The quantified-self movement has existed for many years as a
collaboration of people collecting continual data on their everyday
activities in order to make better choices about their health and
behaviour. But, with today’s Internet of Things, the movement has begun
to come into its own and have a wider impact.
Smartphones contain a rich record of people’s activities, including
who they know (contact lists, social networking apps), who they talk to
(call logs, text logs, e-mails), where they go (GPS, WiFi, and
geotagged photos) and what they do (apps we use, accelerometer data).
Using this data, and specialized machine-learning algorithms, detailed
and predictive models about people and their behaviours can be built to
help with urban planning, personalized medicine, sustainability and
medical diagnosis.
For example, a team at Carnegie Mellon University has been looking at
how to use smartphone data to predict the onset of depression by
modelling changes in sleep behaviors and social relationships over
time. In another example, the Live hoods project, large quantities of
geotagged data created by people’s smartphones (using software such as
Instagram and Foursquare) and crawled from the Web have allowed
researchers to understand the patterns of movement through urban spaces.
In recent years, sensors have become cheap and increasingly
ubiquitous as more manufacturers include them in their products to
understand consumer behaviour and avoid the need for expensive market
research. For example, cars can record every aspect of a person’s
driving habits, and this information can be shown in smartphone apps or
used as big data in urban planning or traffic management. As the trend
continues towards extensive data gathering to track every aspect of
people’s lives, the challenge becomes how to use this information
optimally, and how to reconcile it with privacy and other social
concerns.
No comments:
Post a Comment