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

Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice,are the primary healthcare cost drivers in developed countries. Pervasivecomputational, sensing, and communication technology provided by smartphonesand smartwatches have made it possible to support individuals in their everydaylives to develop healthier lifestyles. In this paper, we propose an exerciserecommendation system that also predicts individual success rates. The system,consisting of two inter-connected recurrent neural networks (RNNs), uses thehistory of workouts to recommend the next workout activity for each individual.The system then predicts the probability of successful completion of thepredicted activity by the individual. The prediction accuracy of thisinterconnected-RNN model is assessed on previously published data from afour-week mobile health experiment and is shown to improve upon previouspredictions from a computational cognitive model.

Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks