╨╧рб▒с>■  13■   0                                                                                                                                                                                                                                                                                                                                                                                                                                                ье┴G ┐ц"bjbjО┘О┘ ".ь│ь│      ]bbbbbbbvvvvv В vSьЪЪЪЪЪЪЪЪавввввв$?Ї3 ╨╞НbЪЪЪЪЪ╞╩bbЪЪЪ╩╩╩ЪЇbЪbЪаvvbbbbЪа╩╓╩аbbаЪО `ФЖF┴vvО<аBiomechanical Signal processing in alpine skiing research Vladimir Medved*, Ph.D. Assoc. Professor, Ozren Raenovi*, B.Ed. Grad. student, Bojan Nemec**, Ph.D. *Faculty of Physical Education, University of Zagreb, Zagreb, Croatia, **Department of Automation, Biocybernetics and Robotics, Jo~ef Stefan Institute, Ljubljana, Slovenia Summary and Conclusion A research is underway aimed at quantifying movement structures in alpine sking by means of kinematic video, kinetic and electromyographic (EMG) data. Signal acquisition is followed by processing including basic signal similarity algorithms applied to quantify the level of locomotor skill displayed by a skier. The comparisons of skiing techniques as well as of types of skies are thus enabled. Time domain processing of EMG and force data confirms, in general, that carving skies impose less demand to the neuromuscular system. This may have implications both to sports training and to minimization of chronic trauma in an average skier. Introduction To quantify movement skill by means of biomechanical signal processing is not a new idea (Gandhi 1980, Medved and Tonkovi 1991, Medved et al. 1995, Medved 2001). In traditional biomechanical areas such as gait analysis, electromyographic (EMG) patterns have been identified in order to differentiate gait pathologies (Shiavi et al. 1986, for instance). Applying similar approach to alpine skiing is challenging due, primarily, to technical problems during outdoor measurements. We have measured alpine skiers with the principal aim to compare different skies and different skiing techniques (i.e. Austrian and Croatian) biomechanically, and thus to evaluate corresponding influences on neuromuscular system (Medved et. al. 2001, Raenovi et al. 2001). Material and Methods Our experiments took place at the slopes of the Rogla ski resort in Slovenia. One of co-authors of this paper (O.R.) himself, being a ski instructor and a member of Croatian Demo Team, served as a subject. He was equipped with a specially developed ski boot-fitted ground reaction force measuring system combined with video (developed at Jo~ef Stefan Institute) and in addition with the 4-channel portable surface EMG device (MEGA, Finland), recording lower extremity myoelectric activity (m.rectus femoris, m.vastus lateralis, m.vastus medialis, m. biceps femoris, unilaterally) Electrodes were located near the centre of muscle belly, at 3 cm centre-to-centre distance and approximaterly paralel to muscle fibers, following standard ISEK reccomendations. Raw EMG was recorded, and was then digitally smoothed (full-wave rectified and low pass filtered). Skier performed various turns on a defined path using two types of skies and two types of techniques (Raenovi et al. 2001). Meteorological conditions during measurements were normal, and only minor problems were encountered due to low temperature. Signal processing included basic time domain statistics and calculation of correlations between various force and EMG signal waveforms. Results and Conclusion Each movement pattern resulted with one multichannel signal record. The records are segmented in time corresponding to kinematic movement sequences (determined visually from video records). Basic signal statistics were calculated for each sequence. Mathematical algoritms are then applied characterizing the degree of signal similarity (Medved et al. 1995, Medved 2001). Preliminary results indicate superiority of carving skies with regard to the load imposed on the locomotor system. To conclude; this approach offers new information on the neuro-muscular function in alpine skiing. This might allow improvements in training and teaching of skiing and better quantification of chronic traumatic loadings (knee joint) as well. Acknowledgement This study was supported by the Ministry of Science and Technology of the Republic of Croatia (Pr.No. 034-004) References Gandy et al. 1980. Acquisition and analysis of electromyographic data associated with dynamic movements of the arm. Med. Biol. Eng. Comput. 57. Medved, V. 2001. Measurement of Human Locomotion, CRC Press, Boca Raton, Fl. Medved, V. and Tonkovi, S. 1991. Method to evaluate the skill level in fast locomotion through myoelectric and kinetic signal analysis. Med. Biol. Eng. Comput. 29:406-412. Medved, V., Tonkovi, S. and Cifrek, M. 1995. Simple neuro-mechanical measure of the locomotor skill: an example of backward somersault. Med. Progr. Technol. 21:77-84. Medved, V., Nemec, B., Kasovi-Vidas, M., Raenovi, O. 2001. Laboratory and field research into biomechanics of alpine skiing. In: Science and Skiing II. Schriftenreihe: Schriften zur Sportwissenschaft, Band 26 (Eds: E. Mueller, H. Schwameder, C. Raschner, S. Lindinger and E. Kornexl). Kova , Hamburg, 84-94. Raenovi, O., Nemec, B., Medved, V. 2001. A new biomechanical measurement and testing paradigm for tourns in alpine skiing, accepted for MEDICON 2001, Pula. Shiavi, R., Bourne, J., and Holland, A. 1986. Automated extraction of activity features in linear envelopes of locomotor electromyographic patterns. IEEE Trans. Biomed. 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