Research Study Abstract

Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ Activity Monitors

  • Published on Oct. 30, 2013

Purpose To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors.

Methods A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model.

Results GENEA produced significantly higher (p < 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p < 0.05) prediction accuracy. Prediction accuracy was not compromised when interchangeably using FD models between monitors.

Conclusions It may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration.


  • Dinesh John 1
  • Jeffer Sasaki 2
  • John Staudenmayer 3
  • Marianna Mavilia 4
  • Patty S. Freedson 2


  • 1

    Health Sciences, Northeastern University, 316D Robinson Hall, 360 Huntington Ave., Boston, MA 02115, USA

  • 2

    Department of Kinesiology, University of Massachusetts, Amherst, MA 01003, USA

  • 3

    Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA

  • 4

    College of Osteopathic Medicine, University of New England, ME 04103, USA