The chart shows different sensors extracting different types of data, how they might be classed and what might be inferred from the data. Note that it’s not just about the sensors. How the sensors are used and what the user is doing with other applications (“soft sensing”) also becomes part of the user’s data stream, and these are fused together by something Nachman called the “Activity Fusion Algorithm.”
If you want the systems in your life to really be smarter about what you do, and make recommendations or give you useful information, then you’ll want all of the sensors in all of your systems (portable or not) to aggregate that data. The inference engine can then be much smarter about what predictions it makes about what you’ll do or like. If that sounds creepy, it could be. But if it’s done right, it could be a huge productivity enhancer.
Now comes the tricky part. All of this data collection and aggregation requires tremendous compute and storage capability. It also implies that some of this data is either stored in the cloud, on distant servers, or passed through systems on the Internet. So, users need granular control over what data is collected and where it’s stored.
All of this is still in very early stages of development. For example, if you want to have sensors up and running 24x7, the power draw needs to be extremely low, and you need the ability to quickly and easily recharge. Anyone who’s used a GPS radio on a smartphone can understand how challenging this might be for certain classes of sensors.
Of course, the biggest question is social: will people really want it? Different users might have different feelings about being monitored constantly. Just look at the controversy surrounding the recent launch of Facebook Places.
In the end, context-aware computing will likely become prevalent a decade or so from now. However, it’s really difficult to predict what form it will take and how it will be actually be implemented and used. That’s the nature of research: what shows up in the lab today may affect what products we see a decade later, but predicting what those will be is a much harder nut to crack.