Build a training interface to install the bat's echolocation skills in humans
February 17, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Miyoko Tsumaki, Yu Teshima, Takao Tsuchiya, Kaoru Ashihara, Kohta I. Kobayasi, Shizuko Hiryu
arXiv ID
2302.08794
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.SD,
eess.AS
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Bats use a sophisticated ultrasonic sensing method called echolocation to recognize the environment. Recently, it has been reported that sighted human participants with no prior experience in echolocation can improve their ability to perceive the spatial layout of various environments through training to listen to echoes (Norman, et al., 2021). In this study, we developed the new training system for human echolocation using the eye-tracker. Binaural echoes of consecutive downward linear FM pulses that were inspired by feeding strategies of echolocating bats were simulated using the wave equation finite difference time domain method. The virtual echoes were presented to the sighted subject in response to his or her eye movements on the monitor. The latency from eye gazing to the echo presentation wasn't audible delay to perceive. In a preliminary experiment in which the participants were asked to identify the shapes of the hidden target, the participants were found to concentrate their gaze on the edges of the hidden target on the monitor. We will conduct a psycho-acoustical experiment to examine the learning process of human echolocation in a shape-identification task, which will lead to device development in the field of welfare engineering.
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