A Survey on Remote Assistance and Training in Mixed Reality Environments

The recent pandemic, war, and oil crises have caused many to reconsider their need to travel for education, training, and meetings. Providing assistance and training remotely has thus gained importance for many applications, from industrial maintenance to surgical telemonitoring. Current solutions such as video conferencing platforms lack essential communication cues such as spatial referencing, which negatively impacts both time completion and task performance. Mixed Reality (MR) offers opportunities to improve remote assistance and training, as it opens the way to increased spatial clarity and large interaction space. We contribute a survey of remote assistance and training in MR environments through a systematic literature review to provide a deeper understanding of current approaches, benefits and challenges. We analyze 62 articles and contextualize our findings along a novel taxonomy based on collaboration degree, perspective sharing, MR space symmetry, time, input and output modality, visual display, and application domain. We identify the main gaps and opportunities in this research area, such as exploring collaboration scenarios beyond one-expert-to-one-trainee, enabling users to move across the reality-virtuality spectrum during a task, or exploring advanced interaction techniques that resort to hand or eye tracking. Our survey informs and helps researchers in different domains, including maintenance, medicine, engineering, or education, build and evaluate novel MR approaches to remote training and assistance.

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@inproceedings {Fidalgo2023, 
 author = {Fidalgo, Catarina and Yan, Yukang and Cho, Hyusung and Sousa, Mauricio and Lindlbauer, David and Jorge, Joaquim}, 
 title = {A Survey on Remote Assistance and Training in Mixed Reality Environments}, 
 year = {2023}, 
 publisher = {IEEE}, 
 keywords = {Mixed Reality, Remote Assistance, Remote training}, 
 url = {https://ieeexplore.ieee.org/document/10049704}, 
 doi = {10.1109/TVCG.2023.3247081}, 
 location = {Shanghai, China}, 
 series = {IEEE VR '2023} 
 }