Health and wellness monitoring is a primary way to manage personal health and awareness for a healthy lifestyle. Many wearable activity tracking devices, smart watches, and smartphone applications collect and analyze data from bodily sources. Other types of devices used to monitor vital signs can help the wearer manage health issues but can limit the wearer’s mobility or cause discomfort if worn continuously. These restrictions have prompted a growing interest in developing noncontact, wireless devices to detect vital signs, especially for people who are unconscious, sleeping, very young, elderly, or infirm. This article explains why 24 GHz Doppler radar sensors are useful for such devices and highlights some of the features suitable for meeting these applications.

Doppler Radar Technology Essentials

A Doppler radar sensor that is compliant with the CFR 15.245 standard is a suitable option for data collection in health and wellness monitoring.

Historically, the 23~25 GHz frequency range has long been utilized for applications like land mobile communication, maritime mobile communication, space/satellite communication, radio astronomy, broadcasting, amateur radio/satellite, and standard frequency and time signal.

In the United States, the unlicensed radio band usable for such radar applications is defined as 24.075 GHz ~ 24.175 GHz (CFR 15.245) and 24.000 GHz ~ 24.250 GHz (CFR 15.249), and the field strength of the fundamental signal is limited to 2,500 mV/m and 250 mV/m, respectively. A Doppler radar sensor that is compliant with the CFR 15.245 standard is a suitable option for data collection in health and wellness monitoring.

The Doppler effect is a known physical theory, commonly noticeable by the apparent, variable sound effect from a traveling ambulance or police car’s siren. A listener hears a higher siren pitch while the vehicle approaches and a lower siren pitch while the car recedes away from the listener. This high-to-low tone transition is very clear.

The 24 GHz Doppler radar utilizes the same process and transforms these sounds to a radio wave. A Doppler radar sensor uses continuous wave modulation (CW mode) to generate a constant frequency radio signal that is transmitted through a TX antenna. It then detects the frequency difference by receiving the reflected radio signal through the RX antenna for the speed of the object (see Figure 1).

Fig. 1 - Doppler effect basics.

Gaining 1 Hz of Doppler effect from a transmitted frequency to reflection frequency requires half the radio wave length of movement per second. For example, with a 24.100 GHz radar frequency, one wavelength is roughly equal to 1.24 cm (half inch), and thus, 1 Hz of Doppler effect is equivalent to 0.62 cm (quarter inch)/sec movement of the object.

Using the same frequency condition, the Olympic-class marathon running speed of 21 Km/h will generate around 942 Hz of Doppler effect, and the natural human walking speed of 4 Km/h will generate 179 Hz of Doppler effect (see Figure 2).

Fig. 2 - Gaining 1 Hz of Doppler effect from transmit frequency to reflection frequency requires half the radio wavelength of movement per second. Therefore, the Olympic-class marathon running speed of 21 Km/h will generate around 942 Hz of Doppler effect.

Sensing Human Vital Signs

A Doppler radar sensor can detect minute movements of the human body. For example, even though a heartbeat occurs inside the human body, its movement is observable at the body’s surface through the change in blood pressure. Although these movements are just a few millimeters per heartbeat at most, it is an involuntary movement that translates to a higher Doppler effect, which provides a signal capable of being analyzed by an external circuit and host system software.

Similarly, respiration is detectable at the body’s surface with movements larger than a heartbeat. Respiration is primarily a slow movement and can be detected using the Doppler effect, but it may require utilizing another parametric character of the reflecting signal. For example, the slowly changing amplitude of the synchronized inhale and exhale motion may be an alternative source to use in parallel. Figure 3 illustrates the trend of the reflected signal strength from the inhale and exhale movement. This is subject to the distance from the Doppler radar sensor to the target, which may not be significant.

Fig. 3 - Trend of reflected signal strength from the inhale and exhale movement.

Figure 4 shows a sample image of detection results using a 24 GHz Doppler radar sensor with embedded algorithm to identify actual heart and respiration rate from the Doppler effect. In this example, the inhalation and exhalation are intentionally exaggerated for illustrative purposes. The red top waveform plot indicates the detected heart rate signal, and the blue bottom waveform plot indicates the detected respiration rate signal. The overlapping green and magenta color plot in the middle indicates the radar sensor output raw signals I and Q.

Fig. 4 - Detection results using a 24 GHz Doppler radar sensor with embedded algorithm to identify heart (red) and respiration (blue) rates from the Doppler effect.

