How a Contactless WiFi Heart Rate Monitor Works

Key Takeaways

• WiFi signals reflect off the body's chest wall vibrations, encoding heartbeat and breathing motion into radio signals.
• The Doppler effect and phase shift in reflected waves enable detection of millimetre-scale chest wall movement.
• Signal processing algorithms separate the larger breathing signal from smaller cardiac oscillations using frequency analysis.
• Standard WiFi routers provide the radio source; no specialised wearable device is required for monitoring.
• Contactless monitoring enables passive physiological observation in care environments where wearing sensors is impractical.

Every heartbeat moves the body. The chest wall vibrates with each cardiac cycle, producing mechanical displacement well below a millimetre in amplitude. That tiny, rhythmic motion is what a contactless WiFi heart rate monitor is designed to detect.

No electrode. No wearable. Just radio waves.

Radio Waves Reflect Off the Body

WiFi signals are electromagnetic radiation in the radio frequency range, typically at 2.4 GHz or 5 GHz. Like visible light, they reflect off surfaces. When a WiFi signal propagates through a room, it bounces off furniture, walls, and the people inside. Each reflection carries physical information about the surface it struck.

The chest wall is never truly still. Breathing is the dominant motion, expanding and contracting the chest by roughly 5 to 10 millimetres with each breath. The heartbeat produces smaller vibrations layered beneath that breathing motion. A receiver designed to analyse returning signals with sufficient precision can detect both, and then separate them. That is the core insight that makes contactless physiological sensing possible.

Phase Shift and the Doppler Effect Encode Movement

Two properties of reflected radio waves make heart rate extraction possible.

The first is the Doppler effect. Christian Doppler described this phenomenon in 1842: when a wave reflects off a moving surface, the frequency of the return signal shifts relative to the velocity of that surface. A chest wall moving rhythmically with each heartbeat creates a periodic Doppler signature in the reflected WiFi signal, even when the motion is far too small to see.

The second is phase shift. Radio waves have a repeating cycle. When the path length between transmitter, body, and receiver changes by even a fraction of a millimetre, the timing of that cycle shifts measurably. The IEEE 802.11 standard, which defines the WiFi protocol, structures transmissions across dozens of sub-carrier frequencies simultaneously using orthogonal frequency-division multiplexing. This gives receivers phase and amplitude readings across all those sub-carriers at once, producing what engineers call Channel State Information, or CSI. That data encodes fine-grained movement in the reflected signal at high resolution.

Together, these two effects give a continuous record of chest wall motion. The signal is noisy, and extracting heart rate from it requires careful processing. But the physical information is there.

Separating Heart Rate from Breathing

Breathing and the heartbeat both appear in the reflected signal, and breathing is far louder. The respiratory cycle produces large, slow movements, typically in the 0.1 to 0.5 Hz frequency range for a resting adult. The cardiac signal sits higher, usually somewhere between 0.8 and 2.5 Hz, and its physical amplitude is much smaller.

Signal processing separates them in stages. First, the algorithm identifies and removes the breathing component. What remains is dominated by the cardiac oscillation. Second, it looks for periodicity in that residual, a repeating pattern at the frequency corresponding to heart rate.

The challenge is that real environments add noise. Multipath reflections arrive at the receiver from different directions after bouncing off multiple surfaces. Small ambient movements, a person shifting in a chair, a ceiling fan rotating, all add low-level interference. Modern systems use machine learning to handle this. Models trained on collected data learn to distinguish genuine cardiac oscillations from artefacts, making the extraction more robust across varied conditions.

The Hardware Required Is Minimal

No specialised transmitter is necessary. A standard WiFi access point works as the radio source. A receiver, running software that samples the wireless channel at high speed, captures the returning signals continuously. Sampling rates of hundreds of measurements per second are sufficient to resolve the cardiac frequency band cleanly.

The receiver feeds those measurements into a processing pipeline. Filtering separates the respiratory and cardiac components. From the cardiac waveform, instantaneous heart rate is estimated. The entire chain, from router to heart rate estimate, relies on the same wireless infrastructure already present in most buildings.

At Neuralase, we are developing technology that synthesizes heart rate and respiratory signals using ambient radio signals from conventional WiFi routers. Our work on WiFi heart rate monitoring focuses on analysing how reflected radio waves change in phase and Doppler shift as they bounce off the body, enabling physiological measurement without any wearable device.

Why This Matters

Traditional heart rate monitoring requires skin contact. Chest straps press electrodes against the skin. Smartwatches use optical sensors held against the wrist. Both depend on a person actively wearing a device. In many care environments, that is simply not practical, whether because of the patient's age, condition, or the difficulty of maintaining sensor contact over long periods.

Contactless monitoring changes the picture. A person can be observed simply by being in a room. That difference matters enormously for elderly adults, hospital patients, or infants, where continuous passive observation would otherwise require constant physical intervention. The monitoring becomes part of the environment rather than something placed on the body.

The same physical principles extend to other sensing applications. Our work on detecting movement through walls demonstrates that radio signals retain meaningful physical information even through solid barriers. Heart rate monitoring is one specific application within a broader capability for radio-based body sensing.

Current Limitations

WiFi heart rate monitoring performs best when the subject is relatively still. Significant body movement produces Doppler and phase changes large enough to overwhelm the cardiac signal. Multiple people in the same space complicate signal separation. Distance, room geometry, and wall materials all affect signal quality and resolution.

These are active areas of improvement. Better algorithms and more robust machine learning models continue to extend the conditions under which contactless monitoring can reliably operate. The volumetric sensing work we are exploring shows how far radio-based body tracking can reach when the underlying physics is pushed to its limits.

The heartbeat is silent. It is never invisible to the right receiver.