Having an insight into the behavior and preferences of users of a system allows delivery of better services to those users. For example, shops with an insight into behavioral histories of shoppers can stock products that are of interest to those shoppers. Museums, on the other hand, can get a better understanding of which displays are of more interest to their patrons and organize museum exhibits accordingly. Alternatively, content providers can target the content to be provided appropriately, whether the content is used for advertising, informational or educational purposes.
One of the key requirements of acquiring historic behavioral patterns is automated data collection. In a networked environment, data collection regarding a user's activity can be readily carried out by collecting data regarding a user based on a user account and activity of that account at web sites, social networks and other digital activities. Cookies and other tracking mechanisms facilitate tracking and data collection.
Collecting such data coherently for a user regarding that user's physical activity, however, is challenging since a user does not automatically log in to an account to perform their physical activities. Moreover, data collection becomes even more challenging given privacy requirements. Accordingly, new system and methods that address problems specifically associated with collecting data regarding a user's physical activities are needed.