Track 6. Sport Biomechanics - Joint ISB Track

[4] Cheung JT., Zhang M., An KN. Foot Ankle Int. 2006; in press.

4508 We, 11:30-11:45 (P31 ) Foot joints and tibial kinematic coupling patterns during stance phase of barefoot versus shod running

M. Eslami 1,2, M. Damavandi 1,2, P. Allard 1,2. 1Department of Kinesiology, University of Montreal, Montreal, Canada, 2 Laboratoire d'~tude du Mouvement, Research Center, Sainte-Justine Hospital, Montreal, Canada

Background: Three dimensional forefoot motions contributed to rearfoot frontal plane motion during running [1]. However, it is unclear that, a) if forefoot- rearfoot coupling motion pattern is manipulated via shod, b) if this coupling motion affects on excessive tibial rotation. Objectives: a) to compare the frontal plane excessive excursion of forefoot, rearfoot and the transverse plane tibial rotation during first 50% of stance phase of barefoot versus shod running, b) to determine differences in mean relative phase angle of the frontal plane forefoot and rearfoot motion during the stance phase of barefoot versus shod running. Methods: Twelve subjects ran 10 times at 170 steps per minute under barefoot and shod conditions. A force plate and a six-camera system recorded impact forces and three-dimensional kinematic coordination, respectively. The three- dimensional forefoor-reafoot and rearfoot-tibial rotations determined using a joint coordinate system approach [2]. Directional statistics and relative phase angle measures were used to test the shod effects. Results: High rearfoot eversion/tibial internal rotation ratio was found in run- ning with shoes (2.13°±1.14). This finding contributed significantly to decrease in tibial rotation excursion in the shod condition (4.27°±2.04) when compared to the barefoot running (16.02°±10.38) (p <0.01). Forefoot varus with respect to rearfoot increased about 3.310 in shod condition while it was -0.850 valgus in the barefoot condition (p <0.05). Mean absolute relative angle of the forefoot and rearfoot was more in phase (30 °) in midstance than early and late stance phases of running during both conditions (p <0.05). Conclusion: Running with shoes could reduce excessive transverse tibial rotation during first 50% of stance phase. However, it was ineffective to change frontal plane forefoot-rearfoot coupling motion patterns during running.

References [1] Pohl MB, et al. Changes in foot and lower limb coupling due to systematic

variations in step width. Clinical Biomechanics 2006; 21(2): 175-183. [2] Kidder S, et al. A system for the analysis of foot and ankle kinematics during

gait. IEEE Transactions on Rehabilitation Engineering 1996; 4: 25-32.

4637 We, 11:45-12:00 (P31) Using neural networks to understand relationships in the traction of studded footwear on sports surfaces B. Kirk, M. Carr6, S. Haake, G. Manson. Department ef Mechanical Engineering, University of Sheffield, Sheffield, UK

The influence of shoe outsoles and stud types on traction forces has been investigated in a variety of studies. Barry et al. (2000), for example, have developed mechanical test devices to which soccer shoes are attached and forces and moments measured for specific movements. Measurements by virtue of pressure insoles and force plates have also been undertaken (Smith et aL, 2003). However, the influence of shoe variables, such as outsole material and number and shape of studs, on traction performance parameters, such as dynamic traction force, has not been clearly illustrated. This is largely due to the variation of multiple parameters between trials. Traction test devices have been developed which focus on the interaction of studs with sports surfaces in translational motion (Kirk et al., 2005). Methodology of such testing aims to identify variables which control traction and quantify their relationships with traction performance parameters. Such relationships are often non-linear and unclear, limiting the use of traditional regression techniques. Artificial neural networks have been trained to model traction depending on stud input parameters for a single surface using experimental measurements. The networks were tested with further experimental measurements which showed an average prediction accuracy of 10%, compared to 36% when a linear model is implemented. The trained neural networks allow rapid prediction of traction forces for any combination of stud variables within the limits of the training data. Such techniques show promise in the prediction, understanding and optimisation of stud and surface parameters with respect to traction forces experienced by athletes.

References Barry E. P., Kummer R. and Milburn P.D. (2000). In: The Engineering of Sport:

Research, Development and Innovation. Blackwell Scientific, Oxford, pp. 103- 112.

