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5 Results for: "Wrist"

Daily physical activity patterns from hip- and wrist-worn accelerometers

  • Published on Sep 21, 2016

Accelerometer wear location may influence physical activity estimates. This study investigates this relationship through the examination of activity patterns throughout the day. Participants from the aging research evaluating accelerometry (AREA) study (n men  =  37, n women  =  47, mean age (SD)  =  78.9 (5.5) years) were ...


Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph.

  • Published on Aug 2016

Purpose: Sedentary behavior is an emerging independent health risk factor. The accuracy of measuring sedentary time using accelerometers may depend on the wear location. This study in older adults evaluated the accuracy of various hip- and wrist-worn ActiGraph accelerometer cutoff points to define sedentary time using the activPAL as the ...


Physical activity measurement considerations: Development and validation of new algorithms for wrist worn accelerometers

  • Presented on 2015

Abstract: This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high frequency wrist worn accelerometer data. The models were developed and tested on twenty participants (n=10 males, n=10 females, mean age= 24.1, mean BMI = 23.9) who wore an ActiGraph GT3X+ accelerometer on their dominant wrist ...


Methods to estimate aspects of physical activity and sedentary behavior from high frequency wrist accelerometer measurements

  • Published on June 25, 2015

Abstract: This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high frequency wrist worn accelerometer data. The models were developed and tested on twenty participants (n=10 males, n=10 females, mean age= 24.1, mean BMI = 23.9) who wore an Actigraph GT3X+ accelerometer on their dominant wrist ...


Machine Learning for Activity Recognition: Hip versus Wrist Data

  • Presented on June 18, 2013

Introduction: Wrist-worn accelerometers are convenient to wear and are associated with greater compliance. However, validated algorithms for predicting activity type and/or energy expenditure from wrist-worn accelerometer data are lacking. Purpose: To compare the activity recognition rates of an activity classifier trained on raw tri-axial acceleration signal (30 Hz) collected on ...