One of the big challenges of working with GPS data is the large amount of data that comes with it, which becomes difficult to store and analyze. This project solves this challenge by using compression algo- rithms to first reduce the amount of data and analyzes this information to efficiently store information about a user’s life through wearable glasses. The system analyzes the reduced dataset to answer queries about the user’s history. Applying machine learning techniques to the data, we develop a robust algorithm that accurately determines key aspects of users’ everyday life.

This research project entails working with the Distributed Robotics Lab- oratory group headed by Professor Daniela Rus on their iDiary project. The members of the project are working with a smart glasses system that can record images as well as triangulate its position through GPS. This system uses a user’s GPS data to identify locations that have been visited. Activities and terms associated with these locations are found using latent semantic analysis and then presented as a searchable diary.