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

Human-machine systems required a deep understanding of human behaviors. Mostexisting research on action recognition has focused on discriminating betweendifferent actions, however, the quality of executing an action has receivedlittle attention thus far. In this paper, we study the quality assessment ofdriving behaviors and present WiQ, a system to assess the quality of actionsbased on radio signals. This system includes three key components, a deepneural network based learning engine to extract the quality information fromthe changes of signal strength, a gradient based method to detect the signalboundary for an individual action, and an activitybased fusion policy toimprove the recognition performance in a noisy environment. By using thequality information, WiQ can differentiate a triple body status with anaccuracy of 97%, while for identification among 15 drivers, the averageaccuracy is 88%. Our results show that, via dedicated analysis of radiosignals, a fine-grained action characterization can be achieved, which canfacilitate a large variety of applications, such as smart driving assistants.

Qualitative Action Recognition by Wireless Radio Signals in Human-Machine Systems