Effect of wearable sensor dynamics on physical activity estimates: A comparison between SCI vs. healthy individuals

C. Jayaraman, C. K. Mummidisetty, A. Jayaraman*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Accuracy of physical activity estimates predicted by activity monitoring technologies may be affected by device location, analysis algorithms, type of technology (i.e. wearable/stickable) and population demographics (disability) being studied. Consequently, the main purpose of this investigation was to study such sensor dynamics (i.e. effect of device location, type and population demographics on energy expenditure estimates) of two commercial activity monitors. It was hypothesized that device location, population studied (disability), choice of proprietary algorithm and type of technology used will significantly impact the accuracy of the predicted physical activity metrics. 10 healthy controls and eight individuals with spinal cord injury (SCI) performed structured activities in a laboratory environment. All participants wore, (i) three ActiGraph-G3TX's one each on their wrist, waist & ankle, (ii) a stickable activity monitor (Metria-IH1) on their upper-arm and (3) a Cosmed-K4B2 metabolic unit, while performing sedentary (lying), low intensity (walk 50 steps at self-speed) and vigorous activity (a 6 minute walk test). To validate the hypothesis, the energy expenditures (EE) predicted by ActiGraph-GT3X and Metria-IH1 were benchmarked with estimated EE per Cosmed K4B2 metabolic unit. To verify the step count accuracy predicted by ActiGraph-GT3X's and Metria-IH1, the manually calculated step count during the low intensity activity were compared to estimates from both devices. Results suggest that Metria-IH1 out-performed ActiGraph-GT3X in estimating EE during sedentary activity in both groups. The device location and population demographics, significantly affected the accuracy of predicted estimates. In conclusion, selecting activity monitor locations, analysis algorithm and choice of technology plays based on the movement threshold of population being studied can pave a better way for reliable healthcare decisions and data analytics in population with SCI.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3282-3285
Number of pages4
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2016-October
ISSN (Print)1557-170X

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period8/16/168/20/16

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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