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

Wrist-bands such as smartwatches have become an unobtrusive interface forcollecting physiological and contextual data from users. Smartwatches are beingused for smart healthcare, telecare, and wellness monitoring. In this paper, weused data collected from the AnEAR framework leveraging smartwatches to gatherand store physiological data from patients in naturalistic settings. This dataincluded temperature, galvanic skin response (GSR), acceleration, and heartrate (HR). In particular, we focused on HR and acceleration, as these twomodalities are often correlated. Since the data was unlabeled we relied onunsupervised learning for multi-modal signal analysis. We propose using k-meansclustering, GMM clustering, and Self-Organizing maps based on Neural Networksfor group the multi-modal data into homogeneous clusters. This strategy helpedin discovering latent structures in our data.

SmartEAR: Smartwatch-based Unsupervised Learning for Multi-modal Signal Analysis in Opportunistic Sensing Framework