English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We outline the role of forward and inverse modelling approaches in the designof human--computer interaction systems. Causal, forward models tend to beeasier to specify and simulate, but HCI requires solutions of the inverseproblem. We infer finger 3D position $(x,y,z)$ and pose (pitch and yaw) on amobile device using capacitive sensors which can sense the finger up to 5cmabove the screen. We use machine learning to develop data-driven models toinfer position, pose and sensor readings, based on training data from: 1. datagenerated by robots, 2. data from electrostatic simulators 3. human-generateddata. Machine learned emulation is used to accelerate the electrostaticsimulation performance by a factor of millions. We combine a ConditionalVariational Autoencoder with domain expertise/models experimentally collecteddata. We compare forward and inverse model approaches to direct inference offinger pose. The combination gives the most accurate reported results oninferring 3D position and pose with a capacitive sensor on a mobile device.

Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose