RunPacer: A Smartwatch-Based Vibrotactile Feedback System for Symmetric Co-Running by Visually Impaired Individuals and Guides
July 06, 2025 Β· Declared Dead Β· π International ACM SIGACCESS Conference on Computers and Accessibility
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yichen Yu, Huan-Song Xu, Ming-Yen Lin
arXiv ID
2507.04241
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
International ACM SIGACCESS Conference on Computers and Accessibility
Last Checked
5 months ago
Abstract
Visually impaired individuals often require a guide runner to safely participate in outdoor running. However, maintaining synchronized pacing with verbal cues or tethers can be mentally taxing and physically restrictive. Existing solutions primarily focus on navigation or obstacle avoidance but overlook the importance of real-time interpersonal rhythm coordination during running. We introduce RunPacer, a smartwatch-based vibrotactile feedback system that delivers synchronized rhythmic pulses to both runners. In contrast to conventional guide-running systems that rely heavily on continuous verbal communication or mechanical tethering, RunPacer emphasizes interpersonal cadence alignment as its core interaction model. By pre-setting a target step frequency or dynamically adapting to the guide's natural pace, the system ensures that both runners receive identical haptic cues, enabling them to maintain coordinated motion intuitively and efficiently. This poster presents the system architecture, positions it within prior research on haptic entrainment, and outlines the vision for future field deployment, including potential multimodal feedback extensions. RunPacer contributes a lightweight, socially cooperative, and non-visual assistive framework that reimagines co-running as a shared, embodied, and accessible experience.
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