Advertisement

Annals of Biomedical Engineering

, Volume 43, Issue 2, pp 416–426 | Cite as

Detecting Slipping-Like Perturbations by Using Adaptive Oscillators

  • Peppino Tropea
  • Nicola Vitiello
  • Dario Martelli
  • Federica Aprigliano
  • Silvestro Micera
  • Vito Monaco
Article

Abstract

This study introduces a novel algorithm to detect unexpected slipping-like perturbations based on the comparison between actual leg joint angles and those predicted by a pool of adaptive oscillators. The approach grounds on the hypothesis that during postural transitions, the difference between these datasets diverges and can early signal that the dynamic balance is challenged. To test this hypothesis, leg joint angles of twelve healthy young participants were recorded while undergoing four different perturbations delivered during steady locomotion. Joint angles were estimated after spanning the whole domain of the adaptive oscillator dynamics. Results confirmed that the implemented strategy allows to early detect a postural transition induced by a slipping-like perturbation: the best performance is represented by a mean detection time ranging between 150 and 250 ms and a low rate (lower than 10%) of false alarms. On the whole, the proposed approach is efficient even if it is based on a quite simple threshold-based algorithm. Moreover, it does not need any falling-based training before being implemented, is not computationally heavy, and is not subject dependent. Finally, since it is based on leg joint angles, it appears well suited to be implemented in lower-limb orthoses/prostheses already equipped with joint position sensors.

Keywords

Pre-fall detection Adaptive oscillators Perturbation Walking Joint angles Threshold algorithm 

Notes

Acknowledgments

This work was supported by the European Union within the CYBERLEGs (The CYBERnetic LowEr-Limb CoGnitive Ortho-prosthesis, ICT 287894) and the I-DONT-FALL (Integrated prevention and Detection sOlutioNs Tailored to the population and Risk Factors associated with FALLs, CIP-ICT-PSP-2011-5-297225) projects.

