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A multi-scale analysis of leg muscle contraction mechanics during a leg press exercise
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Journal of Biomechanics 2006, Vol. 39 (Suppl 1)
7233 We, 14:30-14:45 (P33) Musculoskeletal model of the lower extremity: validation of muscle moment arms and maximal isometric muscle force M.D. Klein Horsman 1, H.F.J.M. Koopman 1, H.E.J. Veeger 2, F.C.T. van der Helm 1. 1Laboratory of Biomedical Engineering, University of Twente,
Enschede, The Netherlands, 2Faculty of Human Movement Sciences, Free University, Amsterdam, The Netherlands Large-scale musculoskeletal models are used to study the function of specific muscles for certain tasks (e.g. [1]). Because one complete dataset is lacking in literature, these models are parameterized using different datasets (e.g. [2]). This leads to inaccuracies because of inter-individual anatomical differences and co-variance between parameters. We have developed a musculoskeletal model of the lower extremity based on a recently collected, accurate and complete anatomical dataset [3]. The goal of this study is to evaluate muscle moment arms and maximal isometric force with their functional implications. These properties determined with consistent model parameters. In this two-legged model, 10 joints are crossed with 264 Hill-type muscle elements, defined by muscle parameters such as optimal fibre length. 'Via' points or wrapping geometries were defined in case of a curvature in muscle line of action. Moment arm, maximal isometric force and the resulting moments for all muscle elements were simulated as a function of the corresponding joint angles. Moment arms determined in this study fell within the relatively large interindividual range found in literature. When datasets are combined relevant subject-specific relations such as the relation between moment arm and fibre length might get lost, which can conceal model limitations. In this study forcelength curves of some muscle fibers (e.g. Soleus) were too small for generating active force for the entire range of motion. This indicates that besides the elastic properties and pennation effects other principles might be relevant for the necessary range of the force-length curve. References [1] Delp S.L., et al. IEEE Transactions on Bio-medical Engineering 1990; 37: 757767. [2] Yamaguchi G.T., et al.. In: Multiple Muscle Systems, biomechanics and movement organization, Winters JM, Woo SL-Y (Eds). 1990; Springer Verlag NY, pp. 717-774. [3] Klein Horsman M.D., et al. In: Proceedings XXth Congress of the International Society of Biomechanics. 2005.
4152 We, 14:45-15:00 (P33) Human vertical jump at resonance J. Babi~, J. Lenar~ie. Department of Automatics, Biocybemetics and Robotics,
JoZef Stefan Institute, Ljubljana, Slovenia The purpose of our study was to analyze the vertical jump and to show that for each and every subject there exists an optimal triceps surae muscletendon complex stiffness that ensures the maximal possible height of the jump. We defined the influence of the m. gastrocnemius activation timing and the m. gastrocnemius and Achilles tendon stiffness on the jump height and established the methodology for analysis and evaluation of the vertical jump. We monitored kinematics, dynamics and m. gastrocnemius electrical activity during the maximum height countermovement jump of ten human subjects and measured viscoelastic properties of the m. gastrocnemius and Achilles tendon. Furthermore, we used the results of these measurements as inputs to the biomechanical model of the vertical jump that was individualized for each subject. The model allowed us to investigate the role of different biomechanical parameters in performing the vertical jump and to carry out the optimization of these parameters with regard to the jump height. All subjects activated their m. gastrocnemius slightly before the optimal moment, determined by means of simulations. In average, the difference between the optimal and measured knee angle when the m. gastrocnemius was activated was 6.4±2.220 . The average ratio between the optimal stiffness of the Achilles tendon determined by means of simulations and the measured stiffness of the Achilles tendon was 81 ±5.4%. The methodology and the obtained results of our study offer a new effective tool for improvement of the human jump performance. However, it has to be considered that this study deals only with one elastic tendon. Although the Achilles tendon we included in our study has the most distinctive elastic properties among all tendons of the human leg, the influence of other elastic tissues should also be studied. Anderson, F.C., Pandy, M.G. (1993). Storage and utilization of elastic strain energy during jumping. Journal of Biomechanics, 26, 1413-1427. Babi~, J., Lenar~ie, J. (2004). In vivo determination of triceps surae muscletendon complex viscoelastic properties. European Journal of Applied Physiology, 92, 477-484. Shorten, M.R. (1987). Muscle elasticity and human performance. Medicine and Sport Science, 25, 1-18.
