Challenges in Mixed Reality in Assisting Adults with ADHD Symptoms
August 11, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Valerie Tan, Jens Gerken
arXiv ID
2508.07854
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In this position paper, we discuss symptoms of attention deficit hyperactivity disorder (ADHD) in adults, as well as available forms of treatment or assistance in the context of mixed reality. Mixed reality offers many potentials for assisting adults with symptoms commonly found in (but not limited to) ADHD, but the availability of mixed reality solutions is not only limited commercially, but also limited in terms of proof-of-concept prototypes. We discuss two major challenges with attention assistance using mixed reality solutions: the limited availability of adult-specific prototypes and studies, as well as the limited number of solutions that offer continuous intervention of ADHD-like symptoms that users can employ in their daily life.
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