Wireless movement-tracking system promises health, behavioral insights

May 09, 2019 //By Rich Pell
Wireless movement-tracking system promises health, behavioral insights
Researchers at MIT (Cambridge, MA) have developed a system that uses radio-frequency (RF) signal reflections from human bodies to wirelessly monitor people's movement inside their homes to provide insight for behavioral research or medical care.

The system, called Marko, transmits a low-power RF signal into an environment and monitors changes if the signal has bounced off a moving human. Algorithms then analyze 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, say the researchers, can provide insights about how people interact with each other and the environment.

In a paper on the system, the researchers described 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, say the researchers, demonstrated the system's ability to distinguish individuals based solely on wireless signals, and revealed some useful behavioral patterns that shows that the system can provide a new, passive way to track functional health profiles of patients at home.

"These are interesting bits we discovered through data," says first author Chen-Yu Hsu, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). "We live in a sea of wireless signals, and the way we move and walk around changes these reflections. We developed the system that listens to those reflections ... to better understand people's behavior and health."

Once deployed in a home, the system works by first transmitting an RF signal. When the signal reflections return from the environment, it creates a type of heat map cut into vertical and horizontal "frames," which indicates where people are - appearing as bright blobs on the map - in a three-dimensional space.

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 convolutional neural network — a machine-learning model commonly used for image processing — uses those tracklets to separate reflections by certain individuals. For each


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