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Many of us already record the places we go and things we do by using our smartphone to diligently snap photos and videos, and to update social-media accounts. A company called ARO is building technology that automatically collects a more comprehensive, automatic record of your life.

ARO is behind an app called Saga that automatically records every place that a person goes. Now ARO’s engineers are testing ways to use the barometer, cameras, and microphones in a device, along with its location sensors, to figure out what someone is up to and where. That approach should debut in the Saga app in late summer or early fall.

The current version of Saga, available for Apple and Android phones, automatically logs the places a person visits; it can also collect data on daily activity from other services, including the exercise-tracking apps FitBit and RunKeeper, and can pull in updates from social-media accounts like Facebook, Instagram, and Twitter. Once the app has been running on a phone for a little while, it produces infographics about the user’s life; for example, charting the variation in times when he or she leaves for work in the morning.

Software running on ARO’s servers creates and maintains a model of each user’s typical movements. Those models power Saga’s life-summarizing features and help the app to track a person all day without requiring sensors to be always on, which would burn too much battery life.

“If I know that you’re going to be sitting at work for nine hours, we can power down our collection policy to draw as little power as possible,” says Andy Hickl, CEO of ARO. Saga will wake up and check a person’s location if, for example, a phone’s accelerometer suggests he or she is on the move; and there may be confirmation from other clues, such as the mix of Wi-Fi networks in range of the phone. Hickl says that Saga typically consumes around 1 percent of a device’s battery, significantly less than many popular apps for e-mail, mapping, or social networking.

That consumption is low enough, says Hickl, that Saga can afford to ramp up the information it collects by accessing additional phone sensors. He says that occasionally sampling data from a phone’s barometer, cameras, and microphones will enable Saga to log details like when a person walked into a conference room for a meeting or visited Starbucks, either alone or with company.

The Android version of Saga recently began using the barometer present in many smartphones to distinguish locations close to one another. “Pressure changes can be used to better distinguish similar places,” says Ian Clifton, who leads development of the Android version of ARO. “That might be first floor versus third floor in the same building, but also inside a vehicle versus outside it, even in the same physical space.”

ARO is internally testing versions of Saga that sample light and sound from a person’s environment. Clifton says that using a phone’s microphone to collect short acoustic fingerprints of different places can be a valuable additional signal of location and allow inferences about what a person is doing. “Sometimes we’re not sure if you’re in Starbucks or the bar next door,” says Clifton. “With acoustic fingerprints, even if the [location] sensor readings are similar, we can distinguish that.”

Occasionally sampling the light around a phone using its camera provides another kind of extra signal of a person’s activity. “If you go from ambient light to natural light, that would say to us your context has changed,” says Hickl. With the help of this information it should be possible for Saga to learn the difference between, say, the different areas of an office.

Ultimately, sampling light, sound, and pressure data will enable Saga’s machine learning models to fill in more details of a user’s life, says Hickl. “[When] I go home today and spend 12 hours there, to Saga that looks like a wall of nothing,” he says, noting that Saga could use sound or light cues to infer when during that time at home he was, say, watching TV, playing with his kids, or eating dinner.

Andrew Campbell, who leads research into smartphone sensing at Dartmouth College, says that adding more detailed, automatic life-logging features is crucial for Saga or any similar app to have a widespread impact. “Automatic sensing relieves the user of the burden of inputting lots of data,” he says. “Automatic and continuous sensing apps that minimize user interaction are likely to win out.”

Campbell says that automatic logging, coupled with machine learning, should allow apps to learn more about users’ health and welfare, too. He recently started analyzing data from a trial in which 60 students used a life-logging app he developed, called Biorhythm. It uses various data collection tricks, including listening for nearby voices to determine when a student is in a conversation. “We can see many interesting patterns related to class performance, personality, stress, sociability, and health,” says Campbell. “This could translate into any workplace performance situation, such as a startup, hospital, large company, or the home.”

Campbell’s project may shape how he runs his courses, but it doesn’t have to make money. ARO, funded by Microsoft cofounder Paul Allen, ultimately needs to make life logging pay. Hickl says he has already begun to rent out some of ARO’s technology to other companies that want to be able to identify their users’ location or activities. Aggregate data from Saga users should also be valuable, he says.

“Now we’re getting a critical mass of users in some areas and we’re able to do some trend-spotting,” he says. “The U.S. national soccer team was in Seattle, and we were able to see where activity was heating up around the city.” Hickl says the data from that event could help city authorities or businesses plan for future soccer events in Seattle or elsewhere. He adds that Saga could provide similar insights into many other otherwise invisible patterns of daily life.

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Tagged: Computing, Communications, Web, Mobile, quantified self, mobile computing

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