Long-term activity recognition from wristwatch accelerometer data

Clicks: 545
ID: 111213
2014
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #22 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user's needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to men …
Reference Key
e2014sensorslong-term Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Garcia-Ceja E;Brena RF;Carrasco-Jimenez JC;Garrido L;;
Journal sensors
Year 2014
DOI
DOI not found
URL
https://www.pubmed.gov/?cmd=Search&term=Jose%20C.%20Carrasco-Jimenez
Keywords
artificial intelligence Monitoring National Center for Biotechnology Information NCBI NLM MEDLINE humans pubmed abstract nih national institutes of health national library of medicine models statistical reproducibility of results research support non-u.s. gov't algorithms enrique garcia-ceja sensitivity and specificity time factors pmid:25436652 pmc4299024 doi:10.3390/s141222500 ramon f brena leonardo garrido accelerometry / methods* actigraphy / methods* computer simulation data interpretation longitudinal studies markov chains ambulatory / methods* motor activity / physiology* pattern recognition automated / methods*

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.