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

Event detection is a critical feature in data-driven systems as it assistswith the identification of nominal and anomalous behavior. Event detection isincreasingly relevant in robotics as robots operate with greater autonomy inincreasingly unstructured environments. In this work, we present an accurate,robust, fast, and versatile measure for skill and anomaly identification. Atheoretical proof establishes the link between the derivative of thelog-likelihood of the HMM filtered belief state and the latest emissionprobabilities. The key insight is the inverse relationship in which gradientanalysis is used for skill and anomaly identification. Our measure showedbetter performance across all metrics than related state-of-the art works. Theresult is broadly applicable to domains that use HMMs for event detection.

Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures