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Updated: Mar 8, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
Published on: April 21, 2017
Accelerometer wear-site detection: When one site does not suit all, all of the time
Alex V Rowlands1, Tim S Olds2, Kishan Bakrania3
1Diabetes Research Centre, University of Leicester, Leicester Diabetes Centre, Leicester General Hospital, United Kingdom; National Institute for Health Research (NIHR) Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit (BRU), Leicester Diabetes Centre, Leicester General Hospital, United Kingdom; Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Australia.
Objectives:
Choice of accelerometer wear-site may facilitate greater compliance in research studies. We aimed to test whether a simple method could automatically discriminate whether an accelerometer was worn on the hip or wrist from free-living data.
Design:
Cross-sectional.
Methods:
Twenty-two 10-12y old children wore a GENEActiv at the wrist and at the hip for 7-days. The angle between the forearm and the total acceleration vector for the wrist-worn monitor and between the pelvis and the total acceleration vector for the hip-worn monitor (i.e. the angle between the Y-axis component of the acceleration and the total acceleration vector) was calculated for each 5s epoch. The standard deviation of this angle (SDangle) was calculated over time for the wrist-worn and hip-worn monitor for windows of varying lengths. We hypothesised that the wrist angle would be more variable than the hip angle.
Results:
Wear site could be discriminated based on SDangle; the shorter the time window the lower the optimal threshold and Area under the Receiver-Operating-Characteristic curve (AUROC) for discrimination of wear-site (AUROC=0.833 (1min) - 0.952 (12h)). Classification accuracy was good for windows of 8min (sensitivity=90%, specificity=87%, AUROC=0.92) and plateaued for windows of ≥60min (sensitivity and specificity >90%, AUROC=0.95-0.96).
Conclusions:
We have presented a robust, computationally simple method that detects whether an accelerometer is being worn on the hip or wrist from 8 to 60min of data. This facilitates the use of wear-site specific algorithms to analyse accelerometer data.
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