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• Project requirements.
• I personally separate my deliverables into three categories.
  • The first category is the ideal requirement.
  • The second category is the realistic requirement.
  • The third category is the minimal requirement.
• Drafts.
  • The ideal requirement refers to hypothetical scenario where limitations does not exists. This refer for a deliverable to be as ideal as possible, disregarding limit on money, knowledge, and time available. The idea is here is to know all the possibilities. Hence, the this ideal solution can be further dumbed down into more down-to-earth solution.
  • The minimal requirement refers to a minimal deliverable to test functionalities of the overall deliverable. The key is to make thing that is just barely testable. The goal is to set a stepping stone to develop the realistic requirement.
  • The goal of this project is to define and build realistic requirement. The upper ceiling point was set by previously defined ideal requirements. And, the entry point was set by defining minimal requirement. The process started defining the ceiling point that can be further dumbed down, while practically making the from ground up. The result from the process is the realistic requirement.
  • The physical computing, as I previously defined in (please reference to State of the Art here!), will mostly deal with which sensors to use.
  • The ideal case scenario is to implement all known sensors have ever used for social data gathering. This notion and this project intention to make the alternative to Sociometric Badge with experiment and research in mind, suggest that the device needs to be easily extended and reduced as well. Hence, it can afford to be extended by various sensors those have not yet defined as useful for taking incoming social signal. In term of hardware flexibility, previously mentioned Queercon Badge offers two input and output socket in its uppermost section. These let its users extend the physical capability of Queercon Badge.
  • The minimal scenario is just to implement a multi-modal sensor to the alternative of Sociometric Badge this project is trying to make. The example of concise device that only use a multi-modal sensor is Memoto/Narrative Clip. It only have a camera to take both photos and videos. However, data comes from photos or videos is very rich. Thus, there are a lot of things can be used to interpret for social scientists.
• From both cases, the realistic approach is to keep using one multi-modal sensor and use specific context-given sensors. From the previous chapter (please reference the previous chapter about State of the Art), the latent context-given agent are either location, presence, machine learning, or manually given by human. Since I have no experience on machine learning, this makes only three options left. However, manually given context by human is not scalable. With increasing respondents, the amount of works will be increased exponentially. So, the options left in presence and location. Conveniently, both can be done with identification from infrared transceivers between devices. However, if I would choose which one is the more important, that will be presence detection. With ability to identify who is facing/around the user give more dimension for social experiment that related to real-life interactivity.e interactivity.