Scientists from Massachusetts Institute of Technology (MIT) have devised a wireless movement-tracking system that uses radio frequency signals -- flowing around us and bouncing off our bodies -- and helps collect health and behavioural data.
The system called "Marko" transmits a low-power radio-frequency (RF) signal into the environment. The signal returns to the system with certain changes if it has bounced off a moving human.
Novel algorithms then analyse those changed reflections and associate them with specific individuals. The system then traces each individual's movement around a digital floor plan.
Matching these movement patterns with other data can provide insights about how people interact with each other and the environment, say researchers.
"These are interesting bits we discovered through data. We live in a sea of wireless signals and the way we move and walk around changes these reflections," said study first author Chen-Yu Hsu, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.
"We developed the system that listens to those reflections... to better understand people's behaviour and health," he added in a paper presented this week at the Conference on Human Factors in Computing Systems in Glasgow, Scotland.
The researchers describe the system and its real-world use in six locations -- two assisted living facilities, three apartments inhabited by couples, and one townhouse with four residents.
The case studies demonstrated the system's ability to distinguish individuals based solely on wireless signals -- and revealed some useful behavioural patterns.
This is how the system works.
When deployed in a home, "Marko" shoots out an RF signal. When the signal rebounds, it creates a type of heat map cut into vertical and horizontal "frames," which indicates where people are in a three-dimensional (3D) space.
People appear as bright blobs on the map.
Vertical frames capture the person's height and build, while horizontal frames determine their general location.
As individuals walk, the system analyzes the RF frames -- about 30 per second -- to generate short trajectories, called tracklets.
A Machine Learning (ML) model commonly used for image processing uses those tracklets to separate reflections by certain individuals.
Combining all the information -- height, build, and movement -- the network associates specific RF reflections with specific individuals.
The sensors never have to be charged, and, after training, the individuals don't need to wear them again.
In home deployments, "Marko" was able to tag the identities of individuals in new homes with between 85 and 95 percent accuracy, said researchers.
The research was led by Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the MIT Center for Wireless Networks and Mobile Computing (Wireless@MIT).