and HR that extremely alleviates the problems associated with harmonic interferences. Besides, a more suitable model is proposed, which can cover time-variability cardiorespiratory features. Taking advantage of this model and based on the unconditional orthonormal representation of band-limited signals, a filter with time-varying coefficients is proposed to extract HRV. This extraction is done without resorting to tracking HR trace that makes it more precise and robust than the conventional approach. The indicated filter enables us to suggest a novel approach to remove random body motion effects. By experimental data, the applicability of the new radar for a wide range of real world scenarios is investigated, and its advantages beside conventional approaches are proved practically in different aspects.

All-in-One Shape-Adaptive Self-Charging Power Package for Wearable Electronics.

Recently, a self-charging power unit consisting of an energy harvesting device and an energy storage device set the foundation for building a self-powered wearable system. However, the flexibility of the power unit working under extremely complex deformations (e.g., stretching, twisting, and bending) becomes a key issue. Here, we present a prototype of an all-in-one shape-adaptive self-charging power unit that can be used for scavenging random body motion energy under complex mechanical deformations and then directly storing it in a supercapacitor unit to build up a self-powered system for wearable electronics. A kirigami paper based supercapacitor (KP-SC) was designed to work as the flexible energy storage device (stretchability up to 215%). An ultrastretchable and shape-adaptive silicone rubber triboelectric nanogenerator (SR-TENG) was utilized as the flexible energy harvesting device. By combining them with a rectifier, a stretchable, twistable, and bendable, self-charging power package was achieved for sustainably driving wearable electronics. This work provides a potential platform for the flexible self-powered systems.

Self-powered textile for wearable electronics by hybridizing fiber-shaped nanogenerators, solar cells, and supercapacitors.

Wearable electronics fabricated on lightweight and flexible substrate are believed to have great potential for portable devices, but their applications are limited by the life span of their batteries. We propose a hybridized self-charging power textile system with the aim of simultaneously collecting outdoor sunshine and random body motion energies and then storing them in an energy storage unit. Both of the harvested energies can be easily converted into electricity by using fiber-shaped dye-sensitized solar cells (for solar energy) and fiber-shaped triboelectric nanogenerators (for random body motion energy) and then further stored as chemical energy in fiber-shaped supercapacitors. Because of the all-fiber-shaped structure of the entire system, our proposed hybridized self-charging textile system can be easily woven into electronic textiles to fabricate smart clothes to sustainably operate mobile or wearable electronics.

Single-Antenna Doppler Radars Using Self and Mutual Injection Locking for Vital Sign Detection With Random Body Movement Cancellation

This work presents a single-antenna self-injection-locked (SIL) radar to reduce the hardware complexity of continuous-wave (CW) Doppler systems. The theory provides a basis for determining the signal-to-noise spectral density ratio (SNDR) with the effects of clutter. Experimental results agree closely with the theoretical predictions, showing that the clutter does not affect the optimal SNR performance in an SIL radar. A single-antenna SIL radar array is designed to detect vital signs with random body movement cancellation. To this end, a subject is placed between two single-antenna SIL radars to measure the rates of respiration and heartbeat using Doppler shift, and the effects of random movement of the subject are cancelled by wireless mutual injection locking (MIL) of the two radars. In an experiment, a prototype of such a two-radar array with a spacing of 2 m was implemented at 2.4 GHz, providing accurate and reliable cardiopulmonary monitoring of a subject who jogged on a treadmill with random body motion of many centimeters.