Advisor: Dr. Yingying (Jennifer) Chen
Mentors: Changming LiGR, Honglu LiGR, Tianfang ZhangGR
Team: Dirk Catpo RiscoGR, Brody VallierHS, Emily YaoHS
Final Poster with Results }}} == Project Overview Augmented reality/virtual reality (AR/VR) is used for many applications and have been used for many purposes ranging from communicating and tourism, all the way to healthcare. Accessing the built-in motion sensors does not require user permissions, as most VR applications need to access this information in order to function. However, this introduces the possibility of privacy vulnerabilities: zero-permission motion sensors can be used in order to infer live speech, which is a problem when that speech may include sensitive information. == Project Goal The purpose of this project is to extract motion data from AR/VR devices inertial measurement unit (IMU), and then input this data to a large language model (LLM) to predict what the user is doing == Weekly Updates === Week 1 **Progress** * Read research paper ![1] regarding an eavesdropping attack called Face-Mic **Next Week Goals** * We plan to meet with our mentors and get more information on the duties and expectations of our project === Week 2 **Progress** * Read research paper ![2] regarding LLMs comprehending the physical world * Build a connection between research paper and also privacy concerns of AR/VR devices **Next Week Goals** * Get familiar with AR/VR device: * Meta Quest * How to use device * Configure settings on host computer * Extract motion data from IMU * Connecting motion sensor application program interface (API) to access data * Data processing method === Week 3 **Progress** * Set up host computer and android studio environment * Started extracting data from the inertial measurement unit (IMU) * Recorded and ran trials of varying head motions **Next Week Goals** * Run more tests to collect more data * Design more motions for data collection *Different head motions * Rotational * Linear * Combinations of head motions * Looking left and then right * Looking up and then down === Week 4 **Progress** * Designed and collected more motion data * Looking up then back middle * Looking right then back middle * Moving head around in a shape * Moving head back and forward * Used MATLAB noise removing functions to clean graphs * smooth * lowpass * findpeaks * 3D visual of acceleration to show time and position **Next Week Goals** * Find a way to get hand motion data using Android Studio * Work on fixed prompts to get accurate LLM results using ChatGPT 4o and ChatGPT 4 === Week 5 **Progress** * Enabling hand motion data collection using VR device * Utilize Android Studio and !VrApi to access VR controller interface and extract motion data * Conducted additional motion experiments to gather comprehensive data sets * Motion data with both head and hand activities * Implemented 3D plots to visualize and analyze hand motion data for accuracy **Next Week Goals** * Utilize motion research paper ![3] to model more motion activities * Start building a CNN model that can recognize activity based on motion data === Week 6 **Progress** * Made a list of different motion data to capture and train a convolutional neural network (CNN) * Do research on previous work based on raw motion data and CNN’s * Specifics of the motion data * Samples * Users * Data * Design prompt for LLM **Next Week Goals** * Start getting more motion data * Start using LLM to analyze the collected data * Use the designed prompts * Design and try more prompt structures and compare LLM responses === Week 7 **Progress** * Placeholder **Next Week Goals** * Placeholder == Links to Presentations {{{#!html
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Final Presentation
}}} == References {{{#!html }}}