Nowadays, most of our daily living entails constant interactions with different utilities and devices. Every time you turn on your kettle, take a shower, send a text or buy something with your card, information about this interaction is collected by different companies, such as your energy, water, telephone provider or bank. These companies may then use that data for different purposes, for example to recalculate your tariffs or to suggest further services.
The goal of the Unobtrusive Behaviour Monitoring project is to combine all the information that we are continuously generating through daily interactions with technology and to feed it back in a constructive and organised way so we can see different patterns in our behaviour. This type of analysis could be particularly insightful for people who suffer from mood disorders, who could use it to monitor their condition, provide early warning signs and facilitate the development of self-awareness of their triggers. Overall, the ultimate goal is to enable patients to take control of their condition. In other words, we want to allow people to benefit from the data they themselves are constantly generating and help them to use it, when they want, to monitor their mood. And, more importantly, we want to do this in a way that respects their privacy and keeps their data safe.
From a machine learning perspective, this project is interesting because it would require the development of algorithms that could work locally in order to protect people’s privacy (i.e. analysis should be done at each person’s home instead of in a centralised database), while also work with sparse or incomplete data (i.e. in the case that one person decided to turn off one of the sources of information).
This project is an on-going collaboration between the Faculty of Engineering, the Institute of Mental Health, and the Faculty of Science. Phase I was funded by MindTech and its main goal was to explore the literature in the area and to engage with experts. We explored ethics, technical, legal and social aspects of four different sources of data (utility and device usage, car usage and driving style, banking and card transactions, and mobile phone usage) and carried out different engagement activities with patients groups and other researchers in the area.
These activities, particularly our engagement with patients’ groups, were extremely useful. Talking with people with mood disorders who are interested in self-monitoring their condition, helped us identify potential directions and issues from a usability perspective, such as the need for total customisation of sensor (i.e. the possibility of turning off particular sources of data, instead of all of them at the same time) – or the possibility of adding additional sensors like sleep or activity sensors through wearables like Fit Bit.
For Phase II, we are hoping that we can carry out a pilot study in which we collect some data and develop machine learning algorithms able to cope with distributed information that might be scarce or incomplete.
Mercedes Torres Torres, Transitional Assistant Professor, Horizon Digital Economy Research