Kirk, R. E, Haake S. J., Senior, T. and Carre, M. J. (2005). In: The Impact of Technology on Sport. ASTA, Tokyo Institute of Technology, Japan, pp. 336-342.

Smith N., Dyson R. and Janaway L. (2003). Sports Engineering, 7: 159-163.

6.3. Footwear- Movement Control $183

5844 We, 12:00-12:15 (P31) Quantification of parameters for running shoes using hall effect sensors K. Roemer, T. Milani. Institute of Sport Science, Chemnitz University of Technology, Chemnitz, Germany

To take a closer look on characteristics of midsoles in running shoes exist several different testing devices. There are different setups to test mechanical properties like the impacter to quantify damping constants. Other setups are used to quantify biomechanical parameters during running like tibial accelera- tion, ground reaction forces, plantar pressure or the pronation angle. But what happens between the measured plantar pressure and the GRF? How do the different parts of the sole react on the load during running? A new setup based on hall effect sensors was used to collect data of the damping and supporting behaviour of different parts of the midsole. 25 sensors were implemented into the sole to measure the sole compression with a frequency of 1000 Hz during running. Synchronized with the hall sensors also data were collected of the plantar pressure, the GRF and the Pronation angle. For this study twelve subjects in three different weight classes (60-90kg) performed 12-15 running trials with a speed of 3.5 m/s. The results show, that the sole compression during heel strike does not correlate with the subject's weight. But during the pronation and full contact phase there was a distinct correlation concerning the weight. This leads to the conclusion that proprioception and not the weight is the main influencing factor on the first impact and following the sole compression during heel strike. But the sole structures under the mid- and forefoot should take the runner's weight into account.

References Milani T.L. (2003). Biomechanical research in footwear development. Bras. J. Biom.

4(1): 15-20.

7345 We, 12:15-12:30 (P31) Theoretical and experimental considerations regarding the human impact biomechanics in rugby game

I. lacob 1 , E. Budescu 2 . 1University "AI. I. Cuza" of lasi, Faculty of Physical Education and Sports, lasi, Romania, 2 Technical University "Gh. Asachi" of lasi, Biomechanics Laboratory, lasi, Romania

The impact between two sportsmen in rugby game may have diametrically opposed, on two levels: - regarding the sportive performance, during the offensive, the sportsmen that

succeed to break up through the defensive lines of the opposite team, may gather points;

- regarding the accidents, direct taking over of the shock provoked by the impact may result in injuries that can make unavailable the sportsman.

The impact, centric or oblique, may be mathematically modeled through: the equation of impulse preservation and the equation defining the coefficient of restitutio. The second equation makes the link between a known parameter, the restitutio coefficient, and the speeds of the two sportsmen immediately before and after the impact. Depending on the value of the coefficient of restitutio, the impact may be: elastic, plastic or elastic-plastic. The paper deals with the problems of impact in rugby game from theoretical point of view, that is modeling this phenomenon, and from experimental point of view, that is determining the coefficient of retitutio. At the same time, there are analyzed the simulation performed for a team of rugby from Romania. Aided by impact model and knowing some input data (sportsmen masses and before impact velocities), we can determinate the sportsmen velocities after impact.

5450 We, 12:30-12:45 (P31) Development of an FES rowing machine - numerical simulation and design M. Kuchler, M. Gfoehler. Vienna University of Technology, Institute for Engineering Design and Logistics Engineering, Vienna, Austria

The objective of this project was to simulate an FES induced rowing process of a paraplegic person and to design an adequate FES rowing machine based on the numerical results. The model of the paraplegic-rowing machine system represents a planar 8-1ink kinematic chain with three degrees of freedom. The resistance mechanism was modeled by Euler's principal equation for flow machinery. The equations of motion were derived using the Newton-Euler equations. The following muscles are considered: Tibialis anterior, Soleus, Gastrocne- mius, Vastii, Rectus femoris, Hamstrings, Gluteus maximus, Iliopsoas, Brachio- radialis, Brachialis, Biceps, Deltoideus anterior, Deltoideus posterior, Triceps caput mediale, Triceps caput laterale, Triceps caput Iongum, Teres major, Teres minor and Latissimus dorsi. The muscles of the lower extremities are artificially activated by surface electrodes. Considering muscle atrophy the physiological cross sectional areas of these muscles have been scaled to 23% of the values of healthy subjects (Gfoehler et al., 2003).