Perceptual Dimensions of Physical Properties of Handheld Objects Induced by Impedance Changes
December 04, 2023 Β· Declared Dead Β· π IEEE Access
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Takeru Hashimoto, Shigeo Yoshida, Takuji Narumi
arXiv ID
2312.01707
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
IEEE Access
Last Checked
4 months ago
Abstract
Haptics in virtual reality is the emerging dimension after audiovisual experiences. Researchers designed several handheld VR controllers to simulate haptic experiences in virtual reality environments. Some of these devices, equipped to deliver active force, can dynamically alter the timing and intensity of force feedback, potentially offering a wide array of haptic sensations. Past research primarily used a single index to evaluate how users perceive physical property parameters, potentially limiting the assessment to the designer's intended scope and neglecting other potential perceptual experiences. Therefore, this study evaluates not how much but how humans feel a physical property when stimuli are changed. We conducted interviews to investigate how people feel when a haptic device changes motion impedance. We used thematic analysis to abstract the results of the interviews and gain an understanding of how humans attribute force feedback to a phenomenon. We also generated a vocabulary from the themes obtained from the interviews and asked users to evaluate force feedback using the semantic difference method. A factor analysis was used to investigate how changing the basic elements of motion, such as inertia, viscosity, and stiffness of the motion system, affects haptic perception. As a result, we obtained four critical factors: size, viscosity, weight, and flexibility factor, and clarified the correspondence between these factors and the change of impedance.
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