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Mood sensing tech uses GPS to detect depression

29 Feb 2016  | Patrick Mannion

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Researchers from the University of Birmingham and University College London are looking at the potential of GPS to generate data to unobtrusively monitor moods and detect, or even predict, depression. If designers are able to apply the research findings into smartphones or even GPS-enabled personal health monitors, they could help initiate an intervention, and potentially save lives.

The part about being unobtrusive is important: It's easy to query any health monitor wearers or smartphone users as to their state of mind when they're in a good mood, but as depression creeps in, the likelihood of getting a response decreases rapidly. This makes it difficult to gather sufficient data to make an accurate determination, so the monitoring has to be through another means that doesn't require direct user interaction.

That is where GPS and movement tracking comes in, as another symptom of depression is reduced mobility activity. Of course, that inactivity could be for any reason: sickness, twisted ankle, last weeks of intense work before a new product launches, or simply a case of bad weather and bingeing on Game of Thrones.

Or, maybe, just maybe, that person has retreated entirely from the world and is spiraling down past the point of no return. A dark place, where they risk doing harm either to themselves or someone else. It's worth its weight in lives saved to figure out the difference between bingeing and spiraling, and then predicting the latter.

Activity and depression during THIST

Figure 1: The team worked to find the relationship between activity and depression during THIST, but also the relationship between that movement and the PHQ score result obtained on February 28.

Of course, figuring out the difference between a twisted ankle and a depressive spiral is tricky. That's why the research is so detailed and complex, involving spatial statistics, GPS traces and movement patterns. It also required the full cooperation of participants in answering questions from the widely used 'PHQ-8' depression test that quantifies depressive states.

An Android app called MoodTraces was developed to do this with 28 users, and it's still available if you'd like to try it yourself. Some interesting aspects are that they focused on conserving smartphone energy, as GPS-based location data gathering is power consuming. To help with that, the app employs three chief mechanisms to minimise battery consumption: 1) It samples only when the user moves from one place to another (assumes application device has a motion trigger of some sort); 2) It can use either true GPS or position data from the network provider, with the latter consuming less energy. It can also switch back and forth if one signal is lost; and 3) MoodTraces subscribes to a 'passive location provider' so that it passively receives location updates when other applications or services request them, independently of the states of the two state machines.

We won't get into the math and statistics here, but you can do that by viewing the paper the team presented last September (2015) at the ACM International Joint Conference on Pervasive and Ubiquitous Computing in Osaka, Japan. It's titled: Trajectories of Depression: Unobtrusive Monitoring of Depressive States by means of Smartphone Mobility Traces Analysis. (L Canzian, M. Musolesi.)

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