Investigating Encoding and Perspective for Augmented Reality
October 01, 2025 Β· Declared Dead Β· π International Symposium on Mixed and Augmented Reality
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jade Kandel, Sriya Kasumarthi, Spiros Tsalikis, Chelsea Duppen, Daniel Szafir, Michael Lewek, Henry Fuchs, Danielle Szafir
arXiv ID
2510.00407
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
International Symposium on Mixed and Augmented Reality
Last Checked
4 months ago
Abstract
Augmented reality (AR) offers promising opportunities to support movement-based activities, such as personal training or physical therapy, with real-time, spatially-situated visual cues. While many approaches leverage AR to guide motion, existing design guidelines focus on simple, upper-body movements within the user's field of view. We lack evidence-based design recommendations for guiding more diverse scenarios involving movements with varying levels of visibility and direction. We conducted an experiment to investigate how different visual encodings and perspectives affect motion guidance performance and usability, using three exercises that varied in visibility and planes of motion. Our findings reveal significant differences in preference and performance across designs. Notably, the best perspective varied depending on motion visibility and showing more information about the overall motion did not necessarily improve motion execution. We provide empirically-grounded guidelines for designing immersive, interactive visualizations for motion guidance to support more effective AR systems.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted