Volume 2, Issue 5, October 2014, Page: 38-44
Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly
Amy Papadopoulos, AFrame Digital, Inc., Vienna, VA, USA
Nicolas Vivaldi, AFrame Digital, Inc., Vienna, VA, USA; Department of Biomedical Engineering, George Washington University, Washington, DC, USA
Cindy Crump, AFrame Digital, Inc., Vienna, VA, USA; The Center for Study of Chronic Illness and Disability, George Mason University, Fairfax, VA, USA
Christine Tsien Silvers, AFrame Digital, Inc., Vienna, VA, USA; Children’s Hospital Informatics Program, Children’s Hospital, Boston, MA, USA
Received: Oct. 16, 2014;       Accepted: Oct. 29, 2014;       Published: Nov. 10, 2014
DOI: 10.11648/j.ijbse.20140205.11      View  3231      Downloads  191
Abstract
Estimating physical activity in the elderly from wrist-gathered acceleration data was studied. Thirty individuals (65+ years) were video-recorded while wearing a wrist device and going about their normal activities within their regular living environment for four hours each. Acceleration data were summarized into an activity value [via the “differential signal magnitude” (DSM) method] and compared to metabolic equivalent of task (MET) values determined by video analysis for each time period (“epoch”). Different sampling rates and epoch sizes were evaluated. Sampling at 4 Hz and using 60-second epochs provided the best results, with a moderate Pearson’s correlation coefficient of 0.58 between DSM activity values and MET values. The area under the receiver operating characteristic curve (AUC) for classifying each minute of data as active (MET >= 2.0) versus moderately active (MET > 1.2 and < 2.0) was 0.87 (sensitivity 80%, specificity 79%). DSM activity values (sampling at 4 Hz) were compared to the widely known signal magnitude area (SMA) values (requiring low-pass filtering and sampling at 40 Hz), with an excellent correlation of 0.994.
Keywords
Accelerometer, Activity Level, Differential Signal Magnitude, MET Values, Metabolic Equivalent of Task, Physical Activity Level, Tri-Axial Accelerometer, Wearable Sensors
To cite this article
Amy Papadopoulos, Nicolas Vivaldi, Cindy Crump, Christine Tsien Silvers, Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly, International Journal of Biomedical Science and Engineering. Vol. 2, No. 5, 2014, pp. 38-44. doi: 10.11648/j.ijbse.20140205.11
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