I think that this year will be the breakout year for wearable personal analytics gadgets, those bracelets, clips, shoe sensors and apps that count our steps, track our fitness, watch what we eat and analyse our sleep patterns. This month saw these gizmos capture some significant limelight at the annual Consumer Electronics Show in Vegas with everything from smart watches to kids pedometers, and from geeky bangles to the now notorious Hapifork (yes it’s a fork, but it buzzes to tell you to slow down when your are eating too fast!).
In the past these types of gadgets have tended to focus on measuring one type of activity, perhaps sleep, or counting steps and measuring distance and speed with the help of a connected phone app; it all really started back in 2006 when Apple announced their partnership with Nike and launched the Nike+ pedometer, which communicated with your iPod nano as you exercised. But today devices like Jawbone’s UP are taking this a lot further, by combining activity tracking with sleep monitoring and allowing you to track your eating. And I must say there is something very compelling as you start to use these devices and related apps as you quickly become immersed in your personal activity data. It’s fun to slice and dice your progress as the weeks roll in to months.
I think that two things will happen this year to push this tech into the mainstream. First they will be come truly wearable. This means that their batteries will last for days, they will offer over the air synching, and they will survive a trip to the shower. They will also start to look good!
Second these apps and devices will start to get a lot smarter. Right now I need to tell my iOS RunKeeper app when I am jogging vs cycling vs skiing. But in truth this information is hiding in plain sight in the patterns of accelerometer and location data the app collects. Data that this year’s new crop of apps will start to mine and use. How exactly? Well identifying the activity is just the first step and, truth be told, not a terribly interesting one. The next logical step will be to use this data to make recommendations to people for new products and services, running shoes or fitness programmes, for example. Maybe offer a discount for a local gym that specialises in the type of cross training that you are not getting from your run-focused fitness schedule. Will my app recommend a change in my activity or eating habits to aid a better night’s sleep? Or what using our activity data to identify and create communities of like-minded people to exercise together. This is all just around the corner. And as I wrote this blog post I came across Amiigo on the crowdfunding site Indiegogo, which aims to do much of this by June this year.