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Lower Limb Wearable Capacitive Sensing and Its Applications to Recognizing Human Gaits
Sensors 2013, 13(11), 14754-14763; doi:10.3390/s131114754

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

1,* , 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
* 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.
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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