Author(s): Pentland A, Lazer D, Brewer D, Heibeck T
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Abstract We live our lives in digital networks. We wake up in the morning, check our e-mail, make a quick phone call, commute to work, buy lunch. Many of these transactions leave digital breadcrumbs--tiny records of our daily experiences. Reality mining, which pulls together these crumbs using statistical analysis and machine learning methods, offers an increasingly comprehensive picture of our lives, both individually and collectively, with the potential of transforming our understanding of ourselves, our organizations, and our society in a fashion that was barely conceivable just a few years ago. It is for this reason that reality mining was recently identified by Technology Review as one of "10 emerging technologies that could change the world". Many everyday devices provide the raw database upon which reality mining builds; sensors in mobile phones, cars, security cameras, RFID ('smart card') readers, and others, all allow for the measurement of human physical and social activity. Computational models based on such data have the potential to dramatically transform the arenas of both individual and community health. Reality mining can provide new opportunities with respect to diagnosis, patient and treatment monitoring, health services planning, surveillance of disease and risk factors, and public health investigation and disease control. Currently, the single most important source of reality mining data is the ubiquitous mobile phone. Every time a person uses a mobile phone, a few bits of information are left behind. The phone pings the nearest mobile-phone towers, revealing its location. The mobile phone service provider records the duration of the call and the number dialed. In the near future, mobile phones and other technologies will collect even more information about their users, recording everything from their physical activity to their conversational cadences. While such data pose a potential threat to individual privacy, they also offer great potential value both to individuals and communities. With the aid of data-mining algorithms, these data could shed light on individual patterns of behavior and even on the well-being of communities, creating new ways to improve public health and medicine. To illustrate, consider two examples of how reality mining may benefit individual health care. By taking advantage of special sensors in mobile phones, such as the microphone or the accelerometers built into newer devices such as Apple's iPhone, important diagnostic data can be captured. Clinical pilot data demonstrate that it may be possible to diagnose depression from the way a person talks--a depressed person tends to speak more slowly, a change that speech analysis software on a phone might recognize more readily than friends or family do. Similarly, monitoring a phone's motion sensors can also reveal small changes in gait, which could be an early indicator of ailments such as Parkinson's disease. Within the next few years reality mining will become more common, thanks in part to the proliferation and increasing sophistication of mobile phones. Many handheld devices now have the processing power of low-end desktop computers, and they can also collect more varied data, due to components such as GPS chips that track location. The Chief Technology Officer of EMC, a large digital storage company, estimates that this sort of personal sensor data will balloon from 10\% of all stored information to 90\% within the next decade. While the promise of reality mining is great, the idea of collecting so much personal information naturally raises many questions about privacy. It is crucial that behavior-logging technology not be forced on anyone. But legal statutes are lagging behind data collection capabilities, making it particularly important to begin discussing how the technology will and should be used. Therefore, an additional focus of this chapter will be the development of a legal and ethical framework concerning the data used by reality mining techniques.
This article was published in Stud Health Technol Inform
and referenced in Journal of Data Mining in Genomics & Proteomics