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GENEAs, irrespective of wear location, accurately discriminated between all activity intensities (sedentary, light, moderate and vigorous) with the hip mounted monitor recording the largest area under the curve for each intensity (area under the curve=0.94-0.99).

Similar results were shown for concurrent validity (right: r=.830 left: r=.845 hip: r=.985, all p<0.01). The GENEA showed good criterion validity at both wrist locations (right: r=.900 left: r=.910, both p<0.01), although the hip-mounted monitor demonstrated significantly higher criterion validity (r=.965, p<0.05). Introduction The use of accelerometers to assess children's physical activity is by no means novel, and multiple devices have been validated for use within a child population, such as the Actical (Mini Mitter, Bend, OR, USA), the ActiGraph (ActiGraph, Pensacola, FL, USA) and the RT3 (, Monrovia, CA, USA). Pearson's r correlations were used to assess validity of the GENEA monitors at each location and location-specific activity intensity cut-points were established via Receiver Operator Characteristic curve analysis. ActiGraph output and V˙⁡O(2) were used for assessment of concurrent and criterion validity, respectively.

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The study was a lab-based validation and calibration study.įorty-four children, aged 8-14 years, completed eight activities (ranging from lying supine to a medium paced run) whilst wearing GENEA accelerometers at three locations (each wrist and at the right hip), an ActiGraph GT1M at the hip and a portable gas analyser.

We have successfully developed algorithms suitable for use with wrist-worn accelerometers for detecting certain types of physical activities the performance is comparable to waist-worn accelerometers for assessment of physical activity.The purpose of the study was to establish activity intensity cut-points for the GENEA accelerometer via calibration with oxygen consumption (V˙⁡O(2)). The computational results demonstrated that the algorithm we developed can accurately classify certain types of daily activities, with high overall classification accuracy for both waist-worn GENEA (0.99) and wrist-worn GENEA (right wrist = 0.97, left wrist = 0.96).

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Features obtained from both fast Fourier transform and wavelet decomposition were extracted, and machine learning algorithms were used to classify four types of daily activities including sedentary, household, walking, and running activities. ACTITRACK Software for locomotor activity, exploration and anxiety Email Print Description Key features Specs How to order Publications Description ActiTrack controls data coming from up to 32 IR frames. Acceleration data were collected at 80 Hz.

Throughout, three GENEA accelerometers were worn: one at the waist, one on the left wrist, and one on the right wrist. Inset right: Overhead view of GENEA accelerometers positioned in the test jig Inset left: Multi-Axis Shaker Table (MAST) complete with the test jig secured in place Main Figure: Comparison of a.

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m⁻²) completed an ordered series of 10-12 semistructured activities in the laboratory and outdoor environment.

Sixty participants (age = 49.4 ± 6.5 yr, body mass index = 24.6 ± 3.4 kg The purpose of this study was to develop methods to classify physical activities into walking, running, household, or sedentary activities based on raw acceleration data from the GENEA (Gravity Estimator of Normal Everyday Activity) and compare classification accuracy from a wrist-worn GENEA with a waist-worn GENEA. Output is in arbitrary counts that can be classified by activity intensity according to published thresholds. Most accelerometer-based activity monitors are worn on the waist or lower back for assessment of habitual physical activity.

Genea actimeter