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 compiled a demo application and collected a motion database of more than10,000 smartphone users to produce a health risk model trained on physicalactivity streams. We turned to adversarial domain adaptation and employed theUK Biobank dataset of motion data, augmented by a rich set of clinicalinformation as the source domain to train the model using a deep residualconvolutional neuron network (ResNet). The model risk score is a biomarker ofageing, since it was predictive of lifespan and healthspan (as defined by theonset of specified diseases), and was elevated in groups associated withlife-shortening lifestyles, such as smoking. We ascertained the target domainperformance in a smaller cohort of the mobile application that included userswho were willing to share answers to a short questionnaire related to theirdisease and smoking status. We thus conclude that the proposed pipelinecombining deep convolutional and Domain Adversarial neuron networks (DANN) is apowerful tool for disease risk and lifestyle-associated hazard assessment frommobile motion sensors that are transferable across devices and populations.

A novel health risk model based on intraday physical activity time series collected by smartphones