A person鈥檚 bodily outline can be worked out from the pattern of Wi-Fi signals moving across a room, which could help monitor older people at risk of hurting themselves if they fall or enable the detection of home intruders.
Indoor cameras and radar-based technologies can be used to detect a person鈥檚 body shape or what position they are in, but these can introduce a privacy risk, such as a camera feed being hacked, or involve specialised hardware and installation.
Now, and his colleagues at Carnegie Mellon University in Pittsburgh, Pennsylvania, have used indoor Wi-Fi to produce images of the shape of people鈥檚 bodies.
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鈥淧eople might be a little freaked out now, in the sense that internet service providers might locate what people are doing at home but, no, we are still not there,鈥� says De la Torre. 鈥淭he only thing that this paper shows is that, in a very constrained setting鈥� [with] three receivers of Wi-Fi signal, there is enough signal there for the fine-grained detection of human body parts.鈥�
To detect the body parts and where they are, De la Torre and his team placed three Wi-Fi transmitters and three receivers in different positions across a small room. When a person walks through the room, the presence of their body changes the pattern of the signal, which is recorded by the receivers.
The researchers then used a machine-learning algorithm, which had been trained on the relationship between Wi-Fi signals and visual images of a person鈥檚 body shape, to decode these recorded Wi-Fi signals.
鈥淚t鈥檚 like throwing out memory foam fibres into the whole room, and when we retract those fibres into a thread, our algorithm disentangles the fibres in the thread so that we can recover what the fibre has recorded,鈥� says team member .
While the algorithm currently only works for the specific room and set-up it has been trained on, De la Torre and his team hope that it can be generalised to cope with more complex set-ups and real-world scenarios, where interference from other Wi-Fi networks might be present.
If it can be developed in this way, it could be used for cheap indoor monitoring to determine whether people in retirement homes have had a fall, for example, because it only requires two common Wi-Fi routers with three antennas each to replicate the number of antennas used in the study, or even to interact with computers, such as in an augmented reality set-up, says De la Torre.
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