There is a rapidly growing demand for compact, energy-efficient radar sensors for remote bio-signal monitoring in the medical field.
For general patients who are not critically ill, monitoring multiple vital signs (e.g., respiration + heart rate) has been shown to prevent up to 75% of deaths and adverse events.[1]
A particularly important aspect in patient and geriatric care is monitoring vital signs such as respiratory rate and heart rate.
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Testing equipment such as ECGs and pulse oximeters require electrodes attached directly to the skin via wires.
In addition, the cable is long and requires frequent charging, which may limit the patient's freedom of movement.
However, the advent of improved edge-type radar has enabled a new, non-contact method of biosignal monitoring.
Accordingly, it is being easily used in home appliances and IoT products based on its advantages such as continuous bio-signal monitoring through non-contact measurement and early detection of abnormal symptoms.
This article explains how radar sensors can improve patient care and introduces Infineon’s XENSIV™ 60 GHz radar sensor as a leading radar sensor technology.
■ Understanding radar sensors
Radar sensors emit RF signals in the form of electromagnetic waves, and as they move through space at the speed of light, they detect even the slightest movement through signals reflected by people and objects in space, enabling comprehensive monitoring activities based on this.
For this purpose, FMCW (Frequency Modulated Continuous Wave) radar can be used to measure and detect the subtle movements of the human body, and can measure accurate distance and speed by analyzing the frequency change while using frequency modulation.
In the following, we will examine the exact mechanism of biosignal monitoring using radar sensors.
■ Measuring biosignals using FMCW radar sensors
The radar sensor can detect movement of the thoracic cavity during breathing by detecting minute differences in radar reflection signals caused by changes in thoracic cavity movement during the breathing process.
This technology is called 'radar-based breathing detection'. Let's take a look at the basic operating mechanism.
FMCW radar continuously emits RF signals over time while modulating them by increasing and decreasing their frequency, thereby repeatedly creating a waveform of a constant frequency band.
This modulated radar signal is emitted towards the target (the chest cavity) and part of it is reflected and returns to the radar receiver. This reflected signal, called the echo, contains useful information about the distance and speed of the target.
FMCW radars emit a linearly modulated waveform and compare it to the reflected signal waveform.
During this process, a 'beat' or intermediate frequency (IF) is created, which is correlated to the distance to the object being detected.
In other words, distance measurement accuracy can be significantly improved by precisely measuring the intermediate frequency (IF), which is the frequency difference between the transmitted and received signals.
In other words, the accuracy of the measured distance is improved by the precision of the intermediate frequency (IF) measurement.
■ Benefits of non-contact biometric monitoring
○ Improving patient safety
Traditional wired monitoring systems use electrocardiogram and pulse oximetry sensors to measure heart rate and breathing rate.
Additionally, because the electrodes attached to the string are attached to the skin, it can cause skin irritation. This can be especially true for sensitive patients. It can also cause anxiety in those with epilepsy or psychiatric disorders.
Radar sensors, on the other hand, can measure vital signs non-contactly by using built-in antennas to track the position and velocity of the chest wall.
The antenna tracks breathing and heartbeat and provides accurate vital signs. It is a non-contact measurement method, so it does not restrict or restrain the patient.
Therefore, it does not cause discomfort or skin irritation to the patient.
○ Patient convenience
Polysomnography under sufficient sleep duration has limitations such as high cost and first night effect (FNE) for traditional obstructive sleep apnea symptoms [9].
In particular, falls on the subject of observation could potentially cause malfunctions.
However, this radar sensor detects targets by its own reflected signal, independent of any external signals, and can penetrate non-metallic materials, so it can be used unobtrusively behind the outer cover of the final product.
○ Accuracy and reliability
A traditional ECG attaches electrodes to the body to measure heart rate. These electrodes can cause discomfort to patients and disrupt a person's natural breathing patterns, which can lead to inaccuracies in the biometric measurements being measured.
In contrast, non-contact vital sign monitoring can improve the accuracy of vital sign measurement because the patient does not need to be conscious of the measurement, and thus the possibility of respiratory fluctuations is reduced.
In addition, conventional ECGs apply pressure to the skin by attaching electrodes to it, which can change the electrical properties and cause inaccuracies in the measured signal.
However, radar sensors measure without contact and do not put any pressure on the human body. Accordingly, the detected signal approximates the original biological function state, enabling accurate measurement of biosignals.
○ Scalability
By combining radar sensors with AI and sensor fusion, healthcare environments such as bio-signal measurement can be built in hospitals, nursing homes, and homes.
This means that radar can be flexibly installed in everyday home appliances and electronic/electrical products such as night lights, smartphones, tablets, and smart alarm clocks, and can be used as a smart home device to provide healthcare functions for patients and the elderly, as well as for security functions.
○ Small size and anonymity
The radar's characteristics are robustness, simplicity of configuration and installation, and product miniaturization.
The radar is robust to temperature changes and can operate reliably under a variety of environmental changes and harsh conditions, such as changes in light brightness and humidity and the influence of dust.
It can also be hidden behind a non-conductive material cover, making it invisible to the end user.
Another advantage is that radar does not collect any personal or identifying data.
Therefore, unlike sensors or microphones, it can prevent privacy invasion and leakage of personal information.
Radar also allows for miniaturization of sensor products. As a representative example, Infineon’s XENSIV™ 60GHz radar sensor BGT60UTR11AIP is only 16㎟ in size.
It is possible to design a compact device because it takes up less than 2㎠ of space on the board even when all the necessary components are added.
■ Applications that can utilize non-contact biometric monitoring
○ Hospital
Hospitals can use non-contact vital sign monitoring to improve patient care. It can be usefully utilized in places such as ICU, operating rooms, general wards, emergency rooms, and sleep centers.
By utilizing radar sensors, it is possible to continuously monitor a patient's vital signs in a non-contact manner, for example in an ICU.
In particular, it allows anesthesiologists and surgical teams to closely monitor the patient's physiological responses in real time in the operating room.
In addition, non-contact vital sign monitoring in general wards eliminates the need for people to directly measure vital signs on a regular basis.
This allows patients to sleep more comfortably at night and obtain more accurate and reliable biosignal data.
○ Assisted living facilities and nursing homes
Assisted living facilities and nursing homes can use real-time monitoring to intervene immediately when needed.
It can automatically alert medical staff when there is a significant change from the patient's baseline vital signs.
Additionally, radar sensors can detect residents' movement patterns, gait, and even changes in posture, allowing for rapid intervention.
Doctors can contact patients, examine them, prescribe medications and even send nurses to visit patients without the need for a face-to-face visit.
■ Evolution of radar sensor technology
The functionality of radar sensors is increasing with the use of machine learning, data fusion, and variable adaptive filters.
It excels at learning repetitive patterns through machine learning algorithms and removing noise from complex data.
Detection accuracy can be improved by effectively separating target biosignals from unwanted noise using machine learning algorithms.
A study conducted by SCALE in Singapore found that applying machine learning to a mm-Wave FMCW radar-based non-contact biosignal monitoring system can easily filter out external noise and improve the accuracy of the biomarker information [8].
Enhanced monitoring capabilities can be added by fusing information from multiple radar sensors (data fusion) [3].
By combining radar sensors with optical sensors or accelerometers, more comprehensive and accurate data monitoring can be achieved.
This allows healthcare providers to better treat patients by providing a more holistic view of an individual's biomarkers.
To avoid clutter and improve signal quality, variable adaptive filters can be used, and the two most commonly used algorithms are the Least Mean Square (LMS) algorithm and the Recursive Least Square (RLS) algorithm.
These algorithms can be used to suppress noise and interference in radar data [5].
CFAR (Constant False Alarm Rate) detection and Adaptive BeamforminFilter techniques such as g can also be used. These techniques can further reduce the background noise at the threshold level [6].
Additionally, if the radar sensor has multiple channels, it can measure biosignals of multiple people in parallel.
This capability could open up a wider range of applications for radar sensors beyond hospitals and nursing homes. For example, it could be used in automobiles to improve the safety of drivers and their replacement drivers.
Multi-channel mm-wave radar can continuously monitor drivers/switch drivers for early detection of signs of a heart attack or other cardiopulmonary abnormalities.
How to use these techniques will depend on the needs of a particular radar system, its signal characteristics, and the biosignals it requires.
■ Tasks and Considerations
The accuracy and reliability of radar sensor measurements can be affected by a variety of factors. Let's look at two important challenges for radar sensors and how to solve them.
○ Amplitude of human body movement
Rapid body movements, such as those caused by physical activity or seizures, can cause distortion in radar signals, making it difficult to extract accurate data. For example, when walking, it is difficult to distinguish between the small chest wall movements caused by breathing and the heartbeat caused by large leg and arm movements.
To overcome these problems, improved motion compensation techniques based on algorithms such as adaptive filters, Kalman filters, and particle filters can be used [6].
These techniques allow for more accurate extraction of biosignals by calculating and compensating for the effects of motion.
○ Periodic calibration/inspection
Radar sensors can produce erroneous biometric readings over time due to aging, environmental conditions, and component degradation.
