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Reality Mining

A short informative presentation on reality mining and its future developments.

Christopher Robinson

on 4 May 2010

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Transcript of Reality Mining

Harvard Business Review named it "Breakthrough
idea of 2009" REALITYMINING Reality Mining is the collection and analysis of machine-sensed environmental data pertaining to human social behavior, with the goal of identifying predictable patterns of behavior.

It was declared to be one of the "10 technologies most likely to change the way we live" by Technology Review Magazine By: Susan Wagner
Greg Lambert
Christopher Robinson Applications of Reality Mining Traffic Data Epidemiology Research ~ Real time traffic data ~ How to people adapt to different traffic patterns ~ Data bought by Garmin and Tom Tom ~ Government research for attack on Golden Gate bridge Social Network Mapping Behavior Patterns Data Storage ~ How will Reality Mining affect data storage in the coming years? ~ Where will all the data be stored? Privacy Issues Live Sensor Modeling Future of Reality Mining Reality Mining and Mental Health Sociometrics Personal Behavioral Monitoring Questions? Thank You Even though they are treatable, mental diseases rank among the top health problems worldwide in terms of cost to society. Major depression, for example, is the leading cause of disability in established market economies (RAND Corporation, 2004). Reality mining technology might assist in the early detection of psychiatric disorders such as depression, attention deficit hyperactivity disorder (ADHD), bipolar disorder, and agoraphobia. Many signs and symptoms of these types of psychiatric disorders explicitly or implicitly relate to an individual’s physical movement and activity patterns and communicative behavior Data streams from reality mining allow direct, continuous, and long term assessment of these behavior patterns. Accelerometers in mobile phones might reveal fidgeting, pacing, abrupt or frenetic motions, and other small physical
movements. Location tracking functions reveal individuals’ spatial and geographic ranges, variation in locations visited, and the overall extent of physical mobility. The frequency and pattern of individuals’ communications with others and the content and manner of speech might also reflect key signs of several psychiatric disorders. Researchers have long known that speech activity can be affected in pathological states such as depression or mania. Thus, they have used audio features such as fundamental frequency, amplitude modulation, formant structure, and power distribution to distinguish between the speech of normal, depressed, and schizophrenic subjects (France, et al., 2000; Stoltzman, 2006). Today, common cell phones have the computational power needed to monitor these sensitive indicators of psychological state, offering the possibility of early detection of mental problems. The same types of reality mining data used for diagnosis would also be relevant for monitoring patient response to treatment, especially when such data on the patient are available for a period before diagnosis and can serve as a
baseline for comparison. 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. 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." Legal statutes are lagging behind data collection capabilities, making it particularly important to begin discussing how the technology will and should be used.

What are the legal requirements around personal data?
Who owns things like the information on your cell phone that list your current physical location? Basic functionality of mobile phones consists of the digital signal processing and transmission of the human voice

Advanced mobile phones have accelerometers, so that they can measure the body movement of their users

