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Sensors 2013, 13(11), 14754-14763;

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

Health Sciences, Northeastern University, 316D Robinson Hall, 360 Huntington Ave., Boston, MA 02115, USA
Department of Kinesiology, University of Massachusetts, Amherst, MA 01003, USA
Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA
College of Osteopathic Medicine, University of New England, ME 04103, USA
Author to whom correspondence should be addressed.
Received: 17 September 2013 / Revised: 21 October 2013 / Accepted: 28 October 2013 / Published: 30 October 2013
(This article belongs to the Special Issue Wearable Gait Sensors)
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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. View Full-Text
Keywords: wearable activity monitors; raw acceleration; physical activity wearable activity monitors; raw acceleration; physical activity
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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John, D.; Sasaki, J.; Staudenmayer, J.; Mavilia, M.; Freedson, P.S. Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ Activity Monitors. Sensors 2013, 13, 14754-14763.

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