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

This paper proposes a method for using an accelerometer, microphone, and GPS in a mobile phone to recognize the movement of the user. Past attempts at identifying the movement associated with riding on a bicycle, train, bus or car and common human movements like standing still, walking or running have had problems with poor accuracy due to factors such as sudden changes in vibration or times when the vibrations resembled those for other types of movement. Moreover, previous methods have had problems with has the problem of high power consumption because of the sensor processing load. The proposed method aims to avoid these problems by estimating the reliability of the inference result, and by combining two inference modes to decrease the power consumption. Field trials demonstrate that our method achieves 90% or better average accuracy for the seven types of movement listed above. Shaka's power saving functionality enables us to extend the battery life of a mobile phone to over 100 hours while our estimation algorithm is running in the background. Furthermore, this paper uses experimental results to show the trade-off between accuracy and latency when estimating user activity.

Shaka: User Movement Estimation Considering Reliability, Power Saving, and Latency Using Mobile Phone