- For the kind of projects there some intersections between DIY project and research project as well as commercial project and research project.
- It is understandable why there are no intersection on commercial and DIY project.
- For any projects those are related to interactivity and human interaction, it seems that most of these implementations have a sense for location.
- Location can be both global positioning system from GPS or local positioning system that can be simple as to mention on which room the user is in.
- AS previously mentioned, wireless communication is not mandatory unless the implementation can work independently.
- However, implementations about interactivity cannot work independently as it needs counter measure from similar device. For these device the minimum wireless communication will be infrared that transfer identification codes to other devices.
- What I want to say here?
- I think I would just infer directly from the table....
- But that might be too long.
- By looking from the table, it seems that every social related devices need to have context. Any ....
- I suggest that there are two layers of results come from socially aware devices. The first layer is the raw sensor data from the device itself. However, just sole numbers are not enough for human comprehension, context needs to be given. This present the second layer of the results, of which context agent will take role on determining context on each numbers. Context agent gives meaning to the numbers. For example, there is a fitness tracking device that measures the speed of its wearer. It shows information on how fast the wearer runs. However, for social data gathering the numbers does not mean anything. There are unlimited of possible contexts. For an instance, whether the wearer was running as their daily routine or running to chase departing train from the station. Usually, common fitness tracking uses internal or smart phone GPS to determine where the user was running. If it detected acceleration and the wearer was nearby train station, the latter context can be assumed.
- Usually context agent comes as a qualitative data (boolean, ID, or local position system). From the implementations listed in the table (please put reference from the State of the Art table) position and presence are meant as a context given agent. However, quantity data like global positioning system can define context as well. Nowadays machine learning can be leveraged as a context agent to detect patterns and then give meaning to the raw sensor data (please put reference here from the main paper of Sociometer and Sociometric Badge). However, for smaller data set, context can be manually given by human as well. Finally, after both layers are identified, conclusion can be pulled. be pulled.