How I Met Your Quantified Self Mother

I didn’t attend the show myself this year, but for what I’ve followed the media coverage and observations from my fellow ABIers who have been at the event, it would seem that the Internet of Things has indeed been at the epicentre of CES 2014. This isn't too surprising. A quick glimpse of Google Trends, for example, confirms that the IoT reached such a level of buzz in 2013 that it’s evidently starting to show up also in the consumer space.

Of the IoT concepts that have been showcased at CES, Mother from is perhaps the one that intrigues me the most. What I particularly like about it is that the product is trying to put the idea of a universal, multi-purposeful sensor dongle into a form that would be pleasant and unintrusive enough for Quantified Self plays. Sensing only temperature and motion/presence through its “motion cookies”, Mother is not as comprehensive as e.g. Twine from Supermechanical (which is more of a Smart Home play), but that has permitted to make those sensor cookies small enough to be attached to a water glass or a toothbrush. This is important is because it allows the users to experiment with a very wide away of “Things” they want to monitor and thus help to expand its repertoire of possible use cases.

What also caught my attention was the fact that ZDNet happened to include Mother in its list of the worst, “born-to-fail” gadgets of the show, with the point being that the product’s uses revolve around “problems” that really don’t need to be solved with the IoT to begin with. One doesn’t require technology to make the kids brush their teeth, or to remember to drink enough water; just apply ordinary offline parenting, drink water when you’re thirsty, and be done with it. These are legit points, as such, but in my view they over-simplify the whole idea of Quantified Self. The value does not necessarily come from monitoring one single activity and being reminded of its significance. Rather, it’s about capturing data across our everyday lives, spotting patterns in the data, and making accordingly informed decisions that will improve our quality of living.

Take for example that water-drinking use case, and assume that I would start measuring my water consumption. The real value of this would not be in preventing thirst, but in quantifying the intake and thus determining whether I’m being adequately hydrated. (See, if I only drink water only when I'm feeling thirsty, there’s a good chance that I'm not actually getting enough of it.) The water data – combined with data data on how much physical exercise I do, how much time I spend sitting, what the temperature in my bedroom is, etc.  – could then be analyzed against the data on my sleeping patterns, to explore whether my quality of sleep would improve if I “optimized” my water intake, changed my exercise regime, or adjusted the night-time room temperature. There is a fair deal of anecdotal insight and conventional wisdom out there on how factors like those affect the quality of life, or different aspects of it (such as sleep), but for obvious reasons the hard data on all that is so very scarce.

That’s precisely where Quantified Self apps come in. At the end of the day, they are not meant to remind us of doing something, rather than informing us about how that something can help us. Tracking an area such as daily water intake should thus be seen namely as a means to a greater end – and not as a dedicated thirst-prevention application. I can’t tell yet whether and its Mother would be ultimately capable of this kind of holistic, horizontal data gathering with sufficient accuracy, and at this stage they clearly don't have the analytics capabilities needed to deliver the insights either. Nonetheless, I’m confident that a company that manages to pull all that off will have a sure winner in its hands. For that reason, dismissing Mother as born-to-fail only because the problems it aims to solve seem trivial at first sight sounds quite misguided to me. Doing so is to misunderstand the said problems.