BYOD – Bring your own Data. Self-Tracking for Medical Practice and Research

BYOD – Bring your own Data. Self-Tracking for Medical Practice and Research

„Facebook would never change their advertsing relying on a sample size as small as we do medical research on.“ (David Wilbanks) People want to learn about themselves and get their lives soundly supported by data. Parents record the height of their children. When we feel ill, we measure our temperature. And many people own a bathroom scales. But without context, data is little meaningful. Thus we try to compare owr measurements with those of other people. Data that we track just for us alone Self-tracking has been trending for years. Fitness tracker like Fitbit count our steps, training apps like Runtustic deliver to us analysis and benchmark us with others. Since 2008, a movement has been around that has put self-tracking into its center: The Quantified Self. However it is not just self-optimizer and fitness junkies who measure themselves. Essential drive to self-tracking originated from self-caring chronically ill. Data for the physician, for family members, and for nursing staff In the US like in many countries lacking strong public health-care, it becomes increasingly common to bring self-measured data to the physician. With many examinations this saves significant consts and speeds up the treatment. With Quantified Self, many people have been […]

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There is no privacy in mobile

There is no privacy in mobile

Our phones register in radio cells to route the calls to the phone network. When we move around, we occasionally leave one cell and enter another. So our movements over leave a trace through the cells we have been passing the course of the day. Yves-Alexandre de Montjoye and his co-authors from MIT explored, how many observations we need, to identify a specific user. Based on actual data provided by telephone companies, they calculated, that just four observations are sufficient to identify 95% of all mobile users. We need just so little evidence because people’s moving patterns are surprisingly unique, just like our fingerprints, these are more or less reliable identifiers. Location When we analyze the raw data, that we collect through our mobile sensor framework ‚explore‘ we found several other fingerprint-like traces, that all of us continuously drop by using our smartphones. Obviously we can reproduce de Monjoye’s experiment with much more granular resolution when we use the phone’s own location tracking data instead of the rather coarse grid of the cells. GPS and mobile positioning spot us with high precision. Wifi Inside buildings we have the Wifis in reception. Each Wifi has a unique identifier, the BSSID and […]

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