Detecting heartbeat and respiration rates requires some level of signal analytic capability, and the actual implementation may require further optimization. That said, this example illustrates limitless possibilities for using Doppler radar to detect many other human vital signs, beyond heartbeat and respiration rates, to expand noncontact wellness and healthcare monitoring applications.

Optimizing 24 GHz Doppler Radar Sensor Design

The above experiments were conducted using the Fujitsu 24 GHz Doppler radar sensor with selectable, three channels of frequency. This multiple channel capability prevents nearby interference from signal overlap and allows each channel to be used for a different purpose.

The sensor also supports output signals ranging from 1 Hz to 1 MHz with very low I/F (intermediate noise) to allow detection of very slow-to-high-speed moving objects. Although 1 MHz of Doppler effect is faster than rocket speed and not observable in real-world applications, it is useful for detecting nearby interference and allows the system to change to a different channel to escape misdetection before it happens.

To assist designers who may not be familiar with high frequency circuits, Fujitsu located pin head terminals on the back of the product to help facilitate integration.

Beyond Heartbeat and Respiration Detection

Although the 24 MHz Doppler radar sensor excels at detecting minute, human vital signs, it’s also capable of detecting larger motions, making it suitable for other applications. The growing elderly population is creating a need for more noncontact healthcare monitoring and diagnosis devices for use in care facilities and for those wishing to age in their homes. To address this trend, fall detection devices/systems are another application which could be supported by 24 MHz Doppler radar sensors. In addition, such devices or systems could also be modified for monitoring worker safety or machinery failures in factories.

The Doppler radar sensor has the ability to detect a moving object that switches directions. In a test environment (see Figure 5), the Doppler radar sensor detects both the speed of the object and the direction in which it was moving (50 ms per horizontal axis; swift human hand motion, roughly 1 ft/sec). This ability to detect a combination of speed and direction may be applied to a mechanical control monitoring system.

Fig. 5 - A Doppler radar sensor is capable of detecting both the speed of the object and the direction in which it is moving, as shown here — 50 ms per horizontal axis, swift human hand motion, roughly 1 ft/sec.

Figure 6 shows a sample from the respiration raw waveform depicted earlier in Figure 4. Note that there is roughly a 0.2 sec cycle waveform, which translates to about 5 Hz, and about a 1.25 in./sec speed of inhale and exhale motion that changes direction in the middle of the respiration action.

Fig. 6 - A raw waveform sample of the inhalation and exhalation motion that changes direction in the middle as detected by a 24 GHz Doppler radar sensor.

These examples illustrate that analyzing a low-frequency audio-class signal is the key to utilizing a 24 GHz Doppler radar sensor. Many of today’s embedded CPUs have the power and capabilities of CPUs or FPUs, allowing engineers to collect signals and analyze them at the embedded sensor module level.


Using 24 GHz Doppler radar sensors enables great potential for developing wireless and noncontact sensing devices for healthcare and wellness applications, primarily in vital sign monitoring. The Doppler method accurately detects minute motions of the body’s surface and utilizes it for wirelessly sensing a heart rate, respiration rate, and others. Larger movements can also be detected, as well as speed and direction, making it an excellent choice for fall detection systems.

Moreover, the 24 GHz waveform easily penetrates clothing, curtains, glass, and most wooden structures, and unlike other types of detection methods such as infrared, laser scan and ultrasound, Doppler radar sensors are affected less by environmental factors like weather, sound, dirt, temperature and illuminance, making it a very robust and reliable method for wireless detection. Engineers using a 24 GHz Doppler radar sensor that supports multiple channels and has a sufficient output signal range (~1Hz to 1 MHz) with very low I/F noise can easily design interference tolerant capability from nearby electronic equipment while achieving a highly sensitive detection system.

By default, the detection signal does not capture or reveal any personal information, other than a physical radio wave reflection. This addresses patient privacy and security issues, which is an increasing concern in a connected society.

When combined with data analytic capabilities through additional AI-based software and services, 24 GHz Doppler radar sensors, like the FWM7RAZ01 from Fujitsu Components, are a promising technology positioned to serve the healthcare market as well as others that might require movement detection.

This article was written by Akio Nezu, Product Marketing Manager, Wireless Modules, at Fujitsu Components America, Inc., San Jose, CA. For more information, visit here .

Medical Design Briefs Magazine

This article first appeared in the December, 2019 issue of Medical Design Briefs Magazine.

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