Result is rich characterization of their behavior One of the most important applications of reality mining may be the automatic mapping of social networks (Eagle and Pentland, 2006). Careful analysis of these data shows different patterns of behavior depending upon the social relationship between people. As an example, a current research project underway at MIT is aimed at understanding health-related behaviors and infectious disease propagation. Most government health services rely on demographic data to guide service delivery. Demographic characteristics, however, are a relatively poor predictor of individual behavior, and it is behavior – not wealth, age, or place of residence – that is the major determinant of many health outcomes. Reality mining provides a way to characterize behavior, and thus provides a classification framework that is more directly relevant to health outcomes (Pentland, 2008). The pattern of movement between the places a person lives, eats, works, and hangs out are known as a behavior pattern. Reality mining research has shown that most people have only a small repertoire of these behavior patterns, and that this small set of behavior patterns accounts for the vast majority of an individual’s activity (Pentland, 2007). Understanding the behavior patterns of different subpopulations and the mixing between them is critical to the delivery of public health services, because different subpopulations have different risk profiles and different attitudes about health- related choices. The use of reality mining to discover these behavior patterns can potentially provide great improvements in health education efforts and behavioral interventions. Research suggests that some chronic health-related conditions/behaviors are “contagious,” in the sense that individual-level outcomes are linked to other individuals with whom one shares social connections. Both smoking behavior (Christakis and Fowler, 2007) and obesity (Christakis and Fowler, 2008) seem to spread within social networks. Smoking and obesity likely serve as good models for other health related behaviors, such as diet, exercise, general hygiene, and so on. As the world becomes increasingly interconnected through the movement of people and goods, the potential for global pandemics of infectious disease rises as well. In recent years, outbreaks of SARS and other serious infectious diseases in widely separated but socially linked communities highlight the need for fundamental research on disease transmission and effective prevention and control strategies. Logs of location tracking data from cell phones could prove invaluable to public health officials when investigating cases of serious infectious disease (e.g., tuberculosis, SARS, anthrax, measles, Legionnaires’ disease, etc.) to help identify the source of infections and prevent further transmission. People often forget all the locations they have visited, even for recent periods, and similarly might not know many of the people to whom they were exposed or might have exposed themselves, all of which underlines the potential value of systematically analyzing such records for disease control. One approach to the privacy problem is to place the ownership of the personal information in the hands of the individual owner. This is even more important for medical records. Make data anonymous. If data is anonymous there is less concerns over privacy and the data can benefit society
Laws and our views on privacy need to continue to advance to catch up with these technologies.
Expanding technology on cell phones will lead to the ability to get more information for reality mining As technology and access to social networking sites becomes more available, reality mining will expand in the social networking arena Reality mining really is in its infancy, technology is now at the point where this data is becoming available and researchers and companies are learning what to do with. As this technology expands privacy laws will have to be addressed. The current lack of clear laws around this data needs to be addressed Depending on what happens with privacy laws, this tool could expand further into marketing uses and could result on further targeted advertising. One hundred of these phones were deployed to students at MIT during the 2004-2005 academic year. The photo below shows the patterns of proximity among the participants during one day; even casual examination shows that the students were part of two separate groups: the Sloan School and the Media Lab. Studies human interactions based on the usage of wireless devices such as mobile phones and GPS systems.

Providing a more accurate picture of what people do, where they go, and with whom they communicate with rather than from more subjective sources such as a people's own account.

Reality mining is one aspect of digital footprint analysis (BusinessWeek, 2008)
What is Reality Mining? Who uses Reality Mining? Software analytics companys
Sense Networks, Inrix & Path Intelligence

Personal Use
"Live sensors show the temperatures of sensor-instrumented trees. Upon detection of a fire, an alert is displayed. The 3D environment provides high-fidelity contextualizing views, allowing more response planning to be performed before experts arrive on site." (Credit: Accenture Technology Labs) "Virtual tourism or virtual shopping allows consumers to browse their local reality before leaving their homes. In this example, restaurant aerial views are augmented with their logos, which can be clicked to display live menus from the Web." (Credit: Accenture Technology Labs) Reality mining of behavior data is just beginning. In the near future it may be common for smart phones to continuously monitor a person’s motor activity, social interactions, sleep patterns, and other health indicators. MIT Data Storage Project In cooperation with their Social Network mapping
project students at MIT have also been addressing
the hardware issues associated with reality mining
and access to the enormous amounts of data that
are captured.
~ Types of storage media
~ Network structure to be utilized
~ Size and location of data storage facilities
~ Wireless or Blue Tooth Definition:


Journal Articles:
http://hd.media.mit.edu/RWJF-Reality-Mining-summary.pdf References Cell Phone Data Applications of Reality Mining Mapping social networks

Traffic data

Behavioral patterns

Epidemiology research

Medical Diagnosis
Full transcript