x-RAGE: eXtended Reality -- Action & Gesture Events Dataset

With the emergence of the Metaverse and focus on wearable devices in the recent years gesture based human-computer interaction has gained significance. To enable gesture recognition for VR/AR headsets and glasses several datasets focusing on egocentric i.e. first-person view have emerged in recent years. However, standard frame-based vision suffers from limitations in data bandwidth requirements as well as ability to capture fast motions. To overcome these limitation bio-inspired approaches such as event-based cameras present an attractive alternative. In this work, we present the first event-camera based egocentric gesture dataset for enabling neuromorphic, low-power solutions for XR-centric gesture recognition. The dataset has been made available publicly at the following URL: https://gitlab.com/NVM_IITD_Research/xrage.

Non-Invasive Qualitative Vibration Analysis using Event Camera

This technical report investigates the application of event-based vision sensors in non-invasive qualitative vibration analysis, with a particular focus on frequency measurement and motion magnification. Event cameras, with their high temporal resolution and dynamic range, offer promising capabilities for real-time structural assessment and subtle motion analysis. Our study employs cutting-edge event-based vision techniques to explore real-world scenarios in frequency measurement in vibrational analysis and intensity reconstruction for motion magnification. In the former, event-based sensors demonstrated significant potential for real-time structural assessment. However, our work in motion magnification revealed considerable challenges, particularly in scenarios involving stationary cameras and isolated motion.