Artificial Intelligence in Medicine
Volume 50, Issue 1 , Pages 55-61 , September 2010

Human movement onset detection from isometric force and torque measurements: A supervised pattern recognition approach

  • Paolo Soda

      Affiliations

    • Medical Informatics and Computer Science Laboratory, Integrated Research Centre, University Campus Bio-Medico of Rome, Via Alvaro del Portillo, 21, 00128 Roma, Italy
    • Fondazione Alberto Sordi, Via dei Compositori 130, 00128 Roma, Italy
    • Corresponding Author InformationCorresponding author at: Medical Informatics and Computer Science Laboratory, Integrated Research Centre, University Campus Bio-Medico of Rome, Via Alvaro del Portillo, 21, 00128 Roma, Italy. Tel.: +39 06 225419620; fax: +39 06 225419609.
  • ,
  • Stefano Mazzoleni

      Affiliations

    • Advanced Robotics Technology and Systems Laboratory, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera (PI), Italy
  • ,
  • Giuseppe Cavallo

      Affiliations

    • Fondazione Alberto Sordi, Via dei Compositori 130, 00128 Roma, Italy
    • Biomedical Robotics and Biomicrosystems Lab, Integrated Research Centre, University Campus Bio-Medico of Rome, Via Alvaro del Portillo, 21, 00128 Roma, Italy
  • ,
  • Eugenio Guglielmelli

      Affiliations

    • Fondazione Alberto Sordi, Via dei Compositori 130, 00128 Roma, Italy
    • Biomedical Robotics and Biomicrosystems Lab, Integrated Research Centre, University Campus Bio-Medico of Rome, Via Alvaro del Portillo, 21, 00128 Roma, Italy
  • ,
  • Giulio Iannello

      Affiliations

    • Medical Informatics and Computer Science Laboratory, Integrated Research Centre, University Campus Bio-Medico of Rome, Via Alvaro del Portillo, 21, 00128 Roma, Italy
    • Fondazione Alberto Sordi, Via dei Compositori 130, 00128 Roma, Italy

Received 15 September 2008 ,Revised 26 October 2009 ,Accepted 29 March 2010.

