Human Vital Signs Detection Based on Millimeter Wave Radar and Digital Filtering

Liu, Weichen; Zhang, Sutong; Yang, Junxuan; Wang, Mingjiang

doi:10.1109/icipca59209.2023.10257847

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Additionally, these video monitoring systems lose detection capability when mice are obscured by enrichment materials such as nesting pads within their housing environment. Recent advancements in mm-wave radar technology have led to its widespread use for vital sign monitoring, particularly in routine health monitoring applications [16][17][18][19] . The unique ability of mm-waves to penetrate clothing and body hair allows radar signals to capture movements associated with heat transfer and lung ventilation.…”

Section: Introductionmentioning

confidence: 99%

Contactless metabolism estimation of small animals using high-frequency millimeter-wave radar

Ono,

Ishikawa,

Wataki

et al. 2024

Preprint

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SUMMARYAnimals flexibly adapt to internal and external environmental changes by utilizing energy produced from oxygen as fuel. By non-invasively monitoring an animal’s oxygen consumption, it becomes possible to understand an individual’s metabolic state. Calorimeters are known for directly measuring oxygen consumption but come with the issue of high initial costs. Despite the development of non-invasive techniques for measuring vital signs—including respiration rate (RR), heart rate (HR), and body temperature (Tb)—as indicators of metabolism, conventional methods encounter difficulties in estimating oxygen consumption rate (VO2). In this study, we developed a system that estimates the oxygen consumption of small animals using signals obtained from millimeter wave (mm-wave) radar technology processed through machine learning. By identifying frequency bands within the mm-wave signals contributing to VO2, our system is capable of predicting oxygen consumption several minutes in advance. Our system enables contactless, low-cost and multiplexed measurements of oxygen consumption, presenting a significant advancement in the field.

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Section: Introductionmentioning

confidence: 99%

Contactless metabolism estimation of small animals using high-frequency millimeter-wave radar

Ono,

Ishikawa,

Wataki

et al. 2024

Preprint

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Enhancing Road Safety: Fast and Accurate Noncontact Driver HRV Detection Based on Huber–Kalman and Autocorrelation Algorithms

Luo,

Yang,

et al. 2024

Biomimetics

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Enhancing road safety by monitoring a driver’s physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver’s seat. This article mainly describes how advanced signal processing methods, including the Huber–Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system’s effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments.

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Human Vital Signs Detection Based on Millimeter Wave Radar and Digital Filtering

Cited by 2 publications

References 9 publications

Contactless metabolism estimation of small animals using high-frequency millimeter-wave radar

Contactless metabolism estimation of small animals using high-frequency millimeter-wave radar

Enhancing Road Safety: Fast and Accurate Noncontact Driver HRV Detection Based on Huber–Kalman and Autocorrelation Algorithms

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