Therefore, to maintain accuracy, calibration should be performed periodically and necessary adjustments should be made to ensure that readings remain within acceptable limits.
Additionally, the following methods may help improve accuracy and reliability:
- Place the radar sensor in a stable and suitable location to minimize external interference and maximize signal quality.
- Protects millimeter wave radar sensors and antennas from external environmental influences such as rain, sunlight, and wind by using radome (radar dome) technology.
- Compare the measurement results with existing measurement techniques to evaluate reliability and determine if there are areas for improvement.
Achieve robust measurements by learning patterns and correlations from large datasets using machine learning and deep learning algorithms. Can be.
■ Enhanced radar sensing using Infineon’s XENSIV™ 60GHz radar sensor
Infineon's millimeter wave radar portfolio offers FMCW and Doppler radar sensors for a variety of IoT and automotive applications.
The BGT60TR13C and
BGT60UTR11AIP 60GHz radar sensors are particularly suitable for low-cost biosignal measurements (heart rate and respiration rate).
="https://www.infineon.com/cms/en/product/sensor/radar-sensors/radar-sensors-for-iot/60ghz-radar/bgt60tr13c/?utm_source=wewolver&utm_medium=tech_publications&utm_campaign=202307_glob_en_pss_PSS.RFS.P .Radar&utm_term=Radar%20BGT60TR13C&utm_content=article_web" target="_blank">

▲Figure 2: XENSIV™ 60GHz BGT60TR13C
▲Figure 3: Infineon’s JinXENSIV™ 60GHz BGT60UTR11AIP, a radar sensor
Let's take a look at the product features of these sensors for remote bio-signal monitoring systems.
○ Unique package design
These radar sensors indirectly enable bio-signal monitoring through their discrete design.
The BGT60TR13C radar sensor is an L-shaped AIP, while the BGT60UTR11AIP radar sensor is a U-slot AIP. Both designs allow for efficient antenna integration in a compact form factor. Additionally, these sensors are designed in small packages, saving PCB area and simplifying the overall design.
※ Note: AIP (Antenna in Package) technology refers to an antenna packaging solution that embeds one or more antennas into an IC package.
▲Table 1: Differences between these XENSIV™ 60GHz radar sensors
○ Built-in FSM (Finite-state machine)
One important challenge for non-contact biosignal measurement is to enable network self-healing mechanisms when patients exhibit anomalous body movements.
The BGT60TR13C and BGT60UTR11AIP solve this problem by using an autonomous connectivity recovery algorithm based on a finite-state machine (FSM).
These radar sensors can autonomously generate chirps, acquire data, and store them in memory by embedding FSMs.
When an abnormal node occurs, it is replaced with the closest node with low importance to smoothly restore network connectivity [7].
;
By using this optimized power switching mechanism, power consumption (avg. xx mW) is minimized while controlling the duty cycle.
Additional power savings can be achieved through DC duty cycling.
○ Use of wideband frequencies
These sensors have incredibly wide bandwidths (up to 5.6 GHz) and high range resolution (∼3 cm), allowing them to detect microscopic movements with millimeter accuracy at distances of up to 15 meters.
This ability is important for early detection of heart attacks and monitoring the physiological status of critically ill or intensive care patients.
Your healthcare provider can use this detailed information to tailor your treatment plan and prescribe medications that are right for you. This helps to alleviate serious conditions.
○ Ease of use
With a high SNR (signal-to-noise ratio), radar sensors can detect the movement of objects at long distances, even in complex environments that interfere with detection by other devices.
Both the BGT60TR13C and BGT60UTR11AIP are designed with high SNR to detect subtle chest wall movements.
These radar sensors can detect vital signs through clothing, bedding, and other non-metallic obstacles, and they do not require additional complex devices or require patients to undress, making them useful for patient monitoring in a variety of settings, such as hospitals and nursing homes.
■ Conclusion
Radar technology enables continuous, non-invasive monitoring of biosignals without the need for skin contact.
Monitoring vital signs using radar sensors is particularly useful for seniors or individuals who want to monitor themselves as it is easy to use, saves energy, and provides accurate biometric data.
m_term=Radar+nutshell+&utm_content=article_web" target="_blank">Infineon's 60GHz radar sensor enables accurate biosignal measurements by overcoming challenges such as rapid human movement and external signal interference.
More information about Infineon's advanced radar sensor, XENSIV™ 60GHz BGT60UTR11AIP can be found at this
link .
※ References
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[6] Wolff DI (fh) C. False alarm - radartutorial [Internet]. Dipl.-Ing. (FH) Christian Wolff. [cited 2023 May 27]. Available from: https://www.radartutorial.eu/01.basics/False%20Alarm%20Rate.en.html
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