RT Journal Article
SR 00
ID 10.1002/aisy.202100045
A1 Rusu, Marius Mihai
A1 Schött, Svenja Yvonne
A1 Williamson, John H.
A1 Schmidt, Albrecht
A1 Murray-Smith, Roderick
T1 Low‐dimensional embeddings for interaction design
JF Advanced Intelligent Systems
YR 2022
FD 2022-02
VO 4
IS 2
K1 Autoencoders, data gloves, dimensionality reduction, embedding, interaction.
AB Physical interactions with the real world have many degrees of freedom, which has led to the development of novel input devices with a multitude of sensors to capture increasingly high‐dimensional data. This high dimensionality makes the design of interactive systems more complex. Herein, the use of autoencoder‐based dimensionality reduction is explored to simplify the design process. For this purpose, a data glove equipped with accelerometers is used to record high‐dimensional hand movement data that are thereafter reduced to 2D embeddings using autoencoders. The exploration and evaluation of the resulting embeddings suggest that autoencoders can be used to create meaningful low‐dimensional representations of complex human movement. The characteristics generality, variability, connectivity, and distinguishability are established and a guideline is provided for assessing low‐dimensional embeddings. Referring to these characteristics, system engineers can evaluate different input modalities and gestures for their specific interaction task. Further, a framework is outlined for designing and evaluating gesture interaction in the low‐dimensional space. By demonstrating the exemplary design of the interaction with a virtual lever, this research gives system engineers a template for interaction design in the low‐dimensional space.
PB Wiley
SN 2640-4567
LK https://eprints.gla.ac.uk/248397/