Measuring the spatial Acuity of vibrotactile Stimuli: A new Approach to determine universal and individual Thresholds
August 10, 2023 Β· Declared Dead Β· π Displays (Guildford)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Max vom Stein, Maximilian Hoppe, Maxim Sommer, Kai-Dietrich Wolf
arXiv ID
2308.05497
Category
cs.HC: Human-Computer Interaction
Cross-listed
q-bio.NC
Citations
2
Venue
Displays (Guildford)
Last Checked
4 months ago
Abstract
Tactile perception is an increasingly popular gateway in human-machine interaction, yet universal design guidelines for tactile displays are still lacking, largely due to the absence of methods to measure sensibility across skin areas. In this study, we address this gap by developing and evaluating two fully automated vibrotactile tasks that require subjects to discriminate the position of vibrotactile stimuli using a two-interval forced-choice procedure (2IFC). Of the two methodologies, one was initially validated through a preliminary study involving 13 participants. Subsequently, we applied the validated and improved vibrotactile testing procedure to a larger sample of 23 participants, enabling a direct and valid comparison with static perception. Our findings reveal a significantly finer spatial acuity for static stimuli perception compared to vibrotactile stimuli perception from a stimulus separation of 15 mm onwards. This study introduces a novel method for generating both universal thresholds and individual person-specific data for vibratory perception, marking a critical step towards the development of functional vibrotactile displays. The results underline the need for further research in this area and provide a foundation for the development of universal design guidelines for tactile displays.
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