Oral Presentations
6437 We, 15:00-15:15 (P33) A hopping robot controlled by an artificial neural network D.P. Ferris 1,2, T.J. Serbowicz 3, C.R. Kinnaird 1. 1Kinesiology, 2Biemedical
Engineering and 3Mechanical Engineering, University of Michigan, Ann Arbor, ML USA Humans prefer to hop in place at a frequency around 2 Hz. This preferred hopping frequency does not change with alterations in surface stiffness (added springs in series) or elastic bracing of the ankle (added springs in parallel). It is not known why humans have this preferred hopping frequency and why they maintain it across mechanical conditions. To provide insight into human hopping, we built a hopping robot controlled by an artificial neural network. The robot had one leg with one joint (ankle) and was constrained to vertical motion by a beam and hinge. An artificial pneumatic muscle connected to a metal spring (i.e. artificial tendon) acted as a plantar flexor for the robot. The artificial muscle had a similar force bandwidth (2.3Hz) as human skeletal muscle (2.2Hz). The controller was a Matsuoka artificial neural oscillator that selftunes to the resonant frequency of a mechanical system. Because there are different governing equations for hopping dynamics during the stance phase and the aerial phase (i.e. it has hybrid dynamics), there is no true resonant hopping frequency. As a result, it was not clear how a self-tuning artificial neural network would respond when controlling a hopping robot. We collected joint kinematics, ground reaction forces, and artificial muscle dynamics as the robot hopped in place over a range of muscle gain values and supraspinal gain values. We found that hopping frequency changed by less than 10% over a 100-fold variation in both muscle gain and supraspinal gain (1.88-1.98 Hz and 1.85-2.01 Hz, respectively). Over these parameter ranges, positive mechanical work for each hop was primarily provided by the artificial tendon (70-79%) with much less work provided by the artificial plantar flexor (21-30%). We plan to examine how the robot and controller respond to surface compliance and parallel elasticity at the ankle joint in future studies. 7251 We, 15:15-15:30 (P33) A multi-scale analysis of leg muscle contraction mechanics during a leg press exercise A.A. Ahmed 1, D.E. Thelen 2, B.R. Whittington 2, J.A. Ashton-Miller 1. 1University
of Michigan-Ann Arbor, USA, 2University of Wisconsin, Madison, USA One of the greatest risk factors for falls in the elderly is decreased lower extremity strength. An effective way to slow down, or even prevent, age-related loss of skeletal muscle strength is through progressive resistance training. Significant variability exists in the results of randomized controlled trials assessing the effectiveness of these training regimens. Part of this variability may be explained by interindividual differences in the training stimulus at the target muscle level. For example, in the leg press, training protocols prescribe the level of foot force, but do not assess the resulting force distribution in the target muscles. Therefore, the changes in single muscle fiber contractility assayed via repeated muscle biopsies are not really possible to interpret because the magnitude and time history of the force and/or work causing the fiber to adapt is unknown. The study aim was to quantify the mechanical stimulus applied to the vastus lateralis and gluteus maximus musculotendon units during a leg press exercise. Joint moments were obtained from recorded kinematic and ground reaction force data using inverse dynamics. Individual muscle forces were then calculated for one young female using a computed-muscle control model [Thelen, J Biomech, 2003]. The mean (SD) correlations between external foot force and knee moment, vastus lateralis force, hip moment, and gluteus maximus force are, respectively, -0.85 (0.15), 0.51 (0.25), -0.64 (0.15), 0.48 (0.23). Myoelectric activity of the agonist and antagonist muscles was also collected. The results show that neither the time history of the vastus lateralis force nor that of the knee joint moment correlate well with the foot reaction force during the vastus lateralis muscle lengthening or shortening phase. The training stimulus to vastus lateralis during leg press training cannot therefore be inferred from the foot reaction force history. Acknowledgments: NIH Grants T32 AG000114-21 & P30 AG 08808 5205 Th, 08:15-08:30 (P37) Overcoming marker occlusion using the procrustes method A. Rozumalski 1, M.H. Schwartz 1,2, K. Evans 1,2. 1Gillette Children's Specialty
Healthcare, St. Paul, USA, 2University of Minnesota, Minneapolis, USA Applying the Procrustes method to motion capture data is a novel way to overcome inter-marker motion artifact and to predict the trajectories of occluded markers. The method first determines a representative configuration for a group of markers. The representative configuration is then aligned to an instantaneous configuration, minimizing point-wise positional errors in a least squares sense. The alignment is unique when at least three markers are visible in the instantaneous configuration. The representative configuration can then be used to predict the trajectories of the occluded markers.
Journal of Biomechanics 2006, Vol. 39 (Suppl 1)
7233 We, 14:30-14:45 (P33) Musculoskeletal model of the lower extremity: validation of muscle moment arms and maximal isometric muscle force M.D. Klein Horsman 1, H.F.J.M. Koopman 1, H.E.J. Veeger 2, F.C.T. van der Helm 1. 1Laboratory of Biomedical Engineering, University of Twente,
Enschede, The Netherlands, 2Faculty of Human Movement Sciences, Free University, Amsterdam, The Netherlands Large-scale musculoskeletal models are used to study the function of specific muscles for certain tasks (e.g. [1]). Because one complete dataset is lacking in literature, these models are parameterized using different datasets (e.g. [2]). This leads to inaccuracies because of inter-individual anatomical differences and co-variance between parameters. We have developed a musculoskeletal model of the lower extremity based on a recently collected, accurate and complete anatomical dataset [3]. The goal of this study is to evaluate muscle moment arms and maximal isometric force with their functional implications. These properties determined with consistent model parameters. In this two-legged model, 10 joints are crossed with 264 Hill-type muscle elements, defined by muscle parameters such as optimal fibre length. 'Via' points or wrapping geometries were defined in case of a curvature in muscle line of action. Moment arm, maximal isometric force and the resulting moments for all muscle elements were simulated as a function of the corresponding joint angles. Moment arms determined in this study fell within the relatively large interindividual range found in literature. When datasets are combined relevant subject-specific relations such as the relation between moment arm and fibre length might get lost, which can conceal model limitations. In this study forcelength curves of some muscle fibers (e.g. Soleus) were too small for generating active force for the entire range of motion. This indicates that besides the elastic properties and pennation effects other principles might be relevant for the necessary range of the force-length curve. References [1] Delp S.L., et al. IEEE Transactions on Bio-medical Engineering 1990; 37: 757767. [2] Yamaguchi G.T., et al.. In: Multiple Muscle Systems, biomechanics and movement organization, Winters JM, Woo SL-Y (Eds). 1990; Springer Verlag NY, pp. 717-774. [3] Klein Horsman M.D., et al. In: Proceedings XXth Congress of the International Society of Biomechanics. 2005.
4152 We, 14:45-15:00 (P33) Human vertical jump at resonance J. Babi~, J. Lenar~ie. Department of Automatics, Biocybemetics and Robotics,
JoZef Stefan Institute, Ljubljana, Slovenia The purpose of our study was to analyze the vertical jump and to show that for each and every subject there exists an optimal triceps surae muscletendon complex stiffness that ensures the maximal possible height of the jump. We defined the influence of the m. gastrocnemius activation timing and the m. gastrocnemius and Achilles tendon stiffness on the jump height and established the methodology for analysis and evaluation of the vertical jump. We monitored kinematics, dynamics and m. gastrocnemius electrical activity during the maximum height countermovement jump of ten human subjects and measured viscoelastic properties of the m. gastrocnemius and Achilles tendon. Furthermore, we used the results of these measurements as inputs to the biomechanical model of the vertical jump that was individualized for each subject. The model allowed us to investigate the role of different biomechanical parameters in performing the vertical jump and to carry out the optimization of these parameters with regard to the jump height. All subjects activated their m. gastrocnemius slightly before the optimal moment, determined by means of simulations. In average, the difference between the optimal and measured knee angle when the m. gastrocnemius was activated was 6.4±2.220 . The average ratio between the optimal stiffness of the Achilles tendon determined by means of simulations and the measured stiffness of the Achilles tendon was 81 ±5.4%. The methodology and the obtained results of our study offer a new effective tool for improvement of the human jump performance. However, it has to be considered that this study deals only with one elastic tendon. Although the Achilles tendon we included in our study has the most distinctive elastic properties among all tendons of the human leg, the influence of other elastic tissues should also be studied. Anderson, F.C., Pandy, M.G. (1993). Storage and utilization of elastic strain energy during jumping. Journal of Biomechanics, 26, 1413-1427. Babi~, J., Lenar~ie, J. (2004). In vivo determination of triceps surae muscletendon complex viscoelastic properties. European Journal of Applied Physiology, 92, 477-484. Shorten, M.R. (1987). Muscle elasticity and human performance. Medicine and Sport Science, 25, 1-18.
