Electrostatic Tactile Display without Insulating Layer
November 07, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hiroyuki Kajimoto
arXiv ID
2411.05149
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
This paper explores an approach to eliminating the surface insulating layer in electrostatic (electroadhesion) tactile displays. Electrostatic tactile displays modulate the surface friction by an electrical charge between the skin and the display. Traditionally, the non-conductive dielectric layer has been considered crucial for charge accumulation, as well as for safety to prevent DC current stimulation. However, by utilizing a current control technology for electrotactile displays, we can achieve electrostatic tactile display without the insulating layer. The electrical charge is possibly accumulated in the skin itself or in the air gap between the skin and the electrodes. Safety is maintained by balancing positive and negative current pulses. Furthermore, this system is compatible with existing electrotactile displays. This paper details the system configuration, presentation algorithm, and experimental results. The preliminary trial revealed that five out of eight participants could clearly feel the vibration, confirmed by acceleration recording, while the remaining participants could not experience the sensation.
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