References 

  1. Aisen ML, Krebs HI, Hogan N, McDowell F, Volpe BT. The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. Archives of Neurology. 1997;54(4):443–446
  2. Reinkensmeyer DJ, Hogan N, Krebs HI, Lehman SL, Lum PS. Rehabilitators, robots, and guides: new tools for neurological rehabilitation. In:  Winters JM,  Crago PE editor. Biomechanics and neural control of posture and movement. Berlin: Springer; 2000;p. 516–533
  3. Krebs HI, Hogan N, Aisen ML, Volpe BT. Robot-aided neurorehabilitation. IEEE Transactions on Rehabilitation Engineering. 1998;6(1):75–87
  4. Mazzoleni S, Van Vaerenbergh J, Toth A, Munih M, Guglielmelli E, Dario P. The Alladin diagnostic device: an innovative platform for assessing post-stroke functional recovery. In:  Kommu SS editors. Rehabilitation robotics. Vienna: Itech Education and Publishing; 2007;p. 535–554
  5. Staude GH. Precise onset detection of human motor responses using a whitening filter and the log-likelihood-ratio test. IEEE Transactions on Biomedical Engineering. 2001;48(11):1292–1305
  6. Plat FM, Praamstra P, Horstink M. Redundant-signals effects on reaction time, response force, and movement-related potentials in Parkinson’s disease. Experimental Brain Research. 2000;130(4):533–539
  7. Tunik E, Adamovich SV, Poizner H, Feldman AG. Deficits in rapid adjustments of movements according to task constraints in Parkinson’s disease. Movement Disorders. 2004;19(8):897–906
  8. Mazzoleni S, Cavallo G, Munih M, Cinkelj J, Jurak M, Van Vaerenbergh J, et al. Towards application of a mechatronic platform for whole-body isometric force–torque measurements to functional assessment in neuro-rehabilitation. In: Robotics and Automation, 2007 IEEE International Conference. Piscataway, NJ, USA: IEEE Press; 2007;pp. 1535–40
  9. Mazzoleni S, Van Vaerenbergh J, Toth A, Munih M, Guglielmelli E, Dario E. Alladin: a novel mechatronic platform for assessing post-stroke functional recovery. In: Rehabilitation Robotics, 2005 IEEE 9th International Conference. Piscataway, NJ, USA: IEEE Press; 2005;pp. 156–59
  10. Dobie RA, Wilson MJ. Objective response detection in the frequency domain. Electroencephalography and Clinical Neurophysiology. 1993;88(6):516–524
  11. Teasdale N, Bard C, Fleury M, Young DE, Proteau L. Determining movement onsets from temporal series. Journal of Motor Behavior. 1993;25(2):97–106
  12. Bonato P, Knaflitz T, di Elettronica MD. A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE Transactions on Biomedical Engineering. 1998;45(3):287–299
  13. Li X, Aruin AS. Muscle activity onset time detection using Teager–Kaiser energy operator. In: Engineering in Medicine and Biology Society, 2005. 27th Annual International Conference. Piscataway, NJ, USA: IEEE Press; 2005;pp. 7549–52
  14. Micera S, Sabatini AM, Dario P. An algorithm for detecting the onset of muscle contraction by EMG signal processing. Medical Engineering and Physics. 1998;20(3):211–215
  15. Staude GH, Wolf WM. Objective motor response onset detection in surface myoelectric signals. Medical Engineering and Physics. 1999;21(6–7):449–467
  16. Staude GH, Wolf WM, Appel U, Dengler R. Methods for onset detection of voluntary motor responses in tremor patients. IEEE Transactions on Biomedical Engineering. 1996;43(2):177–188
  17. Karatas M, Cetin N, Bayramoglu M, Dilek A. Trunk muscle strength in relation to balance and functional disability in unihemispheric stroke patients. American Journal of Physical Medicine & Rehabilitation. 2004;83:81–87
  18. Cunnington R, Windischberger C, Moser E. Premovement activity of the pre-supplementary motor area and the readiness for action: studies of time-resolved event-related functional MRI. Human Movement Science. 2005;24(5–6):644–656
  19. Lehericy S, Gerardin E, Poline JB, Meunier S, Van de Moortele PF, Le Bihan D, et al. Motor execution and imagination networks in post-stroke dystonia. NeuroReport. 2004;15:1887–1890
  20. Wolpert DM, Ghahramani Z, Flanagan JR. Perspectives and problems in motor learning. Trends Cognitive Sciences. 2001;5:487–494
  21. Carr JH, Shepherd RB. Neurological rehabilitation: optimizing motor performance. Butterworth-Heinemann; 1998;
  22. Mazzoleni S, Guglielmelli E, Dario P, Van Vaerenbergh J. Deliverable 2.1: diagnostic device and method for force/torque measurement based therapy assessment, project deliverable. European Commission, IST-2002-507424, Brussels, Belgium; 2005.
  23. Toth A, Mazzoleni S, Guglielmelli E, Van Vaerenbergh J. Deliverable 2.2: technical documentation of the Alladin diagnostic device, project deliverable. European Commission, IST-2002-507424, Brussels, Belgium; 2005.
  24. Willsky A, Jones H. A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems. IEEE Transactions on Automatic Control. 1976;21(1):108–112
  25. Rohrer B, Fasoli S, Krebs HI, Hughes R, Volpe B, Frontera WR, et al. Movement smoothness changes during stroke recovery. The Journal of Neuroscience. 2002;22:8297–8304
  26. Kuncheva LI. Combining pattern classifiers. Wiley–Interscience; 2004;
  27. Ding CHQ, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics. 2001;17(4):349–358
  28. Jain AK, Duin RPW, Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000;22(1):4–37
  29. Schapire RE. The boosting approach to machine learning: an overview. In:  Denison DD editors. Lecture notes in statistics. Berlin: Springer; 2003;p. 149–172

PII: S0933-3657(10)00040-0

doi: 10.1016/j.artmed.2010.04.008

Artificial Intelligence in Medicine
Volume 50, Issue 1 , Pages 55-61 , September 2010