Some Considerations on Benchmarking of Wearable Robots for Mobility

  • Jan F. VenemanEmail author
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 16)


Wearable Robots for Mobility (WR-Mob), i.e. exoskeletons, are currently entering the market. This makes the topic of how to define and measure their performance more relevant and urgent. This abstract provides some considerations that could be taken into account when designing quantitative benchmark metrics that aim to quantify the performance of WR-Mob, focusing on measurement of reduction of metabolic cost and of improvement of balance. The considerations on metrics and their normalization are first steps to well-defined benchmark tests. Proper benchmarks contribute to solid comparison among devices that can be performed in different labs, and thus support a faster progress beyond the state of the art.


Metabolic Cost Technology Readiness Level Supportive Control Balance Project Maximum Perturbation 
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  1. 1.
    Torricelli, D., Gonzalez-Vargas, J., Veneman, J.F., Mombaur, K., Tsagarakis, N., del Ama, A.J., Gil-Agudo, A., Moreno, J.C., Pons, J.L.: Benchmarking bipedal locomotion: a unified scheme for humanoids, wearable robots, and humans. IEEE Robot. Autom. Mag. 22(3), 103–115 (2015)CrossRefGoogle Scholar
  2. 2.
    Brockway, J.M.: Derivation of formulae used to calculate energy expenditure in man. Hum. Nutr.: Clin. Nutr. 41C, 463–471 (1987)Google Scholar
  3. 3.
    Mooney, L.M., Rouse, E.J., Herr, H.M.: Autonomous exoskeleton reduces metabolic cost of human walking. J. Neuroeng. Rehabil. 11(1), 151 (2014)., doi:  10.1186/1743-0003-11-151
  4. 4.
    Sawicki, S., Ferris, D.P.: Mechanics and energetics of level walking with powered ankle exoskeletons. J. Exp. Biol. 211(Pt. 9), 1402–1413 (2008)CrossRefGoogle Scholar
  5. 5.
    Margaria, R.: Positive and negative work performances and their efficiencies in human locomotion. Int. Z Angew. Physiol. Einschl. Arbeitsphysiol. 25, 339–351 (1968)Google Scholar
  6. 6.
    Margaria, R.: Biomechanics and Energetics Of Muscular Exercise. Clarendon Press, Oxford (1976)Google Scholar
  7. 7.
    Vlutters, M., Van Asseldonk, E.H.F., Van der Kooij, H.: Center of mass velocity-based predictions in balance recovery following pelvis perturbations during human walking. J. Exp. Biol. 219(10), 1514–1523 (2016)CrossRefGoogle Scholar
  8. 8.
    Hof, A.L.: Scaling gait data to body size. Gait Posture 4, 222–223 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Health DivisionTECNALIA Research and InnovationDonostia-San SebastianSpain

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