Oral Presentations
6437 We, 15:00-15:15 (P33) A hopping robot controlled by an artificial neural network D.P. Ferris 1,2, T.J. Serbowicz 3, C.R. Kinnaird 1. 1Kinesiology, 2Biemedical
Engineering and 3Mechanical Engineering, University of Michigan, Ann Arbor, ML USA Humans prefer to hop in place at a frequency around 2 Hz. This preferred hopping frequency does not change with alterations in surface stiffness (added springs in series) or elastic bracing of the ankle (added springs in parallel). It is not known why humans have this preferred hopping frequency and why they maintain it across mechanical conditions. To provide insight into human hopping, we built a hopping robot controlled by an artificial neural network. The robot had one leg with one joint (ankle) and was constrained to vertical motion by a beam and hinge. An artificial pneumatic muscle connected to a metal spring (i.e. artificial tendon) acted as a plantar flexor for the robot. The artificial muscle had a similar force bandwidth (2.3Hz) as human skeletal muscle (2.2Hz). The controller was a Matsuoka artificial neural oscillator that selftunes to the resonant frequency of a mechanical system. Because there are different governing equations for hopping dynamics during the stance phase and the aerial phase (i.e. it has hybrid dynamics), there is no true resonant hopping frequency. As a result, it was not clear how a self-tuning artificial neural network would respond when controlling a hopping robot. We collected joint kinematics, ground reaction forces, and artificial muscle dynamics as the robot hopped in place over a range of muscle gain values and supraspinal gain values. We found that hopping frequency changed by less than 10% over a 100-fold variation in both muscle gain and supraspinal gain (1.88-1.98 Hz and 1.85-2.01 Hz, respectively). Over these parameter ranges, positive mechanical work for each hop was primarily provided by the artificial tendon (70-79%) with much less work provided by the artificial plantar flexor (21-30%). We plan to examine how the robot and controller respond to surface compliance and parallel elasticity at the ankle joint in future studies. 7251 We, 15:15-15:30 (P33) A multi-scale analysis of leg muscle contraction mechanics during a leg press exercise A.A. Ahmed 1, D.E. Thelen 2, B.R. Whittington 2, J.A. Ashton-Miller 1. 1University
of Michigan-Ann Arbor, USA, 2University of Wisconsin, Madison, USA One of the greatest risk factors for falls in the elderly is decreased lower extremity strength. An effective way to slow down, or even prevent, age-related loss of skeletal muscle strength is through progressive resistance training. Significant variability exists in the results of randomized controlled trials assessing the effectiveness of these training regimens. Part of this variability may be explained by interindividual differences in the training stimulus at the target muscle level. For example, in the leg press, training protocols prescribe the level of foot force, but do not assess the resulting force distribution in the target muscles. Therefore, the changes in single muscle fiber contractility assayed via repeated muscle biopsies are not really possible to interpret because the magnitude and time history of the force and/or work causing the fiber to adapt is unknown. The study aim was to quantify the mechanical stimulus applied to the vastus lateralis and gluteus maximus musculotendon units during a leg press exercise. Joint moments were obtained from recorded kinematic and ground reaction force data using inverse dynamics. Individual muscle forces were then calculated for one young female using a computed-muscle control model [Thelen, J Biomech, 2003]. The mean (SD) correlations between external foot force and knee moment, vastus lateralis force, hip moment, and gluteus maximus force are, respectively, -0.85 (0.15), 0.51 (0.25), -0.64 (0.15), 0.48 (0.23). Myoelectric activity of the agonist and antagonist muscles was also collected. The results show that neither the time history of the vastus lateralis force nor that of the knee joint moment correlate well with the foot reaction force during the vastus lateralis muscle lengthening or shortening phase. The training stimulus to vastus lateralis during leg press training cannot therefore be inferred from the foot reaction force history. Acknowledgments: NIH Grants T32 AG000114-21 & P30 AG 08808 5205 Th, 08:15-08:30 (P37) Overcoming marker occlusion using the procrustes method A. Rozumalski 1, M.H. Schwartz 1,2, K. Evans 1,2. 1Gillette Children's Specialty
Healthcare, St. Paul, USA, 2University of Minnesota, Minneapolis, USA Applying the Procrustes method to motion capture data is a novel way to overcome inter-marker motion artifact and to predict the trajectories of occluded markers. The method first determines a representative configuration for a group of markers. The representative configuration is then aligned to an instantaneous configuration, minimizing point-wise positional errors in a least squares sense. The alignment is unique when at least three markers are visible in the instantaneous configuration. The representative configuration can then be used to predict the trajectories of the occluded markers.