Slow-Time mmWave Radar Vibrometry for Drowsiness Detection

Ciattaglia, Gianluca; Spinsante, Susanna; Gambi, Ennio

doi:10.1109/metroautomotive50197.2021.9502850

Cited by 7 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a recent application of a 122 GHz FMCW RADAR reached an angular beamwidth of 2 degrees at -3 dB of attenuation [51]. Another application of interest, where we can observe the use of TeraHertz RADAR with operating frequency at 300 GHz (0.3 THz), is in the context of driver drowsiness detection [276]. Sensor fusion: The development of techniques to merge information from multiple RADARs at different locations certainly improves estimation performance related to vital signs, as demonstrated in [45].…”

Section: A Development In Sensor Technologymentioning

confidence: 99%

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

View full text Add to dashboard Cite

In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

show abstract

Section: A Development In Sensor Technologymentioning

confidence: 99%

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Driver attention monitoring studies have focused on radarbased vital sign recognition [24], [25], [26], monitoring of driver head movements [27], eye blinking [28], [29], [30], [31], fatigue, concentration and drowsiness [32], [33], [34], [35], and health indicators, such as blood pressure [36] and blood glucose levels [37]. The presence of body movements and respiration effects both the measurement of heart rate as well as that of eye blinking frequency and duration.…”

Section: B Automotive-internal Perceptionmentioning

confidence: 99%

Sensing and Machine Learning for Automotive Perception: A Review

et al. 2023

View full text Add to dashboard Cite

Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and ranging (LiDAR) used commonly for perception, along with the associated data processing techniques. Critical aspects of perception are considered, such as architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches, given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.

show abstract

“…Most papers used HR and BR monitoring. An abrupt decrease in BR and HR are a sign of a drowsy driver [82]- [86]. On the other hand, head motion and eye blink frequency also can be monitored to detect sleepy drivers [96]- [98], [102]- [107].…”

Section: Occupant Status Monitoringmentioning

confidence: 99%

“…However, making a decision after BR and HR estimation can be more beneficial. One of the benefits of in-cabin radars is to detect drowsy drivers by identifying low BR or apnea [83], [86], [107]. According to statistics, almost 30% of fatal car accidents involving deaths are caused by drowsy drivers [83].…”

Section: Introductionmentioning

confidence: 99%

In-Vehicle Monitoring by Radar: A Review

Gharamohammadi,

Khajepour,

Shaker

2023

IEEE Sensors J.

View full text Add to dashboard Cite

The proliferation of vehicles and subsequent increase in traffic accidents has led to a heightened focus on driving safety. As a result, various researchers have been examining ways to enhance driving safety in daily life by implementing smart car technology. In-vehicle sensing utilizing radar technology has emerged as a leading method for monitoring the driver's health, emotions, and attention, owing to its numerous advantages over traditional sensors, including the ability to detect subjects through non-metallic surfaces and the inherent privacy-preserving mechanisms. In recent years, invehicle sensing through radar has undergone significant advancements. This paper aims to provide a comprehensive survey of the applications, system level design, and signal processing of in-vehicle sensing through radar. The published works in this field are categorized into three main groups: occupancy detection, gesture recognition and occupant status monitoring. The paper will discuss the highlighted works and their respective advantages and limitations in terms of applications.

show abstract

Slow-Time mmWave Radar Vibrometry for Drowsiness Detection

Cited by 7 publications

References 21 publications

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Sensing and Machine Learning for Automotive Perception: A Review

In-Vehicle Monitoring by Radar: A Review

Contact Info

Product

Resources

About