​Why Wireless Sensing Is Edge AI’s Next Act

​Rajesh Subramaniam is Founder and CEO of embedUR systems.

Edge AI has become nearly synonymous with computer vision, with cameras detecting people, objects and faces on-device. But vision is only the first sensory modality to reach the edge, and in many settings, it’s the wrong one. Cameras don’t work as well in darkness and can exceed the power budget of battery-operated devices.​

Wireless sensing offers another, more powerful approach. Using radio signals and devices already in place, it can detect movement, human presence and changes in the environment without requiring any additional sensors. This capability could turn billions of existing devices into ambient sensors, opening up new possibilities for edge AI.​

The Signal Is Already In The Room

The radio chips in routers, hubs and light switches already communicate over Wi-Fi, Bluetooth, cellular, UWB and Zigbee networks. The same wireless signals used for communication can also be used to sense what’s happening in the surrounding environment, with no camera and no wearable required. When a body moves through a room, it disturbs the signal paths between those devices, and software interprets the change in pattern. This makes it possible to sense human presence and activity without installing any additional infrastructure. Today, wireless sensing is mostly used for basic motion and presence detection, with commercial examples including Comcast WiFi Motion and Philips Hue MotionAware.​

What’s new is that edge AI models running on low-power microcontrollers can now interpret these signals in real time. These models are trained on a substantial amount of data showing how signal patterns change when somebody walks or when a signal hits furniture or a wall and bounces back. Edge AI adds significant value because compute and inference run locally, with no cloud round-trip and low latency. The system also keeps working during internet outages.​