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AI insights from wearable sensors
January 27 @ 8:30 pm - 9:30 pm
“Wearable sensing and generative deep learning for gait dynamics assessment”
Speaker: Tian Tan, Postdoctoral Researcher in Radiology, Stanford University
Abstract: Quantifying human gait dynamics, including kinematics, external forces, and joint moments, can help diagnose diseases, prevent injuries, and customize rehabilitation programs. My research aims to use deep learning to model human movement dynamics, thereby enabling wearable-based gait dynamics analysis. To achieve this, I use generative deep learning to model human kinematics and external forces. I also use self-supervised learning to leverage knowledge from wearable sensors without requiring ground-truth kinematics or forces. The outcomes of these studies can be applied to model and monitor human gait dynamics in real life, potentially benefiting millions of underserved patients.
“MEMS & sensors in the Metaverse”
Speaker: Sneha Kadetotad, MS, Engineering Manager, Motion Sensors, Meta Reality Labs
Abstract: Sneha discusses the concept of the Metaverse and explores how MEMS and sensors contribute to enhanced features and experiences in Augmented Reality (AR) and Virtual Reality (VR). Additionally, she delves into the various challenges faced by AR/VR and shares insights on the innovative areas her team is investigating for the development of new AR/VR experiences.