We live in a world where information is ever prevalent. Sensors and big data crunching algorithms work hand in hand to generate stats and numbers like never before. Some of these processes are being applied to the entire concept of fitness tracking and lifelogging, which we’ve talked about previously, to give valuable stats about the body functions.
Tracking and monitoring data indeed gives some interesting results, and this basically leads up to the Quantified Self. But wouldn’t it be better to analyse the reasons behind the change in patterns? Data by itself is good, but it is incomplete. What the Quantified Self movement now needs is the presence of Context, which is the main crux of my article here.
QUANTIFIED SELF: BY ITSELF…
Gathering data from the surroundings, and applying them in a creative and visually appealing manner, has been the norm of tracking for quite some time. With the advent of wearable devices, it has become even more simple and accurate to monitor the fitness stats more effectively.
One thing I keep harping upon is how sensors have become cheaper, leading to inexpensive wearable devices and ultimately leading up to more accessible trackers. These trackers, aided with software suites dedicated to keeping logs of the data collected, generate insights which can actually help frame a better understanding.
… BUT IS IT ENOUGH?
No, it isn’t.
To prove my point, I’ll go with a simple example. Go back to the time you were a student. Monitoring grades was a good habit: you could keep a track of which subjects you deteriorated in, and which subjects you scored well in – and you could therefore get a clear idea on which was your strong subject.
But what if you knew the reason you didn’t do well in a particular subject? Maybe you could use that data to help better yourself there. And this, this is the reason why data by itself isn’t enough.
For instance, consider the following sleep data from the Fitbit blog. Although it provides a lot of statistics and numbers, it doesn’t explain causative forces, and is therefore incomplete.
WHERE DOES CONTEXT COME IN?
Context does not require new data: it uses the data already collected from various sensors to draw valuable conclusions, humanize them, and present them to us in an informative format.
Let’s say on a particular day, you’ve not run as much as you usually do. Through tracking devices following the Quantified Self scheme, you get to know that you’ve run a mile less. But through context, you can analyse the reason for that less mile run: Maybe you’ve got an ankle injury, or the weather was too hot and humid that day, or maybe you didn’t get enough sleep the previous night. Whatever the reason, contextual machine learning algorithms will analyse the causative factors and try and help you out.
And doesn’t this lead to a more comprehensive experience?
With a contextual approach to analysing data, we’re not just looking at the QUANTITY, but also the QUALITY of our actions. And this enhanced level of self assessment ultimately paves the way for a better understanding of ourselves, which can in turn lead to better results in the future.
The following data, taken from the 2015 Super Bowl final between the New England Patriots and the Seattle Seahawks, provides data regarding heart rates, and also applies them to what was going on. Cause and effect right here!
APPLYING CONTEXT IN DAILY LIVES.
Now Newton’s third law states that for every action, there’s an equal and opposite reaction. Applying this law in today’s world of Context and Quantified Self would be an apt step forward. In fact, an essay by Nigel Goldenfeld explains how there is a wide gamut of causative factors behind a particular event, rather than a simple cause-and-effect norm.
For every event that occurs, there are a multitude of possible causes, and the extent to which each contributes to the event is not clear, not even after the fact!
Emberify has applied this mode of thinking in Instant. Simple data is collected from the phone, to lead up to a more defining and comprehensive experience. From basic things like how many times you’ve unlocked your phone and how much time you’ve used on your device, to how much time you’ve actually spent in an application: data like this can help people curb their smartphone addiction problems.
Context is definitely an up and coming concept, with a heavy emphasis on it and machine learning at Google I/O 2015. Data is becoming more intelligent, and I feel that these developments can only get bigger and better in the time to come!