Presenting the Sense of Effort through Vibration Based on Force Estimated by Inverse Dynamics in Videos
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
Ryoma Akai, Masashi Konyo, Satoshi Tadokoro
arXiv ID
2411.05105
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
We present the sense of effort through vibration to help the video viewer understand how the person in the video moves the body. We suppose sense of effort is related to force, so we generate vibration based on force and present the sense of effort through the vibration. We use perceived intensity to make sense of effort proportional to vibration. In our demonstration, you can experience vibration while watching a video. We can create vibration on the spot, so you can experience vibration made from a video taken on the spot.
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