The concept of recording data about our lives is not exactly a new one: history is full of figures like Samuel Pepys, who kept in-depth diaries recording as much as they could about their days. But lifelogging, as a whole, has grown beyond that limited beginning. Where diarists were limited by what details they could remember whenever they could get back to their pens and papers, lifeloggers today rely on technology to gather data for us.
Wearable Computing, Web Cams, and the Beginning of Lifelogging
Lifelogging depends on being able to track data about yourself and your surroundings. Without computers and sensors small enough to wear, it just wouldn’t be possible. Steve Mann is considered the ‘father of wearable computing,’ as well as the first lifelogger. Starting in 1994, Mann wore a a webcam and broadcast a live feed around the clock.
While video might not be our first choice for capturing data these days, continuous live streaming pushed the growth of the lifelogging movement through its early days.
Lifecasting — the process of publicly sharing your whole life through streaming video — is still around. Smartphones have provided new tools to lifecasters, letting some build up large audiences. Other facets of the idea of lifelogging have taken the stage, including Gordon Bell’s MyLifeBits project. Bell, through the project, digitizes all documents he reads or produces (as well as other parts of his life). Lifelogging encompasses these approaches, as well as the use of sensors to record personal data.
Sensors, Quantified Self, and Logging Some Serious Data
As wearable computing has evolved, small sensors (other than web cams and other video cameras) have become available. The expansion of just what inputs we can track have exploded in the past ten years. We can record a wide variety of data, from our own blood oxygen levels to the quality of the air around us — the limits are more about the number of sensors each person is willing to wear at once than about the types of data we can collect.
Wired Magazine took note of the appeal of these sensors for self-tracking and editors Gary Wolf and Kevin Kelly dubbed the movement “Quantified Self.” It stuck: there are Quantified Self groups all over the world, as well as a yearly conference.
Smartphones, Analysis, and Managing Big Numbers
But while the Quantified Self movement built on the concept of tracking every last bit of your life — and perhaps even tweaking your own behaviors as a result — sensors and wearable computers weren’t enough to popularize the concept on their own. It took the development of analytical tools to push the idea into the mainstream. Smartphones, both because of the sensors that such devices automatically put in every user’s pocket and because of the analytical capabilities of the devices, take lifelogging to a whole new level.
Fitness trackers and other sensors have quickly become normal: you might see a friend wearing a Fitbit, a FuelBand or a Jawbone wristband whether or not they consider themselves lifeloggers of any variety. Tracking fitness data is rapidly proving to be the gateway to something deeper, though, as more products with built-in sensors come to market, such as sleep monitors.
But the popularization of lifelogging has highlighted one of the questions sure to guide the future of the movement: What should we do with all this data? Storing it isn’t a problem, but we’ve only started looking at what we can do with analysis, especially across multiple sets of data. Even the most experienced lifeloggers only have years of data to work with; most of us have a few years or even just a few months. We already know that we can extract meaning from older data, but we need to have useful archives first. That doesn’t just mean collecting data in the first place, but storing it in a way that we can come back to the numbers after we’ve put together more information. Lifelogging may have started out with just recording video of what might be happening around you, but it’s rapidly grown into an approach to tracking different parts of your life — and acting on the data you collect.