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Accuracy of Posture Allocation Algorithms for Thigh- and Waist-Worn Accelerometers
Charlotte L Edwardson1, Alex V Rowlands, Sarah Bunnewell
11Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, England, UNITED KINGDOM; 2NIHR Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit, Leicester, England, UNITED KINGDOM; 3Alliance for Research in Exercise, Nutrition and Activity, Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Adelaide, AUSTRALIA; 4National Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, England, UNITED KINGDOM; 5School of Health Sciences, Stirling University, Stirling, Scotland, UNITED KINGDOM; 6University Hospitals of Leicester, Leicester General Hospital, Leicester, England, UNITED KINGDOM; and 7NIHR Collaboration for Leadership in Applied Health Research and Care, East Midlands, Leicester General Hospital, UNITED KINGDOM.
Purpose:
The objective of this study is to compare the accuracy of the activPAL and ActiGraph GT3X+ (waist and thigh) proprietary postural allocation algorithms and an open-source postural allocation algorithm applied to GENEActiv (thigh) and ActiGraph GT3X+ (thigh) data.
Methods:
Thirty-four adults (≥18 yr) wore the activPAL3, GENEActiv, and ActiGraph GT3X+ on the right thigh and an ActiGraph on the right hip while performing four lying, seven sitting, and five upright activities in the laboratory. Lying and sitting tasks incorporated a range of leg angles (e.g., lying with legs bent and sitting with legs crossed). Each activity was performed for 5 min while being directly observed. The percentage of the time the posture was correctly classified was calculated.
Results:
Participants consisted of 14 males and 20 females (mean age, 27.2 ± 5.9 yr; mean body mass index, 23.8 ± 3.7 kg·m). All postural allocation algorithms applied to monitors worn on the thigh correctly classified ≥93% of the time lying, ≥91% of the time sitting, and ≥93% of the time upright. The ActiGraph waist proprietary algorithm correctly classified 72% of the time lying, 58% of the time sitting, and 74% of the time upright. Both the activPAL and ActiGraph thigh proprietary algorithms misclassified sitting on a chair with legs stretched out (58% and 5% classified incorrectly, respectively). The ActiGraph thigh proprietary and open-source algorithm applied to the thigh-worn ActiGraph misclassified participants lying on their back with their legs bent 27% and 9% of the time, respectively.
Conclusion:
All postural allocation algorithms when applied to devices worn on the thigh were highly accurate in identifying lying, sitting, and upright postures. Given the poor accuracy of the waist algorithm for detecting sitting, caution should be taken if inferring sitting time from a waist-worn device.
