Presentation
Ergonomic Augmented Reality Glasses Development for Workload Detection with Biofeedback Data and Machine Learning
DescriptionAugmented reality (AR) glasses have proven effective across various sectors, including rehabilitation, education, surgery, and manufacturing, using devices like Meta Quest and Microsoft Hololens 2. Despite their utility, these headsets often suffer from bulkiness and visual fatigue, which can lead to neck strain and simulator sickness—symptoms like discomfort, nausea, and disorientation. Focusing on hand tracking and use cases in different fields, there's a gap in ergonomic design addressed by literature on EMG and ECG for gesture recognition. Therefore, this study aims to develop ergonomic AR glasses made from lightweight 3D-printed polylactic acid, reducing bulkiness and fatigue issues. The design process includes literature review to identify research gaps, sourcing lightweight components, constructing and programming the glasses with a machine learning algorithm, and testing EMG and ECG data collection. This preliminary study is a steppingstone for the next study to develop rigorous AR glasses to provide workload information real-time.
Contributor
Assistant Professor
Event Type
Lecture
TimeTuesday, September 10th1:50pm - 2:10pm MST
LocationFLW Salon J
Extended Reality