In-ear accelerometer-based sensor for gait classification

  • Authors
  • Piris C, Gartner L, González Ballester MA, Noailly J, Stöcker F, Schönfelder M, Adams T, Tassani S
  • UPF authors
  • GONZALEZ BALLESTER, MIGUEL ANGEL; NOAILLY ., JÉRÔME BERNARD; TASSANI ., SIMONE;
  • Type
  • Scholarly articles
  • Journal títle
  • IEEE Sensors Journal
  • Publication year
  • 2020
  • Volume
  • 20
  • Number
  • 21
  • Pages
  • 12895-12902
  • ISSN
  • 1530-437X
  • Publication State
  • Published
  • Abstract
  • apos;, which contains a three-dimensional accelerometer sensor. The main characteristics between these two activities were detected using 17 time domain features, as for instance the maximums and standard deviations of the 3-axes, and 3 different window sizes were evaluated: 3.75s, 2s and 1s. Support vector machine (SVM) and k -nearest neighbors (KNN) classifiers were implemented and later compared. The total number of features was reduced to 6 for SVM and 12 for KNN preserving the same results. An accuracy over 99% for both classifiers was achieved, outperforming most of the previous studies.
  • Complete citation
  • Piris C, Gartner L, González Ballester MA, Noailly J, Stöcker F, Schönfelder M, Adams T, Tassani S. In-ear accelerometer-based sensor for gait classification. IEEE Sensors Journal 2020; 20(21): 12895-12902.
Bibliometric indicators
  • 4 times cited Scopus
  • 4 times cited WOS
  • Índex Scimago de 0.681(2020)