Reality Mining


Fig. 7 A Hidden Markov Model conditioned on time for situation identification. The model was designed to be able to incorporate many additional observation vectors such as friends nearby, traveling, sleeping and talking on the phone.

(Eagle & Pentland, 2005)

Fig. 4 A ‘low-entropy’ (H = 30.9) subject’s daily distribution of home/work transitions and Bluetooth devices encounters during the month of January. The top figure shows the most likely location of the subject: 'Work, Home, Elsewhere, and No Signal.' While the subject’s state sporadically jumps to 'No Signal,' the other states occur with very regular frequency. This is confirmed by the Bluetooth encounters plotted below representing the structured working schedule of the ‘low-entropy’ subject.

(Eagle & Pentland, 2005)

Eagle, N. and Pentland, A. (2005) ‘Reality mining: sensing complex social systems’, Personal and Ubiquitous Computing, 10(4), pp. 255–268. doi: 10.1007/s00779-005-0046-3. [link]