At Google IO this year, Google announced a new Activity Recognition capability for Android as part of the new Google Play services. It is realized as an API which relies on low-power sensors and a machine learning classifier to track users’ activities. We did a little bit of experimentation to test this API to learn how accurate it is and how it can be exploited in the field.
Location based services (LBS) are increasingly an essential part of our digital lives. What started as an add-on to help improve Internet services has today become an intrinsic aspect to the delivery of very many services, especially in the mobile context. For example, mobile apps are increasingly using the users’ location to deliver services such as weather, transportation info, traffic, shopping, and many more.
The vision for the Connected Car is to bring the Internet into the car, adapting to the specifics of that environment. While the term was coined some time ago, it is often unclear what is meant by the Connected Car: here we outline the Carmesh vision for the Connected Car and some of the issues that arise.
In a previous post, we considered how facebook Graph Search could potentially be used within the automotive context, covering possible use cases etc – Graph Search in general clearly offers huge potential and this is also true of the automotive context.
In Carmesh, we were interested in exploring what is possible with facebook data and, in particular, whether some of the Graph Search capabilities are available to external applications. Continue reading