Preserving Real-World Finger Dexterity Using a Lightweight Fingertip Haptic Device for Virtual Dexterous Manipulation
June 24, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yunxiu XU, Siyu Wang, Shoichi Hasegawa
arXiv ID
2406.16835
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This study presents a lightweight, wearable fingertip haptic device that provides physics-based haptic feedback for dexterous manipulation in virtual environments without hindering real-world interactions. The device's design utilizes thin strings and actuators attached to the fingernails, minimizing the weight (1.76g each finger) while preserving finger flexibility. Multiple types of haptic feedback are simulated by integrating the software with a physics engine. Experiments evaluate the device's performance in pressure perception, slip feedback, and typical dexterous manipulation tasks. and daily operations, while subjective assessments gather user experiences. Results demonstrate that participants can perceive and respond to pressure and vibration feedback. These limited haptic cues are crucial as they significantly enhance efficiency in virtual dexterous manipulation tasks. The device's ability to preserve tactile sensations and minimize hindrance to real-world operations is a key advantage over glove-type haptic devices. This research offers a potential solution for designing haptic interfaces that balance lightweight, haptic feedback for dexterous manipulation and daily wearability.
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