The system uses enhanced routers and machine learning computation to characterize and represent the movements of people, pets and objects. This has potential applications in assisted living, security, smart buildings and industrial applications.
Celeno, founded in 2005, has been a pioneer of elastic MIMO with up to 8T8R and the Doppler imaging technology sits on top of one of Celeno’s standard Wi-Fi connectivity chips typically with multiple distributed antennas
The system then recognizes the Doppler signature generated by moving objects and feeds it to machine learning classifiers to locate, track, count and analyse behaviour and events, while increasing the overall contextual interpretation of a situation.
The technology is capable of quantifying objects: and tracking multiple objects. It can also label objects and differentiate between pets, children and adults. Beyond that the technology can characterize or classify common body movements, such as sitting, standing, bending over, fall detection, it can recognize large-scale gestures and even detecting breathing and measure respiration rate.
Leveraging standard 5GHz and 6GHz Wi-Fi bands, it can work through walls, not requiring line of sight and not dependent on lighting conditions. In addition, it is not dependent on any Wi-Fi clients, wearables of any kind and does not invade privacy.
“We are excited to debut Wi-Fi Doppler Imaging technology which shows great promise to enable a new breadth of applications over Wi-Fi infrastructure. The network is becoming the sensor,” said Gilad Rozen, founder and CEO of Celeno. “This is another milestone in Celeno’s tradition of bringing Wi-Fi innovations.”
“BT research teams are exploring new technologies within the smart home and assisted living markets and we are interested in the potential of Wi-Fi Doppler Imaging technology to enrich service offerings based on the Wi-Fi network,” said Darren Lewis of BT, in a statement issued by Celeno.