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A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

Published online by Cambridge University Press:  22 April 2015

B. Povse
R & D department for automation, robotics and electronic instrumentation, Dax Electronic systems Company, Vreskovo 68, Trbovlje, Slovenia. E-mail:
S. Haddadin*
Institute of Automatic Control, Leibniz Universität Hannover, Hanover, Germany
R. Belder
Robotics and Mechatronics Center, DLR, Oberpfaffenhofen, Germany
D. Koritnik
R & D department for automation, robotics and electronic instrumentation, Dax Electronic systems Company, Vreskovo 68, Trbovlje, Slovenia. E-mail:
T. Bajd
Laboratory of Robotics, Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, Ljubljana, Slovenia
*Corresponding author. E-mail:


This paper treats the systematic injury analysis of lower arm robot–human impacts. For this purpose, a passive mechanical lower arm (PMLA) was developed that mimics the human impact response and is suitable for systematic impact testing and prediction of mild contusions and lacerations. A mathematical model of the passive human lower arm is adopted to the control of the PMLA. Its biofidelity is verified by a number of comparative impact experiments with the PMLA and a human volunteer. The respective dynamic impact responses show very good consistency and support the fact that the developed device may serve as a human substitute in safety analysis for the described conditions. The collision tests were performed with two different robots: the DLR Lightweight Robot III (LWR-III) and the EPSON PS3L industrial robot. The data acquired in the PMLA impact experiments were used to encapsulate the results in a robot independent safety curve, taking into account robot's reflected inertia, velocity and impact geometry. Safety curves define the velocity boundaries on robot motions based on the instantaneous manipulator dynamics and possible human injury due to unforeseen impacts.

