For most of modern history, detecting movement meant pointing a camera at something or relying on a motion detector that sensed heat (passive infrared or PIR). Using radio waves for detection isn’t new—radar systems have done it since the 1940s—but what is new is how accessible that technology has become. Advances in semiconductor design have made it possible to build complex radio systems at a fraction of their former cost, allowing radar-like sensing to move from military and industrial use into everyday life. By combining communication and sensing in the same wireless systems—a concept known as Joint Communication and Sensing (JCAS)—the industry has unlocked new consumer-ready technologies. Wi-Fi Sensing is one of the first to make this leap, transforming the Wi-Fi networks we already rely on into cost-effective tools for detecting presence, motion, and activity—reshaping how we think about safety, health, and connectivity.
Two of the leading technologies driving this shift are millimeter wave (mmWave) and Wi-Fi Sensing. They are often mentioned together, yet they function in distinct ways that lead to very different strengths. Grasping their differences is essential for industries, product teams, and consumers alike to understand where each technology truly fits in the future of tech and eldercare alike.
What is mmWave?
“mmWave” stands for millimeter wave, a portion of the radio frequency spectrum between roughly 30 and 300 gigahertz. The name comes from its wavelength—ranging from 1 to 10 millimeters—but it’s not the wavelength itself that determines its sensing precision. What makes mmWave powerful is the bandwidth and antenna configuration available at these higher frequencies. In simple terms, there are two main types of resolution that define a radar system’s performance. Range resolution—the ability to distinguish two objects that are close together in distance—depends on bandwidth. The wider the bandwidth, the finer the detail the system can resolve. Angular resolution, on the other hand, is about distinguishing two objects at the same distance but at slightly different angles. Wider bandwidth improves range resolution, while antenna gain and directional beamforming (the ability to focus signals in specific directions rather than broadcasting them broadly) enhance angular resolution.
That’s where mmWave shines. The bands it uses—like 60 GHz (unlicensed) and 77–81 GHz (used in automotive radar)—offer exceptionally wide bandwidths, enabling high range resolution. And because antennas get physically smaller at higher frequencies, engineers can build dense antenna arrays that support narrow, high-gain beams for excellent angular resolution. Together, these traits allow mmWave systems to detect presence, measure distance, and even track motion with remarkable precision. Police radar guns have operated in the Ka-band (33–36 GHz) for decades, and satellites have used similar frequencies since the 1990s. What changed in the early 2000s was the availability of low-cost silicon fabrication capable of supporting high-frequency circuits, which opened the door to wider commercial use.
In sensing applications, mmWave works much like radar: a transmitter sends out a radio signal that reflects off objects and returns to the receiver. By analyzing how those reflected signals vary in timing, strength, or phase, the system can infer position, motion, and velocity. This allows for fine-grained capabilities—such as monitoring the rise and fall of a person’s chest as they breathe, detecting gestures, or enabling precise collision avoidance in vehicles. However, mmWave’s high frequencies also bring challenges. Every radio wave moves at the speed of light, but higher-frequency signals fade faster. Around 60 GHz, for example, oxygen and water vapor in the air absorb much more of the signal, limiting how far it can travel. These factors limit range and make mmWave best suited to short-range, line-of-sight environments. Despite that, mmWave radar has become a staple in automotive systems (collision avoidance, blind-spot monitoring, parking assistance), industrial robotics, and even consumer devices like Google’s “Soli” sensor. In short, mmWave excels where precision and responsiveness matter most, particularly in controlled spaces where its physical limitations can be managed and its strengths fully leveraged.
What is Wi-Fi Sensing?
