Non-Contact Driver Respiration Rate Detection Technology Based on Suppression of Multipath Interference with Directional Antenna

Yang, Fan; He, Zhiyuan; Guo, Shisheng; Fu, Yuanhua; Li, Liang; Lu, Junfeng; Jiang, Kui

doi:10.3390/info11040192

Cited by 15 publications

(9 citation statements)

References 25 publications

(25 reference statements)

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“…Respiratory rate seems to be one the most attractive parameters using the non-contact methods and has been addressed in a variety of conferences and journal papers divided as follows: [ 19 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ] (over the period 2010–2015), [ 9 , 11 , 12 , 29 , 32 , 42 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 53 , 55 , 61 , 69 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ,…”

Section: Resultsmentioning

confidence: 99%

“…Breathing monitoring is an important component of clinical detection of vital distress, and is performed for all patients in a hospital, whether in the emergency room, a general or specific ward, or in the intensive care unit. The monitoring of a patient respiration mainly comprises an assessment of the chest wall motion [ 1 , 2 , 3 , 4 ] and measurement of physiological parameters, such as airflow and respiratory volumes [ 5 , 6 , 7 , 8 , 9 ], respiratory rate (RR) [ 10 , 11 , 12 , 13 ], the resulting transcutaneous oxygen saturation (SpO 2 ), and respiratory CO 2 removal [ 14 , 15 , 16 , 17 , 18 ]. Respiration assessment can be classified around these four elements, as shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

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Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Rehouma

Noumeir

Essouri

et al. 2020

Sensors

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Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Rehouma

Noumeir

Essouri

et al. 2020

Sensors

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show abstract

“…However, recent advancements in directional antennas and beamforming can help to mitigate this effect by making direct path refections distinct from and multipath copies. Also, multipath signals mostly act as static clutter that can be removed in the RF front end, and also during postprocessing by bandpass filtering and removing the mean of the signal (Yang et al, 2020).…”

Section: Remote Breathing Monitoringmentioning

confidence: 99%

Can Radar Remote Life Sensing Technology Help Combat COVID-19?

2021

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COVID-19, caused by SARS-CoV-2, is now a global pandemic disease. This outbreak has affected every aspect of life including work, leisure, and interaction with technology. Governments around the world have issued orders for travel bans, social distancing, and lockdown to control the spread of the virus and prevent strain on hospitals. This paper explores potential applications for radar-based non-contact remote respiration sensing technology that may help to combat the COVID-19 pandemic, and outlines potential advantages that may also help to reduce the spread of the virus. Applications arising from recent developments in the state of the art for transceiver and signal processing technologies will be discussed along associated technical implications. These applications include remote breathing rate monitoring, continuous identity authentication, occupancy detection, and hand gesture recognition. This paper also highlights future research directions that must be explored further to bring this innovative non-contact sensor technology into real-world implementation.

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“…A 2.4 GHz continuous wave forward-scattering radar respiratory detection system is proposed based on the theory that the radar cross-section (RCS) of the human body changes with human breathing. 11 Other alternative methods have been proposed to diagnose fatigue 12 ; which can be mentioned to analyze driver's behavior in lane changing. 13 Søndergaard et al 14 have studied discomfort in prolonged sitting and curvature of the back, resulting showed changes in COP (pressure center) as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Fatigue level detection using multivariate autoregressive exogenous nonlinear modeling based on driver body pressure distribution

Parsa

Javadi

Mazinan

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Prolonged driving causes symptoms of fatigue in drivers and changes their physical condition during driving. The purpose of this paper is to use a force measurement system located in the driver’s seat by force-sensitive resistance pressure sensors in order to record the received information to predict fatigue by learning regression-based models. This system is designed with 16 FSR (Force Sensing Resistor) sensors mounted on the seat and its backrest that records the driver’s body’s data, based on the force exerted by the driver on the seat in standard mode and during driving at various times. Fatigue level prediction is based on the trained nonlinear autoregressive exogenous model. In this procedure, models based on multivariate regression are first trained, and then correctness is checked. In this paper, the fatigue index is divided into five parts from 0 to 100 included fully conscious, slightly tired, moderately tired, very tired, and extremely tired, so the criterion for diagnosis is crossing the 75% of fatigue index and entering the extremely tired range. The results show that nonlinear models based on exogenous autoregressive have better performance than the linear mode, and even in the nonlinear model of NARX neural network, the fatigue of one step ahead is well predictable. A flopped state will be predictable when the body is immersed in the seat due to fatigue, so is far from the standard sitting position and will be in the extremely tired warning range.

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Non-Contact Driver Respiration Rate Detection Technology Based on Suppression of Multipath Interference with Directional Antenna

Cited by 15 publications

References 25 publications

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Can Radar Remote Life Sensing Technology Help Combat COVID-19?

Fatigue level detection using multivariate autoregressive exogenous nonlinear modeling based on driver body pressure distribution

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