Physical and behavioral comparison of haptic touchscreens quality
August 29, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Corentin Bernard, Nicolas Huloux, MichaΓ«l Wiertlewski, Jocelyn Monnoyer
arXiv ID
2308.15190
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Touchscreens equipped with friction modulation can provide rich tactile feedback to their users. To date, there are no standard metrics to properly quantify the benefit brought by haptic feedback.The definition of such metrics is not straightforward since friction modulation technologies can be achieved by either ultrasonic waves or with electroadhesion. In addition, the output depends strongly on the user, both because of the mechanical behavior of the fingertip and personal tactile somatosensory capabilities. This paper proposes a method to evaluate and compare the performance of haptic tablets on an objective scale. The method first defines multiple metrics using physical measurements of friction and latency. The comparison is completed with metrics derived from information theory and based on pointing tasks performed by users. We evaluated the comparison method with two haptic devices, one based on ultrasonic friction modulation and the other based on electroadhesion. This work paves the way toward the definitions of standard specifications for haptic tablets, to establish benchmarks and guidelines for improving surface haptic devices.
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