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/

Zendy Plus

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

Zendy Tools

Zendy Open

Unsupervised self-rehabilitation exercises and physical training can causeserious injuries if performed incorrectly. We introduce a learning-basedframework that identifies the mistakes made by a user and proposes correctivemeasures for easier and safer individual training. Our framework does not relyon hard-coded, heuristic rules. Instead, it learns them from data, whichfacilitates its adaptation to specific user needs. To this end, we use a GraphConvolutional Network (GCN) architecture acting on the user's pose sequence tomodel the relationship between the body joints trajectories. To evaluate ourapproach, we introduce a dataset with 3 different physical exercises. Ourapproach yields 90.9% mistake identification accuracy and successfully corrects94.2% of the mistakes.

3D Pose Based Feedback for Physical Exercises