Wi-Fi Sensing takes a fundamentally different approach from mmWave radar. Instead of transmitting and listening to its own reflected signals, Wi-Fi Sensing uses the radio activity that already exists all around us—ordinary Wi-Fi networks. Operating in the 2.4, 5, and 6 gigahertz bands, it analyzes how Wi-Fi signals behave as they travel between devices. When a person moves through a space, their body slightly alters those signals—creating reflections, scattering, and variations in how the waves bounce through the environment (a phenomenon known as multipath). At the core of this process is Channel State Information (CSI), a detailed measurement of how each Wi-Fi signal travels between a transmitter and receiver. By examining small fluctuations in CSI over time, algorithms can infer whether there’s motion in a room or how much activity is happening.
A key distinction between mmWave radar and Wi-Fi Sensing lies in their topology (how their systems are set up). Most radar systems use what’s called a monostatic configuration—the same device acts as both transmitter and receiver, sending out a signal and listening for its echo. Wi-Fi Sensing, by contrast, operates in a bistatic or multistatic topology: the transmitter and receiver are physically separate devices. In a typical home or office network, multiple access points and connected devices interact in this way, forming a rich web of signal paths that can be analyzed for movement and presence. The growing number of Wi-Fi devices in homes—ranging from routers and mesh nodes to smart speakers and connected appliances—continues to make this kind of sensing more powerful and reliable.
Wi-Fi Sensing’s greatest strength is that it builds on infrastructure people already have. No specialized radar chips or antennas are required—sensing can often be enabled through software on existing routers and access points. Because Wi-Fi signals naturally penetrate walls, a single system can provide whole-home coverage without needing line of sight. It’s also inherently privacy-preserving: unlike cameras, Wi-Fi Sensing doesn’t capture images or personally identifying data, only changes in signal patterns. There are, however, trade-offs. Wi-Fi Sensing generally provides lower spatial resolution than mmWave, meaning it’s ideal for detecting motion, and activity trends, but not for high-precision tasks like gesture recognition or fine object tracking. Its accuracy can also vary with network quality and the physical placement of access points—strong, well-distributed coverage produces better results. Even so, Wi-Fi Sensing is already demonstrating its versatility. In home security, it can detect movement without relying on cameras or motion sensors. In healthcare and eldercare, it can identify sleep disruptions, monitor routines, or alert caregivers to unusual activity. And in smart buildings, it can enable energy-efficient systems that respond automatically to occupancy. In short, Wi-Fi Sensing transforms everyday connectivity into environmental awareness—using the same signals that bring us online to make our spaces safer, smarter, and more responsive.
Where Each Technology Fits—and What Comes Next
When you boil it down, the difference is clear. mmWave technology is well suited for high-precision RADAR applications—capable of detecting micro-movements and capturing fine-grained spatial detail. However, it operates best at short range and in line-of-sight conditions, making it ideal for targeted use cases like automotive safety, robotics, and gesture recognition. Wi-Fi Sensing, by contrast, is built for scale. It transforms everyday Wi-Fi networks into a cost-effective, privacy-preserving sensing platform that delivers broad coverage and actionable insights across homes, workplaces, and public spaces.
Looking ahead, these technologies aren’t in competition so much as complementary. The long-term vision is hybrid environments where mmWave delivers precision in specific zones, while Wi-Fi Sensing provides continuous, whole-space awareness. That future, however, is still some distance away. The IEEE 802.11bf standard is currently defining new capabilities to enhance Wi-Fi Sensing. Its timeline overlaps with Wi-Fi 8, which is expected to combine ultra-reliable connectivity with native sensing features—making spatial awareness a built-in function of everyday networks. At the same time, another standards effort known as IEEE 802.11bq is focused on redefining how the 60 GHz millimeter-wave band is used. While this high-frequency spectrum can enable extremely precise, short-range measurements, the 802.11bq work is currently centered on communication rather than sensing—using 60 GHz primarily for high-speed data transfer in short-range applications like VR headsets and wireless entertainment systems. Once these communication goals are established, future iterations could eventually revisit sensing at 60 GHz, but that remains further down the road. In the near term, the continued rollout of Wi-Fi 7—with faster speeds, lower latency, and multi-link operation—is already strengthening Wi-Fi Sensing as a practical, scalable layer of intelligence.