Your body disturbs Wi-Fi signals as you move through them. New research takes advantage of the effect to recognize the presence of people.
What’s new: Yang Liu and colleagues at Syracuse University detected people in a room with a Wi-Fi router by analyzing the signal.
Key insight: Radio waves interfere with one another, creating high-frequency noise that masks other kinds of perturbations. The researchers removed these components, making it easier to identify lower-frequency disturbances caused by human motion.
How it works: A Wi-Fi router comprises many antennas transmitting and receiving radio waves on different frequency bands called subcarriers. The researchers measured the signal strength and phase received by each antenna over a fixed time period to plot what is known as channel state information (CSI). The sequence of CSI images — cubes corresponding to measurements of the transmitting antenna, receiving antenna, and subcarrier — feeds a network that predicts whether someone is moving in the room
- The researchers extracted CSI components that represent signal strength and phase.
- The pre-processing algorithm transformed these components to the frequency domain to capture change from time step to time step.
- They fed the strength and phase information into a dual-input convolutional neural network, basically a pair of AlexNets operating in parallel. The model’s fully connected layers merged the features extracted by each CNN to render a prediction.
Results: The authors’ method slightly outperformed conventional motion detectors based on infrared beams. The dual CNN detected a wider physical area. Although the training data included only people walking, it spotted minimal motion — say, typing on a keyboard while seated — almost twice as well as conventional detectors. (The success rate was only around 5 percent, but for much of the time, typing was the only motion to detect.) It may miss someone if they’re still, but combining multiple predictions over time improved accuracy unless someone was still for minutes on end.
Yes, but: The training and test data come from the same room, so the model’s practicality is limited for now. It would be onerous to retrain for each new room we might use it in.
Why it matters: It’s hard to imagine extracting this kind of information from radio waves without deep learning. Still, the preprocessing step was crucial. Neural networks can be distracted by input features that don’t correlate with the output. Radio interference doesn’t correlate with human motion, so the CNN would have required a huge amount of data to learn to detect people through the noise. Removing it at the outset made training far more efficient.
We’re thinking: It is well known that powerful actions can create a disturbance in the Force. But anyone can create a disturbance in the Wi-Fi.