References

  1. 1.
    Bagalà, F., C. Becker, A. Cappello, L. Chiari, K. Aminian, J. M. Hausdorff, W. Zijlstra, and J. Klenk. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS One 7:e37062, 2012.CrossRefPubMedCentralPubMedGoogle Scholar
  2. 2.
    Bassi Luciani, L., V. Genovese, V. Monaco, L. Odetti, E. Cattin, and S. Micera. Design and evaluation of a new mechatronic platform for assessment and prevention of fall risks. J. Neuroeng. Rehabil. 9:51, 2012.Google Scholar
  3. 3.
    Becker, C., L. Schwickert, S. Mellone, F. Bagala, L. Chiari, J. L. Helbostad, W. Zijlstra, K. Aminian, A. Bourke, C. Todd, S. Bandinelli, N. Kerse, and J. Klenk. Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors. Z. Gerontol. Geriatr. 45:707–715.Google Scholar
  4. 4.
    Bell, A. L., D. R. Pedersen, and R. A. Brand. A comparison of the accuracy of several hip center location prediction methods. J. Biomech. 23:617–621, 1990.CrossRefPubMedGoogle Scholar
  5. 5.
    Borghese, N. A., L. Bianchi, and F. Lacquaniti. Kinematic determinants of human locomotion. J. Physiol. 494(Pt 3):863–879, 1996.Google Scholar
  6. 6.
    Davis R. B. III, S. Õunpuu, D. Tyburski, and J. R. Gage. A gait analysis data collection and reduction technique. Hum. Mov. Sci. 10:575–587, 1991.Google Scholar
  7. 7.
    Ferber, R., L. R. Osternig, M. H. Woollacott, N. J. Wasielewski, and J. H. Lee. Reactive balance adjustments to unexpected perturbations during human walking. Gait Posture 16:238–248, 2002.CrossRefPubMedGoogle Scholar
  8. 8.
    Herr, H. Exoskeletons and orthoses: classification, design challenges and future directions. J. Neuroeng. Rehabil. 6:21, 2009.CrossRefPubMedCentralPubMedGoogle Scholar
  9. 9.
    Kangas, M., I. Vikman, L. Nyberg, R. Korpelainen, J. Lindblom, and T. Jamsa. Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects. Gait Posture 35:500–505, 2012.CrossRefPubMedGoogle Scholar
  10. 10.
    Lau, H. Y., K. Y. Tong, and H. Zhu. Support vector machine for classification of walking conditions using miniature kinematic sensors. Med. Biol. Eng. Comput. 46:563–573, 2008.CrossRefPubMedGoogle Scholar
  11. 11.
    Liu, J., and T. Lockhart. Development and evaluation of a prior-to-impact fall event detection algorithm. IEEE Trans. Biomed. Eng. 2014. doi: 10.1109/TBME.2014.2315784.PubMedCentralGoogle Scholar
  12. 12.
    Lockhart, T. E., J. C. Woldstad, and J. L. Smith. Effects of age-related gait changes on the biomechanics of slips and falls. Ergonomics 46:1136–1160, 2003.CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Mannini, A., and A. M. Sabatini. Gait phase detection and discrimination between walking-jogging activities using hidden Markov models applied to foot motion data from a gyroscope. Gait Posture 36:657–661, 2012.CrossRefPubMedGoogle Scholar
  14. 14.
    Martelli, D., F. Artoni, V. Monaco, A. M. Sabatini, and S. Micera. Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm. PLoS One 9:e92037, 2014.CrossRefPubMedCentralPubMedGoogle Scholar
  15. 15.
    Martelli, D., V. Monaco, L. Bassi Luciani, and S. Micera. Angular momentum during unexpected multidirectional perturbations delivered while walking. IEEE Trans. Biomed. Eng. 60:1785–1795, 2013.Google Scholar
  16. 16.
    Martelli, D., V. Monaco, and S. Micera. Detecting falls by analyzing angular momentum. IEEE Int. Conf. Rehabil. Robot. 2011:5975404, 2011.PubMedGoogle Scholar
  17. 17.
    Masud, T., and R. O. Morris. Epidemiology of falls. Age Ageing 30:3–7, 2001.CrossRefPubMedGoogle Scholar
  18. 18.
    Mellone, S., C. Tacconi, L. Schwickert, J. Klenk, C. Becker, and L. Chiari. Smartphone-based solutions for fall detection and prevention: the FARSEEING approach. Z. Gerontol. Geriatr. 45:722–727, 2012.CrossRefPubMedGoogle Scholar
  19. 19.
    Miller, W. C., M. Speechley, and B. Deathe. The prevalence and risk factors of falling and fear of falling among lower extremity amputees. Arch. Phys. Med. Rehabil. 82:1031–1037, 2001.CrossRefPubMedGoogle Scholar
  20. 20.
    Monaco, V., L. A. Rinaldi, G. Macrì, and S. Micera. During walking elders increase efforts at proximal joints and keep low kinetics at the ankle. Clin. Biomech. (Bristol, Avon) 24:493–508, 2009.Google Scholar
  21. 21.
    Orendurff, M. S., J. A. Schoen, G. C. Bernatz, A. D. Segal, and G. K. Klute. How humans walk: bout duration, steps per bout, and rest duration. J. Rehabil. Res. Dev. 45:1077–1089, 2008.CrossRefPubMedGoogle Scholar
  22. 22.
    Righetti, L., J. Buchli, and A. J. Ijspeert. Dynamic Hebbian learning in adaptive frequency oscillators. Phys. D-Nonlinear Phenom. 216:269–281, 2006.CrossRefGoogle Scholar
  23. 23.
    Ronsse, R., T. Lenzi, N. Vitiello, B. Koopman, E. van Asseldonk, S. M. M. De Rossi, J. van den Kieboom, H. van der Kooij, M. C. Carrozza, and A. J. Ijspeert. Oscillator-based assistance of cyclical movements: model-based and model-free approaches. Med. Biol. Eng. Comput. 49:1173–1185, 2011.Google Scholar
  24. 24.
    Shiratori, T., and M. Latash. The roles of proximal and distal muscles in anticipatory postural adjustments under asymmetrical perturbations and during standing on rollerskates. Clin. Neurophysiol. 111:613–23, 2000.Google Scholar
  25. 25.
    Stolze, H., S. Klebe, C. Zechlin, C. Baecker, L. Friege, and G. Deuschl. Falls in frequent neurological diseases–prevalence, risk factors and aetiology. J. Neurol. 251:79–84, 2004.CrossRefPubMedGoogle Scholar
  26. 26.
    Whittington, B. R., and D. G. Thelen. A simple mass-spring model with roller feet can induce the ground reactions observed in human walking. J. Biomech. Eng. 131:11013, 2009.CrossRefGoogle Scholar
  27. 27.
    Wu, G., F. C. T. Van Der Helm, H. E. J. Veeger, M. Makhsous, P. Van Roy, C. Anglin, J. Nagels, A. R. Karduna, K. McQuade, X. Wang, F. W. Werner, and B. Buchholz. ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion-part II: shoulder, elbow, wrist and hand. J. Biomech. 38:981–992, 2005.CrossRefPubMedGoogle Scholar
  28. 28.
    Wu, G., and S. Xue. Portable preimpact fall detector with inertial sensors. IEEE Trans. Neural. Syst. Rehabil. Eng. 16:178–183, 2008.CrossRefPubMedGoogle Scholar
  29. 29.
    Yu, M., A. Rhuma, S. M. Naqvi, L. Wang, and J. Chambers. A posture recognition based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans. Inf. Technol. Biomed. 16:1274–1286, 2012.CrossRefPubMedGoogle Scholar
  30. 30.
    Zhang, F., S. E. D’Andrea, M. J. Nunnery, S. M. Kay, and H. Huang. Towards design of a stumble detection system for artificial legs. IEEE Trans. Neural. Syst. Rehabil. Eng. 19:567–577, 2011.CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2014

Authors and Affiliations

  • Peppino Tropea
    • 1
  • Nicola Vitiello
    • 1
    • 2
  • Dario Martelli
    • 1
  • Federica Aprigliano
    • 1
  • Silvestro Micera
    • 1
    • 3
  • Vito Monaco
    • 1
  1. 1.The BioRobotics InstituteScuola Superiore Sant’AnnaPontederaItaly
  2. 2.Don Carlo Gnocchi FoundationFlorenceItaly
  3. 3.Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, School of EngineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

Personalised recommendations