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

The last few decades have witnessed an emerging trend of wearable softsensors; however, there are important signal-processing challenges for softsensors that still limit their practical deployment. They are error-prone whendisplaced, resulting in significant deviations from their ideal sensor output.In this work, we propose a novel prototype that integrates an elbow pad with asparse network of soft sensors. Our prototype is fully bio-compatible,stretchable, and wearable. We develop a learning-based method to predict theelbow orientation angle and achieve an average tracking error of 9.82 degreesfor single-user multi-motion experiments. With transfer learning, our methodachieves the average tracking errors of 10.98 degrees and 11.81 degrees acrossdifferent motion types and users, respectively. Our core contributions lie in asolution that realizes robust and stable human joint motion tracking acrossdifferent device displacements.

DisPad: Flexible On-Body Displacement of Fabric Sensors for Robust Joint-Motion Tracking