Wireless movement-tracking system promises health, behavioral insights
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 individual it senses, the system creates two “filtering masks,” which are small circles around the individual that basically filter out all signals outside the circle – locking in the individual’s trajectory and height as they move.
Combining all this information — height, build, and movement — the network associates specific RF reflections with specific individuals. To train their machine learning model, the researchers had individuals temporarily wear low-powered accelerometer sensors, which can be used to label the reflected radio signals with their respective identities.
When deployed in training, Marko first generates users’ tracklets, as it does in practice. Then, an algorithm correlates certain acceleration features with motion features. When users walk, for instance, the acceleration oscillates with steps, but becomes a flat line when they stop.
The algorithm finds the best match between the acceleration data and tracklet, and labels that tracklet with the use’’s identity. In doing so, Marko learns which reflected signals correlate to specific identities. In home deployments, say the researchers, Marko was able to tag the identities of individuals in new homes with between 85% and 95% accuracy.
The researchers hope health care facilities will use the system to passively monitor, for example, how patients interact with family and caregivers, and whether patients receive medications on time. The system may also replace questionnaires and diaries currently used by psychologists or behavioral scientists to capture data on their study subjects’ family dynamics, daily schedules, or sleeping patterns, among other behaviors.
Such traditional recording methods can be inaccurate, contain bias, and aren’t well-suited for long-term studies, where people may have to recall what they did days or weeks ago. While some researchers have started equipping people with wearable sensors to monitor movement and biometrics, patients often forget to wear or charge them.
Monitoring cameras are another alternative, but are more invasive and require someone to watch and manually record all necessary information. Marko, on the other hand, say the researchers, automatically tags behavioral patterns — such as motion, sleep, and interaction — to specific areas, days, and times.
Looking ahead, the researchers plan to combine Marko with prior work they’ve done on inferring breathing and heart rate from the surrounding radio signals. Marko will then be used to associate those biometrics with the corresponding individuals. It could also track people’s walking speeds, which is a good indicator of functional health in elderly patients.
For more, see “Enabling Identification and Behavioral Sensing in Homes using Radio Reflections.”
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