Copyright © Cambridge University Press 2015 

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1. Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T. and Hirzinger, G., “The DLR lightweight robot – lightweight design and soft robotics control concepts for robots in human environments,” Indust. Robot J. 34 (5), 376385 (2007).Google Scholar
2. Townsend, W. and Salisbury, J., “Mechanical Design for Whole-Arm Manipulation,” In: Robots and Biological Systems: Towards a New Bionics? (Dario, P., Sandini, G. and Aebischer, P., eds.) (Springer, Berlin Heidelberg, 1993) pp. 153164.Google Scholar
3. Fitzgerald, C., “Developing Baxter,” IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, MA, USA (Apr. 2013) pp. 1–6.Google Scholar
4. Hirzinger, G., Butterfaß, J., Fischer, M., Grebenstein, M., Hahnle, M., Liu, H., Schäfer, I. and Sporer, N., “A Mechatronics Approach to the Design of Light-Weight Arms and Multi-Fingered Hands,” IEEE Conference on Robotics and Automation (ICRA), San Francisco, CA, USA (2000) pp. 46–54.Google Scholar
5. Luca, A. D., Albu-Schäffer, A., Haddadin, S. and Hirzinger, G., “Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2006), Beijing, China (2006) pp. 1623–1630.Google Scholar
6. Haddadin, S., Albu-Schäffer, A., Luca, A. D. and Hirzinger, G., “Evaluation of Collision Detection and Reaction for a Human-Friendly Robot on Biological Tissues,” International Workshop on Technical Challenges and Dependable Robots in Human Environments (IARP 2008), Pasadena, USA (2008).Google Scholar
7. Grebenstein, M. et al., “The DLR Hand Arm System,” IEEE International Conference on Robotics and Automation (ICRA). IEEE, Shanghai, China (2011) pp. 3175–3182.Google Scholar
8. Tsagarakis, N. G., Morfey, S., Medrano Cerda, G., Zhibin, L. and Caldwell, D. G., “Compliant Humanoid Coman: Optimal Joint Stiffness Tuning for Modal Frequency Control,” IEEE International Conference on Robotics and Automation (ICRA). IEEE, Karlsruhe, Germany (2013) pp. 673–678.Google Scholar
9. Haddadin, S., Albu-Schäffer, A. and Hirzinger, G., “Safety Evaluation of Physical Human-Robot Interaction via Crash-Testing,” Robotics: Science and Systems Conference (RSS2007), Atlanta, USA (2007) pp. 217–224.Google Scholar
10. Najmaei, N., “Applications of artificial intelligence in safe human-robot interactions,” IEEE Trans. Syst. Man Cybern. B 41 (2), 448459 (2011).Google Scholar
11. Ikuta, K., Ishii, H. and Nokata, M., “Safety evaluation method of design and control for human-care robots,” Int. J. of Robot. Res. 22 (5), 281298 (2003).Google Scholar
12. Heinzmann, J. and Zelinsky, A., “Quantitative safety guarantees for physical human-robot interaction,” Int. J. Robot. Res. 22 (7–8), 479504 (2003).Google Scholar
13. Lim, H. O. and Tanie, K., “Human safety mechanisms of human-friendly robots: Passive viscoelastic trunk and passively movable base,” Int. J. Robot. Res. 19 (4), 307335 (2000).Google Scholar
14. Yamada, Y., Hirasawa, Y., Huand, S., Umetani, Y. and Suita, K., “Human-robot contact in the safeguarding space,” IEEE/ASME Trans. Mechatronics 2 (4), 230236 (1997).Google Scholar
15. Haddadin, S., Albu-Schäffer, A. and Hirzinger, G., “Requirements for safe robots: Measurements, analysis and new insights,” Int. J. of Robot. Res. 28 (11–12), 15071527 (2009).Google Scholar
16. Haddadin, S., Albu-Schäffer, A. and Hirzinger, G., “Safety analysis for a human-friendly manipulator,” Int. J. Soc. Robot. 2 (3), 235252 (2010).Google Scholar
17. Haddadin, S., Albu-Schäffer, A., Haddadin, F., Roßmann, J. and Hirzinger, G., “Study on soft-tissue injury in robotics,” IEEE Robot. Autom. Mag. 18 (4), 2034 (2011).Google Scholar
18. Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A. and Albu-Schäffer, A., “On making robots understand safety: Embedding injury knowledge into control,” Int. J. of Robot. Res. 31 (13), 15781602 (2012).Google Scholar
19. Povse, B., Koritnik, D., Kamnik, R., Bajd, T. and Munih, M., “Industrial robot and human operator collision,” IEEE International Conference on Systems Man and Cybernetics (SMC), Istanbul, Turkey (2010) pp. 2663–2668.Google Scholar
20. Haddadin, S., Albu-Schäffer, A. and Hirzinger, G., “Safe physical human-robot interaction: Measurements, analysis & new insights,” International Symposium on Robotics Research (ISRR2007), Hiroshima, Japan (2007) pp. 261–282.Google Scholar
21. Edlich, R., Rodenheaver, G., Morgan, R., Berman, D. and Thacker, J., “Principles of emergency wound management,” Ann. Emergency Med. 17 (12), 12841302 (1988).Google Scholar
22. Haddadin, S., Albu-Schäffer, A. and Hirzinger, G., “The role of the robot mass and velocity in physical human-robot interaction – part 1: Unconstrained blunt impacts,” IEEE International Conference on Robotics and Automation (ICRA2008), Pasadena, USA (2008) pp. 1331–1338.Google Scholar
23. Povse, B., Koritnik, D., Kamnik, R., Bajd, T. and Munih, M., “Emulation system for assessment of human-robot collision,” Meccanica 46 (6), 13631371 (2011).Google Scholar
24. de Leva, P., “Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters,” J. Biomech. 29 (9), 12231230 (1996).Google Scholar
25. Trnkoczy, A., Bajd, T. and Malezic, M., “A dynamic model of the ankle joint under functional electrical stimulation in free movement and isometric conditions,” J. Biomech. 9, 509519 (1979).Google Scholar
26. Hill, A. V., “The heat of shortening and the dynamic constants of muscle,” Proc. R. Soc. Lond. Ser. B Biol. Sci. 126 (843), 136195 (1938).Google Scholar
27. Dorgan, S., “Mathematical modelling and control of human skeletal dynamics,” Eng. Sci. Educ. J. 8 (4), 185192 (1999).Google Scholar
28. Yamada, H., Strength of Biological Materials Williams & Wilkins in Baltimore, USA (The Williams and Wilkins Company, Baltimore, USA, 1970).Google Scholar
29. Hartmann, U., “Ein Mechanisches Finite-Elemente Modell des Menschlichen Kopfes,” Ph.D. Thesis (University of Leipzig, Leipzig, Germany, 1999).Google Scholar
30. McCormack, H. M., David, J., Horne, L. and Sheater, S., “Clinical applications of visual analogue scales: A critical review,” Psychological Med. 18, 10071019, 11 (1988).Google Scholar
31. Huskisson, E., “Measurement of pain,” Lancet 2, 1127–31 (1974).Google Scholar
32. Downie, W. W., Leatham, P. A., V. Rhind, M., Wright, V., Branco, J. A. and Anderson, J. A., “Studies with pain rating scales,” Ann. Rheum Dis. 37, 378–81 (1978).Google Scholar