Publication

Efficient Evaluation of Coding Strategies for Transcutaneous Language Communication

Eurohaptics 2018


Abstract

Communication of natural language via the skin has seen renewed interest with the advent of mobile devices and wearable technology. Efficient evaluation of candidate haptic encoding algorithms remains a significant challenge. We present 4 algorithms along with our methods for evaluation, which are based on discriminability, learnability, and generalizability. Advantageously, mastery of an extensive vocabulary is not required. Haptic displays used 16 or 32 vibrotactile actuators arranged linearly or as a grid on the arm. In Study 1, a two-alternative, forced-choice protocol tested the ability of 10 participants to detect differences in word pairs encoded by 3 acoustic algorithms: Frequency Decomposition (FD), Dominant Spectral Peaks (DSP), and Autoencoder (AE). Detection specificity was not different among the algorithms, but sensitivity was significantly worse with AE than with FD or DSP. Study 2 compared the performance of 16 participants randomized to DSP vs a phoneme-based algorithm (PH) using a custom video game for training and testing. The PH group performed significantly better at all test stages, and showed better recognition and retention of words along with evidence of generalizability to new words.

Related Publications

All Publications

Federated Learning for User Privacy and Data Confidentiality Workshop At ICML - July 24, 2021

Federated Learning with Buffered Asynchronous Aggregation

John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek, Dzmitry Huba

ISMAR - July 29, 2021

Instant Visual Odometry Initialization for Mobile AR

Alejo Concha, Michael Burri, Jesus Briales, Christian Forster, Luc Oth

ICSA - November 6, 2019

Auralization systems for simulation of augmented reality experiences in virtual environments

Peter Dodds, Sebastià V. Amengual Garí, W. Owen Brimijoin, Philip W. Robinson

UAI - July 28, 2021

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization

Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy