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Towards an Equitable Social VR

Lead Research Organisation: University of Cambridge
Department Name: Engineering

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
 
Description A large output is a highly cited review article called 'Inclusive Immersion' which sets up a research agenda, identifies gaps in the literature, and synthesises guidelines for designing inclusive virtual and augmented reality content and interfaces. Another output is an understanding of stakeholder perspectives in accessibility through an interview study with designers. In terms of technical progress, we have created and explored novel immersive interfaces such as 'swarm manipulation' which allows users to control virtual swarm particles to make selections and 'Hot Gestures' which uses state of the art machine learning to allow users to carry out functions in virtual reality by simply carrying them out, bypassing a function selection stage.
Exploitation Route The Inclusive Immersion review sets up a clear research agenda for accesssible interfaces and content in virtual and augmented reality. Where relevant, code is shared and available to other researchers and practioners in industry.
Sectors Creative Economy

Digital/Communication/Information Technologies (including Software)

Leisure Activities

including Sports

Recreation and Tourism

Manufacturing

including Industrial Biotechology

 
Description The 'Hot Gestures' paper resulted in a magzine article in IEEE Spectrum (a trade magazine for electrical and electronics engineers). https://spectrum.ieee.org/virtual-reality-tool-making
First Year Of Impact 2023
Sector Digital/Communication/Information Technologies (including Software),Manufacturing, including Industrial Biotechology
Impact Types Societal

 
Title Research data supporting "HotGestures: Complementing Command Selection and Use with Delimiter-Free Gesture-Based Shortcuts in Virtual Reality" 
Description 1. The HotGestureData.zip contains a dataset used for training the recognizer in the HotGestures paper. The dataset consists of hand skeleton data of 10 gesture classes and the Null gesture class, collected from 8 participants. Each csv file is a gesture clip that records the trajectory of the 1) ABSOLUTE 3D position (x, y, z) and quaternion (x, y, z, w) of the wrist, and 2) positions and rotations of other joints RELATIVE to the wrist. Please read the markdown (.md) file for more details. 2. The HotGestureSource.zip contains the python source code of the recogniser. One can train this recognizer using the dataset above to implement the HotGestures. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact None so far. 
URL https://www.repository.cam.ac.uk/handle/1810/354385