User analytics Project Proposal:
Currently, one of the most discussed museum artifacts is curation, and the conversation usually centers around how to make the experience enjoyable for users and profitable for musuems. In order to effectively evaluate the impact of their curation efforts, as well as the overall selling features of the museum, museums currently rely on user surveys. However, these methods can introduce bias and provide inaccurate results. As a result, there is room for substantial improvement on understanding and effectively monetizing museum visitors. However, many systems currently in existence provide concerns about user privacy, data collection, and legal compliance. To this end, we propose the installation of cheap $5-$10 dollar cameras that track and process user data on board such that there is no concern of data other than batched user analytics leaving the device.
We propose a system that allows museums to track where their visitors spend the most time, interpret emotional feedback, and gather analytics on visitor flow to optimize the presentation. To implement this system, we suggest a network of cost-effective cameras along with other required sensors that maintain user-privacy by anonymizing and performing computations entirely on chip.
We believe that with this series of attributes, the data collection framework has the potential to be compliant with user privacy laws, unitrusive to guests, and enable museums to effectively achieve their missions.
The envisioned audience for your project: Musuem curation teams as well as marketing teams; this is intended to help them more acturately gauge their audiences tastes.
Potential museum collaborators: MFA; Harvard Art Museums, MIT-Museum, a ideal location would probably be a sub exhibit of the MIT musuem.
Technological/spatial/installation approach: For trials, it is possible to make the entire system relatively pain-free to install; it would probably just consist of placing cameras in locations across the installation to be observed, and plugging them in.
Skill sets needed for your project:
TinyML experience, data analyst, some lightweight hardware design, and legal consul to show compliance with local data privacy laws.