Each breath causes roughly a 2 cm expansion in chest circumference, which corresponds to approximately 5-10 mm of outward chest wall movement during normal breathing.

If you imagine this motion for a moment, with light reflecting from the surface of the chest, these reflected waves carry information about the tiny movements of the body.

Radio waves behave in the same fundamental way. In physics, radio waves are part of the electromagnetic spectrum, meaning they are a form of light with longer wavelengths. Because of this, radio waves can also reflect from the body and capture extremely small movements such as breathing and the subtle vibrations caused by the heartbeat.

Modern radio sensing techniques can detect movements well below a millimetre, which makes it possible to measure breathing rate and heart rate without any physical contact.

Neuralase is developing technology that synthesizes heart-rate and respiratory signals using ambient radio signals from conventional Wi-Fi routers. By analysing how these radio waves reflect and change phase when they bounce off the body, it becomes possible to infer vital physiological signals without requiring a wearable device.

This capability is important because continuous health monitoring traditionally relies on wearables such as smartwatches, rings, chest straps, or medical sensors. Contactless monitoring allows health signals to be measured passively in homes, hospitals, or care environments.

Technologies like this could enable:

• Passive health monitoring in homes: rooms that automatically track breathing, heart rate, and sleep quality without requiring devices to be worn.
• Early detection of medical issues: subtle changes in breathing patterns or heart rhythm could provide early warnings for conditions such as sleep apnea, cardiac irregularities, or respiratory disease. Using big data analytics, you can opt-in for AI modelling of future health issues.
• Monitoring for elderly or vulnerable patients: caregivers could be alerted if someone stops breathing normally or experiences distress.
• Smart environments: buildings that can understand occupant health states and adjust lighting, climate, or alerts accordingly.
• Clinical diagnostics: future systems may combine RF sensing with AI models to extract deeper physiological signals such as stress levels, motion disorders, or recovery after surgery.

In essence, Neuralase’s approach demonstrates how radio-frequency sensing and signal processing can transform everyday infrastructure, such as Wi-Fi routers, into powerful health monitoring tools that operate continuously and unobtrusively.