The Quantified Self market has grown in the past year with an array of tracking wearables with expert services. With wearables & sensors in our smartphone, lots of data can be tracked automaticall. But that’s where the contextual experience ends. It needs to go beyond this. For example knowing what data to present when according to the user’s context or understanding patterns to customise the user experience, for every individual person.
We have running apps or step trackers that give us our daily goal trying to help us to form habits. Shouldn’t they consider factors like if we are unwell or that it is snowing outside. It isn’t too tough to detect these contextual parameters. Simple movement or travel for the day can make it clear that the person is unwell or we also have heart rate sensors passively gathering information. These Quantified Self services need to work together to understand patterns rather then just storing data.
Notifications is another problem with Quantified Self services. With sensors or lifelogging apps connected, they notify you the entire day. Does the user even need that data? Or is it a good time to give the user this data? With so much context available to these services they must adapt to an individuals needs & learn the user better.
3 Human interactive data
Expert based services are slightly better in this manner. With the human element present, much more context based understanding happens along with the data. That’s how we can make the Quantified Self movement accessible to everyone. Artificial Intelligence and Machine Learning techniques can solve these problems.
Context can help explain users their data better. The ‘why’ of the Quantified Self movement is still missing. This can add tremendous amounts of value for the user. Thinking beyond collecting the data is a crucial part for the Quantified Self movement to become useful to the masses. It will definitely reduce the churn rate of Quantified Self apps, devices & services.
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