76
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Simple Exoskeleton That Assists Plantarflexion Can Reduce the Metabolic Cost of Human Walking

      research-article
      * , , ,
      PLoS ONE
      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Even though walking can be sustained for great distances, considerable energy is required for plantarflexion around the instant of opposite leg heel contact. Different groups attempted to reduce metabolic cost with exoskeletons but none could achieve a reduction beyond the level of walking without exoskeleton, possibly because there is no consensus on the optimal actuation timing. The main research question of our study was whether it is possible to obtain a higher reduction in metabolic cost by tuning the actuation timing.

          Methodology/Principal Findings

          We measured metabolic cost by means of respiratory gas analysis. Test subjects walked with a simple pneumatic exoskeleton that assists plantarflexion with different actuation timings. We found that the exoskeleton can reduce metabolic cost by 0.18±0.06 W kg −1 or 6±2% (standard error of the mean) (p = 0.019) below the cost of walking without exoskeleton if actuation starts just before opposite leg heel contact.

          Conclusions/Significance

          The optimum timing that we found concurs with the prediction from a mathematical model of walking. While the present exoskeleton was not ambulant, measurements of joint kinetics reveal that the required power could be recycled from knee extension deceleration work that occurs naturally during walking. This demonstrates that it is theoretically possible to build future ambulant exoskeletons that reduce metabolic cost, without power supply restrictions.

          Related collections

          Most cited references34

          • Record: found
          • Abstract: found
          • Article: not found

          Meaningful change and responsiveness in common physical performance measures in older adults.

          To estimate the magnitude of small meaningful and substantial individual change in physical performance measures and evaluate their responsiveness. Secondary data analyses using distribution- and anchor-based methods to determine meaningful change. Secondary analysis of data from an observational study and clinical trials of community-dwelling older people and subacute stroke survivors. Older adults with mobility disabilities in a strength training trial (n=100), subacute stroke survivors in an intervention trial (n=100), and a prospective cohort of community-dwelling older people (n=492). Gait speed, Short Physical Performance Battery (SPPB), 6-minute-walk distance (6MWD), and self-reported mobility. Most small meaningful change estimates ranged from 0.04 to 0.06 m/s for gait speed, 0.27 to 0.55 points for SPPB, and 19 to 22 m for 6MWD. Most substantial change estimates ranged from 0.08 to 0.14 m/s for gait speed, 0.99 to 1.34 points for SPPB, and 47 to 49 m for 6MWD. Based on responsiveness indices, per-group sample sizes for clinical trials ranged from 13 to 42 for substantial change and 71 to 161 for small meaningful change. Best initial estimates of small meaningful change are near 0.05 m/s for gait speed, 0.5 points for SPPB, and 20 m for 6MWD and of substantial change are near 0.10 m/s for gait speed, 1.0 point for SPPB, and 50 m for 6MWD. For clinical use, substantial change in these measures and small change in gait speed and 6MWD, but not SPPB, are detectable. For research use, these measures yield feasible sample sizes for detecting meaningful change.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A new predictive equation for resting energy expenditure in healthy individuals.

            A predictive equation for resting energy expenditure (REE) was derived from data from 498 healthy subjects, including females (n = 247) and males (n = 251), aged 19-78 y (45 +/- 14 y, mean +/- SD). Normal-weight (n = 264) and obese (n = 234) individuals were studied and REE was measured by indirect calorimetry. Multiple-regression analyses were employed to drive relationships between REE and weight, height, and age for both men and women (R2 = 0.71): REE = 9.99 x weight + 6.25 x height - 4.92 x age + 166 x sex (males, 1; females, 0) - 161. Simplification of this formula and separation by sex did not affect its predictive value: REE (males) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) + 5; REE (females) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) - 161. The inclusion of relative body weight and body-weight distribution did not significantly improve the predictive value of these equations. The Harris-Benedict Equations derived in 1919 overestimated measured REE by 5% (p less than 0.01). Fat-free mass (FFM) was the best single predictor of REE (R2 = 0.64): REE = 19.7 x FFM + 413. Weight also was closely correlated with REE (R2 = 0.56): REE = 15.1 x weight + 371.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Derivation of formulae used to calculate energy expenditure in man.

              W Brockway (1987)
              The origins of the data used to construct some of the formulae in current usage for the calculation of energy expenditure are discussed. The differences in expenditure calculated by the various formulae cover a range of about 3 per cent. This error is large in relation to long-term studies of energy balance, and to the accuracy attainable with modern respiration chambers. The differences stem in part from the use of inappropriate original values and in part from errors in arithmetic. A new set of source data and a derived formula are presented.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                13 February 2013
                : 8
                : 2
                : e56137
                Affiliations
                [1]Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
                University of Zurich, Switzerland
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PM WD SG DDC. Performed the experiments: PM SG. Analyzed the data: PM WD SG DDC. Contributed reagents/materials/analysis tools: PM WD SG. Wrote the paper: PM WD SG DDC.

                Article
                PONE-D-12-25626
                10.1371/journal.pone.0056137
                3571952
                23418524
                bae6617c-046c-4376-ad60-b39a6808cd6e
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 13 August 2012
                : 5 January 2013
                Page count
                Pages: 7
                Funding
                This research was funded by the Ghent University (it was conducted during the appointment of the corresponding author as an assistant/PhD student). There was no external funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Anatomy and Physiology
                Musculoskeletal System
                Biomechanics
                Bone and Joint Mechanics
                Exertion
                Biophysics
                Biomechanics
                Bone and Joint Mechanics
                Biotechnology
                Bioengineering
                Bionics
                Engineering
                Bioengineering
                Bionics
                Medicine
                Anatomy and Physiology
                Musculoskeletal System
                Biomechanics
                Robotics
                Physiological Processes
                Energy Metabolism
                Physics
                Biophysics
                Biomechanics
                Bone and Joint Mechanics

                Uncategorized
                Uncategorized

                Comments

                Comment on this article