MIT researchers have built a system that takes a step toward fully automated smart homes, by identifying occupants even when they’re not carrying mobile devices. (Credit: Chelsea Turner, MIT)
MIT researchers have built a system that takes a step toward fully automated smart homes, by identifying occupants even when they’re not carrying mobile devices. (Credit: Chelsea Turner, MIT)

Developing automated systems that track occupants and self-adapt to their preferences is a major next step for the future of smart homes. When you walk into a room, for instance, a system could set to your preferred temperature. Or when you sit on the couch, a system could instantly flick the television to your favorite channel.

But enabling a home system to recognize occupants as they move around the house is a complex problem. Recently, systems have been built that localize humans by measuring the reflections of wireless signals off their bodies. But these systems can’t identify the individuals. Other systems can identify people, but only if they’re always carrying their mobile devices. Both systems also rely on tracking signals that could be weak or get blocked by various structures.

MIT researchers have built a system that takes a step toward fully automated smart home by identifying occupants, even when they’re not carrying mobile devices. The system, called Duet, uses reflected wireless signals to localize individuals. But it also incorporates algorithms that ping nearby mobile devices to predict the individuals’ identities, based on who last used the device and their predicted movement trajectory. It also uses logic to figure out who’s who, even in signal-denied areas.

Experiments done in a two-bedroom apartment with four people and an office with nine people, over two weeks, showed the system can identify individuals with 96 percent and 94 percent accuracy, respectively, including when people weren’t carrying their smartphones or were in blocked areas.

Duet could potentially be used to recognize intruders or ensure visitors don’t enter private areas of your home. In addition, the system could capture behavioral-analytics insights for health care applications. Someone suffering from depression, for instance, might move around more or less, depending on how they’re feeling on any given day. Such information, collected over time, could be valuable for monitoring and treatment.

Duet is a wireless sensor installed on a wall, it’s about a foot and a half squared and incorporates a floor map with annotated areas, such as the bedroom, kitchen, bed, and living room couch. It also collects identification tags from the occupants’ phones.

The system builds upon a device-based localization system that tracks individuals within tens of centimeters based on wireless signal reflections from their devices. It does so by using a central node to calculate the time it takes the signals to hit a person’s device and travel back. In experiments, the system was able to pinpoint where people were in a two-bedroom apartment and in a café. However, that system relied on people carrying mobile devices. So, the researchers combined their device-based localization with a device-free tracking system, called WiTrack, which localizes people by measuring the reflections of wireless signals off their bodies.

Duet locates a smartphone and correlates its movement with individual movement captured by the device-free localization. If both are moving in tightly correlated trajectories, the system pairs the device with the individual and, therefore, knows the identity of that individual. To ensure Duet knows someone’s identity when they’re away from their device, the researchers designed the system to capture the power profile of the signal received from the phone when it’s used. That profile changes, depending on the orientation of the signal, and that change be mapped to an individual’s trajectory to identify them. For example, when a phone is used and then put down, the system will capture the initial power profile. Then it will estimate how the power profile would look if it were still being carried along a path by a nearby moving individual. The closer the changing power profile correlates to the moving individual’s path, the more likely it is that individual owns the phone.

One final issue is that structures such as bathroom tiles, television screens, mirrors, and various metal equipment can block signals. To compensate for that, the researchers incorporated probabilistic algorithms to apply logical reasoning to localization. To do so, they designed the system to recognize entrance and exit boundaries of specific spaces in the home, such as doors to each room, the bedside, and the side of a couch. At any moment, the system will recognize the most likely identity for each individual in each boundary. It then infers who is who by process of elimination.

Suppose an apartment has two occupants: Alisha and Betsy. Duet sees Alisha and Betsy walk into the living room, by pairing their smartphone motion with their movement trajectories. Both then leave their phones on a nearby coffee table to charge — Betsy goes into the bedroom to nap; Alisha stays on the couch to watch television. Duet infers that Betsy has entered the bed boundary and didn’t exit, so must be on the bed. After a while, Alisha and Betsy move into, say, the kitchen — and the signal drops. Duet reasons that two people are in the kitchen, but it doesn’t know their identities. When Betsy returns to the living room and picks up her phone, however, the system automatically re-tags the individual as Betsy. By process of elimination, the other person still in the kitchen is Alisha.

Next, the researchers aim for long-term deployments of Duet in more spaces and to provide high-level analytic services for applications such as health monitoring and responsive smart homes.

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