Exploring Vibrotactile Intensity Perception with Multiple Waveform Parameters
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
Takumi Kuhara, Hikari Yukawa, Yoshihiro Tanaka
arXiv ID
2411.05099
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
It is known that by longing the duration of a vibrotactile stimuli or applying a damping or an increasing factor to the waveform the perceived intensity is affected in different ways. This paper presents a vibrotactile presentation system assembled with a software waveform generator that enables comparison in the perceived intensity for different waveforms made by multiple parameters. The adjustable parameters are frequency, amplitude, and wave type for the basic part of the stimuli and in addition, it is possible to apply an exponential decay or increasing factor to the waveform by specificizing the duration. By using the presented system, an easy comparison of the influence to the perception of intensity by different parameters of the waveform is possible. We conducted a preliminary experiment on a variety of waveshapes with and without damping by using this system.
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