Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly

Amy Papadopoulos; Nicolas Vivaldi; Cindy Crump; Christine Tsien Silvers

doi:doi:10.11648/j.ijbse.20140205.11

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Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly

Amy Papadopoulos, Nicolas Vivaldi, Cindy Crump, Christine Tsien Silvers

Published in International Journal of Biomedical Science and Engineering (Volume 2, Issue 5)

Received: 16 October 2014 Accepted: 29 October 2014 Published: 10 November 2014

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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.

Published in	International Journal of Biomedical Science and Engineering (Volume 2, Issue 5)
DOI	10.11648/j.ijbse.20140205.11
Page(s)	38-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Accelerometer, Activity Level, Differential Signal Magnitude, MET Values, Metabolic Equivalent of Task, Physical Activity Level, Tri-Axial Accelerometer, Wearable Sensors

References

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[14]	D. Karantonis, M. Narayanan, M. Mathie, N. Lovell, and B. Cellar, “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring,” IEEE Transactions on Information Technology in Biomedicine, vol. 10(1), pp. 156-167, 2006.
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APA Style

Amy Papadopoulos, Nicolas Vivaldi, Cindy Crump, Christine Tsien Silvers. (2014). Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly. International Journal of Biomedical Science and Engineering, 2(5), 38-44. https://doi.org/10.11648/j.ijbse.20140205.11

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ACS Style

Amy Papadopoulos; Nicolas Vivaldi; Cindy Crump; Christine Tsien Silvers. Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly. Int. J. Biomed. Sci. Eng. 2014, 2(5), 38-44. doi: 10.11648/j.ijbse.20140205.11

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AMA Style

Amy Papadopoulos, Nicolas Vivaldi, Cindy Crump, Christine Tsien Silvers. Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly. Int J Biomed Sci Eng. 2014;2(5):38-44. doi: 10.11648/j.ijbse.20140205.11

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@article{10.11648/j.ijbse.20140205.11,
  author = {Amy Papadopoulos and Nicolas Vivaldi and Cindy Crump and Christine Tsien Silvers},
  title = {Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {2},
  number = {5},
  pages = {38-44},
  doi = {10.11648/j.ijbse.20140205.11},
  url = {https://doi.org/10.11648/j.ijbse.20140205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20140205.11},
  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.},
 year = {2014}
}

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TY  - JOUR
T1  - Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly
AU  - Amy Papadopoulos
AU  - Nicolas Vivaldi
AU  - Cindy Crump
AU  - Christine Tsien Silvers
Y1  - 2014/11/10
PY  - 2014
N1  - https://doi.org/10.11648/j.ijbse.20140205.11
DO  - 10.11648/j.ijbse.20140205.11
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 38
EP  - 44
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20140205.11
AB  - 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.
VL  - 2
IS  - 5
ER  -

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Author Information

Amy Papadopoulos

AFrame Digital, Inc., Vienna, VA, USA
Nicolas Vivaldi

AFrame Digital, Inc., Vienna, VA, USA
Cindy Crump

AFrame Digital, Inc., Vienna, VA, USA
Christine Tsien Silvers

AFrame Digital, Inc., Vienna, VA, USA

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Plain Text BibTeX RIS

APA Style

Amy Papadopoulos, Nicolas Vivaldi, Cindy Crump, Christine Tsien Silvers. (2014). Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly. International Journal of Biomedical Science and Engineering, 2(5), 38-44. https://doi.org/10.11648/j.ijbse.20140205.11

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ACS Style

Amy Papadopoulos; Nicolas Vivaldi; Cindy Crump; Christine Tsien Silvers. Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly. Int. J. Biomed. Sci. Eng. 2014, 2(5), 38-44. doi: 10.11648/j.ijbse.20140205.11

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AMA Style

Amy Papadopoulos, Nicolas Vivaldi, Cindy Crump, Christine Tsien Silvers. Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly. Int J Biomed Sci Eng. 2014;2(5):38-44. doi: 10.11648/j.ijbse.20140205.11

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@article{10.11648/j.ijbse.20140205.11,
  author = {Amy Papadopoulos and Nicolas Vivaldi and Cindy Crump and Christine Tsien Silvers},
  title = {Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {2},
  number = {5},
  pages = {38-44},
  doi = {10.11648/j.ijbse.20140205.11},
  url = {https://doi.org/10.11648/j.ijbse.20140205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20140205.11},
  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.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly
AU  - Amy Papadopoulos
AU  - Nicolas Vivaldi
AU  - Cindy Crump
AU  - Christine Tsien Silvers
Y1  - 2014/11/10
PY  - 2014
N1  - https://doi.org/10.11648/j.ijbse.20140205.11
DO  - 10.11648/j.ijbse.20140205.11
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 38
EP  - 44
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20140205.11
AB  - 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.
VL  - 2
IS  - 5
ER  -

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