Data Science Story: Snips – Underground Location Tracking

One of our ambassadors, Dr Joseph Dureau of Snips, discusses a study that his organisation worked on related to utilising location in places where location tracking becomes very difficult e.g. on a metro. Using context awareness, a field within artificial intelligence, user locations can be tracked. A summary of the story can be found below with the full version found at

Putting new pressure on your old barometer
There is more to location tracking than triggering avalanches of advertising when you walk by a store. Location is a key element to a specific field of artificial intelligence called context awareness. It can be used to determine where you are, where you’re heading to, and your current needs. For example, it is only natural that one expects a person to behave differently in a church and in a disco. Context awareness is about giving devices enough intelligence to tell the difference between a sacrament and a party, and adjust accordingly.

Traditional activity detection
Traditional activity detection with mobile phones relies on location and accelerometer data. Location estimates can be computed in reference to nearby radio towers, wifi signals, or directly through GPS. Beyond location, the accelerometer can be used to understand what a person is doing. Periodic movements at given frequencies are specific of certain activities: walking, running, biking, etc. When a person goes for a jog, her phone can reconstruct her trajectory and identify when she runs or walks based on the peaks of the accelerometer signal in the frequency domain.

The problem of inferring a user’s activity from location and accelerometer data has been thoroughly studied. As mentioned, it is easy to identify when a user is walking, running or biking. When it comes to detecting automotive transportation, like driving a car, or taking a train or a bus, the accelerometer is of little use. You would think that the accelerations of the vehicle would be significant enough to leave a trace in your phone’s accelerometer data, but in reality the signal is very weak. In this case, location can take over and be used to compute features like average speed, maximal speed, frequency and location of stops. The latter can then be contrasted to nearby roads, junctions, rails or bus stops. Here again, very good performances can be reached.

A blind spot of traditional activity detection

All of this is pretty cool and straightforward, but leaves out an important blind spot: what happens when you’re underground? You may want your phone to understand which route you’ve taken, to warn you of any perturbation in real time, or to let you know when to get off the metro. In this perspective, both your location and accelerometer sensors are worthless.

When you’re taking the metro, unless specific infrastructure has been put in place to provide connectivity for your phone, you get very poor to no location data. It is the case in the vast majority of Parisian metro lines, for example. As mentioned for the other automotive transportation modes, your accelerometer doesn’t bring much information about what’s happening down there. Tough luck. But wait, more and more phones are equipped with a sensor that is often overlooked: the barometer.

Read the full story at