Self-Identification Respiratory Disorder Based on Continuous Wave Radar Sensor System

Abstract

Contactless vital signs detection, based on the Doppler radar sensor system, has opened a great opportunity in biomedical applications. The radar sensor system can be used to provide the respiratory information of people without disturbing their comfort. This sensor system promises high accuracy in measuring breathing disorders as it escapes the touching sensors which might cause discomfort to the user and negatively affect their sleeping habits. Moreover, this sensor system does not require any special environment or depend on temperature and light conditions. In this paper, we propose a model to the end users; this model is to be built based on neural networks. Our proposed system can diagnose whether a person has a low, normal, or high breathing rate. This model can also be extended to more specific categories to help doctors to determine breathing disorders in patients. In this paper, a continuous wave radar sensor system, based on a vector network analyzer (VNA), is used to measure the breathing rate remotely. The measured signal from this radar sensor system is then processed for further purposes. Different extracted feature methods are implemented to obtain the breathing rate from the non-contact radar sensor system. A model based on the machine learning technique is investigated to classify the breathing disorder. A total of 31 people who were asked to perform low/normal/high breathing were measured by the CW radar sensor. The measured data were also used to build a machine learning based model. The breathing rate measured by the CW radar sensor system is compared with the reference measurement by the five-point touching Shimmer sensor. The results of the breathing rate are compatible. Two main time-frequency (TF) extraction feature methods, short-time Fourier transform (STFT) and continuous wavelet transform (CWT), were implemented in the proposed system. Under these extraction techniques, some classification approaches were employed and have shown high accuracy in categorizing the respiratory types. The research shows the possibility of building an artificial intelligence (AI) module for a non-contact radar sensor system to inform the end user of their breathing situation. This research enables a smarter and more friendly remote-detecting vital signs sensor system.

Non-Contact Heartbeat Detection by Heartbeat Signal Reconstruction Based on Spectrogram Analysis With Convolutional LSTM

“…To collect the training data, we observed heartbeat against 8 subjects sitting still for 240 s. Also, to extend the diversity of the HR variation over the training data, we interpolated the input and output data as the following steps: (i) R-peaks were detected over the ECG signal, (ii) the timing when heartbeat did not occur was detected based on the results of the first step, and (iii) the input and output data were interpolated by the AR (Autoregressive) model. Through this extension of the training data, we collected 10,350 training data with the HR of [30,120] bpm. In contrast, as the testing data, we observed heartbeat against 17 subjects with the Doppler sensor.…”

Portable Micro-Doppler Radar with Quadrature Radar Architecture for Non-Contact Human Breath Detection

“…For the past few decades, the medical field has utilized radar systems, such as for tumor detection, breath monitoring, heartbeat monitoring, and the detection of buried victims in natural disasters [ 16 , 17 , 18 , 19 , 20 ]. Many research works in the healthcare sector focused on detecting the function of vital human organs, such as the lungs and heart, as they are essential health indicators [ 7 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Interchannel Interference and Mitigation in Distributed MIMO RF Sensing

“…Depending on the type of radar, it may measure the range, Doppler (or micro-Doppler) signature, and angular information of a target within certain limitations. Depending on the problem, a radar may be designed and deployed as a continuous wave (CW) radar [19], frequency-modulated continuous wave (FMCW) radar [20], pulsed radar [21], bistatic radar, monopulse radar [22,23], synthetic aperture radar (SAR) [24], digital beamforming (DBF) multiple-input multiple-output (MIMO) radar in a monostatic configuration [25,26], or distributed MIMO radar [27][28][29]. Recently, short-to medium-range FMCW radars have been gaining increasing attention for commercial indoor and outdoor applications.…”