In-Cabin MIMO Radar System for Human Dysfunctional Breathing Detection

Lopez, Maria-Jose; Palacios, Cesar; Garbí, Jordi Romeu; Roca, Lluís Jofre

doi:10.1109/jsen.2022.3221052

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…People with driving anxiety may experience a sudden and intense feeling of fear, sweating, racing heart, tremor, nausea, dry mouth, difficulty breathing, dizziness, or fainting. This stress status is analyzed as a dysfunctional breathing pattern [23], where thoracic breathing is dominant. Here, the person takes small, shallow breaths, using his shoulders and thorax instead of his diaphragm to move air into and out of his lungs.…”

Section: Highmentioning

confidence: 99%

“…Because of that, in the first phase of this work, an advanced mechanical system has been built for simulating the movement of the torso. It has been assembled using six plates with dielectric properties similar to those of the skin: ε sk = 5.6@120 GHz [23]. The plates have been mounted in the same arrangement as the six regions defined in the torso and have been connected to DC motors with rotation and position control by means of microcontrollers.…”

Section: Torso Elongation Simulatormentioning

confidence: 99%

“…In addition, physiological measurements, which are the focus of this work, are better suited to detect stress levels as they change in the early stages of drowsiness or anxiety. The level of stress in drivers modifies some physiological functions, such as breathing rate, breathing patterns, and heartbeat, which can be evidenced in particular in the different elongations of the human torso during driving [23].…”

Section: Introductionmentioning

confidence: 99%

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Supervised Machine Learning-Assisted Driving Stress Monitoring MIMO Radar System

López,

Arias,

Romeu

et al. 2023

IEEE Sensors J.

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Factors such as road traffic, challenging ambient temperature conditions, and extended periods of driving have detrimental effects on the physical and mental well-being of a driver. These factors can alter the stress levels of the driver, thereby diminishing his or her capacity to make effective decisions when faced with hazardous situations on the road. In this regard, this study presents a novel approach utilizing machine learning in a MIMO radar system to accurately assess three different driver stress levels, such as drowsiness, awakeness, and anxiety. The MIMO radar system captures the elongation distance and velocity of six specific regions of the frontal torso of the driver in an advanced driving simulator based on virtual reality. This allows for the extraction of vital physiological parameters such as heart rate, respiratory rhythm, and breathing patterns over time, as well as the identification of changes in driving style determined by variations in their relative position in the seat and control of the steering wheel. Then, a fully-connected neural network model is trained with the acquired data, and its performance is evaluated with three volunteers submitted to four different driving situations that induce stress in the driver. The findings show an accuracy in drowsiness detection of 90%, awakeness of 96% and anxiety of 85%.

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Section: Highmentioning

confidence: 99%

Section: Torso Elongation Simulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervised Machine Learning-Assisted Driving Stress Monitoring MIMO Radar System

López,

Arias,

Romeu

et al. 2023

IEEE Sensors J.

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“…The installation cost, privacy concerns, lag times, or persistent inability to detect sedentary individuals has prevented sensors that can truly detect the presence of sedentary occupants from penetrating the market. However, recent developments in Doppler-radar occupancy sensing are promising because they detect breathing motion [10,12,13] and are being shown to reliably detect the presence of sedentary occupants in realistic settings [14], including in vehicle cabins [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Characterization Technique for a Doppler Radar Occupancy Sensor

Whitworth,

Droitcour,

Song

et al. 2023

Electronics

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Occupancy sensors are electronic devices used to detect the presence of people in monitored areas, and the output of these sensors can be used to optimize lighting control, heating and ventilation control, and real-estate utilization. Testing methods already exist for certain types of occupancy sensors (e.g., passive infrared) to evaluate their relative performance, allowing manufacturers to report coverage patterns for different types of motion. However, the existing published techniques are mostly tailored for passive-infrared sensors and therefore limited to evaluation of large motions, such as walking and hand movement. Here we define a characterization technique for a Doppler radar occupancy sensor based on detecting a small motion representing human breathing, using a well-defined readily reproducible target. The presented technique specifically provides a robust testing method for a single-channel continuous wave Doppler-radar based occupancy sensor, which has variation in sensitivity within each wavelength of range. By comparison with test data taken from a human subject, we demonstrate that the mobile target provides a reproducible alternative for a human target that better accounts for the impact of sensor placement. This characterization technique enables generation of coverage patterns for breathing motion for single-channel continuous wave Doppler radar-based occupancy sensors.

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In-Vehicle Multiple Passengers Respiration Monitoring Based on Surface-Circuit Metasurface Tags Using Time-Division FMCW Radar

Kang,

Li,

2024

IEEE Internet Things J.

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In-Cabin MIMO Radar System for Human Dysfunctional Breathing Detection

Cited by 6 publications

References 42 publications

Supervised Machine Learning-Assisted Driving Stress Monitoring MIMO Radar System

Supervised Machine Learning-Assisted Driving Stress Monitoring MIMO Radar System

Characterization Technique for a Doppler Radar Occupancy Sensor

In-Vehicle Multiple Passengers Respiration Monitoring Based on Surface-Circuit Metasurface Tags Using Time-Division FMCW Radar

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