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Original article| Volume 93, ISSUE 11, P1937-1943, November 2012

Predicting Energy Expenditure of Manual Wheelchair Users With Spinal Cord Injury Using a Multisensor-Based Activity Monitor

  • Shivayogi V. Hiremath
    Affiliations
    Department of Veterans Affairs (VA), Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA
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  • Dan Ding
    Correspondence
    Correspondence to Dan Ding, PhD, Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, 6425 Penn Ave, Pittsburgh, PA 15206
    Affiliations
    Department of Veterans Affairs (VA), Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
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  • Jonathan Farringdon
    Affiliations
    BodyMedia Inc, Pittsburgh, PA
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  • Rory A. Cooper
    Affiliations
    Department of Veterans Affairs (VA), Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author

      Abstract

      Hiremath SV, Ding D, Farringdon J, Cooper RA. Predicting energy expenditure of manual wheelchair users with spinal cord injury using a multisensor-based activity monitor.

      Objective

      To develop and evaluate new energy expenditure (EE) prediction models for manual wheelchair users (MWUs) with spinal cord injury (SCI) based on a commercially available multisensor-based activity monitor.

      Design

      Cross-sectional.

      Setting

      Laboratory.

      Participants

      Volunteer sample of MWUs with SCI (N=45).

      Intervention

      Subjects were asked to perform 4 activities including resting, wheelchair propulsion, arm-ergometer exercise, and deskwork. Criterion EE using a metabolic cart and raw sensor data from a multisensor activity monitor was collected during each of these activities.

      Main Outcome Measures

      Two new EE prediction models including a general model and an activity-specific model were developed using enhanced all-possible regressions on 36 MWUs and tested on the remaining 9 MWUs.

      Results

      The activity-specific and general EE prediction models estimated the EE significantly better than the manufacturer's model. The average EE estimation error using the manufacturer's model and the new general and activity-specific models for all activities combined was –55.31% (overestimation), 2.30% (underestimation), and 4.85%, respectively. The average EE estimation error using the manufacturer's model, the new general model, and activity-specific models for various activities varied from –19.10% to –89.85%, –18.13% to 25.13%, and –4.31% to 9.93%, respectively.

      Conclusions

      The predictors for the new models were based on accelerometer and demographic variables, indicating that movement and subject parameters were necessary in estimating the EE. The results indicate that the multisensor activity monitor with new prediction models can be used to estimate EE in MWUs with SCI during wheelchair-related activities mentioned in this study.

      Key Words

      List of Abbreviations:

      EE (energy expenditure), MAE (mean absolute error), MSE (mean signed error), MWU (manual wheelchair user), PA (physical activity), SCI (spinal cord injury)
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