EVALUATING SPORT SHOES USING GROUND REACTION FORCE DATA

INTRODUCTION - The measurement of ground reaction forces (GRF) has been used for many years in biomechanics to quantify external forces during locomotion. The use of force platforms to measure GRF's dates back to Marey in the late 1890's. Since the 19701s, GRF data have been used in the evaluation of sport shoes. The GRF components measured consist of three force components (vertical, antero-posterior (Alp) and medio-latera1 (MIL)) and three moments about the corresponding axes. These values can be used to calculate the center of pressure, the free moment and the resultant force. Furthermore, a number of GRF parameters have been derived to evaluate shoe. FORCES IN LOCOMOTION The vertical force for heel-toe running usually exhibits two peaks; an initial peak often referred to as the passive or impact peak and a second peak referred to as the active peak. The impact peak generally occurs about 5 to 30 ms after ground contact. The active peak generally occurs in the middle of the support phase between 100 and 250 ms after ground contact. Impact forces are the result of the collision of the foot and the ground. The magnitude and the time at which the peak occurs depends on a number of factors including running speed, running style and shoe construction. The primary portion of the shoe that influences the impact peak is the midsole. Midsoles are constructed of many types of materials and have various geometrical constructions. Generally, peak impact forces occur earlier in the support phase during barefoot running compared to shod running and with firm midsole shoes than with soft midsole shoes. The second or passive peak is generated by movements that are controlled by muscular activity. The magnitude and time of occurrence for this peak is not generally affected by footwear construction. The mediolateral GRF component is often linked to the pronation and supination actions of the foot. .Attempts to make this link have not proven particularly fruitful. Bates et al. (1 983) reported significant differences in the MIL impulse between running shoes and suggested these differences were related to pronation. The free moment is often used as a friction coefficient to evaluate the resistance of a shoe to rotation. However, it has also been used to measure the pronation action of the foot. Holden and Cavanagh (1991) used the free moment to evaluate shoes that were specifically constructed to place the foot in a pronated, supinated or neutral position. They reported that the footwear could be differentiated using this technique in that greater the maximum free moment the greater the degree of pronation. Attempts to relate the center of pressure (COP) to differentiate footwear has been unsuccessful. Vililliams (1985) suggested that this was because the COP is a global measurement and does not account for subtle changes that might have occurred during the support period. GRF INTERPRETATION - Cavanagh (1 987) suggested that footwear can affect the GRF patterns recorded although not as drastically as one might have imagined. Bobbert et al. (1991) calculated an estimation of the vertical GRF component from the positional data of the center of mass of each body segment. They calculated the vertical GRF component as: Fz = ~ i =nml i (azi - g) where mi is the mass of the ith segment, azi is the vertical acceleration of the ith segment and g is the acceleration due to gravity. Thus, GRF-data reflect the motion of the center of mass of the runner and not necessarily the motion of the foot at the foot-ground interface. It is evident, therefore, that attempts to differentiate between footwear types is extremely difficult. Far example, the differences between hard and soft midsole shoes are not clear when evaluated by GRF data. In some studies, the GRF data indicate that there are no differences in impact characteristics between hard and soft midsoles although impact tests on the midsole materials show differences. Theoretically, a hard midsole will increase the impact peak and decrease the time to impact. Then the impact peak is summed with the active peak, the result is no change in the peak value. Also, subjects may adjust their kinematics to place the body in a position to better attenuate the impact. In some studies, the difficulty in differentiating footwear is a result of an inappropriate number of trials (Bates et al., 1983). However, most of the problem concerns the fact that GRF data are not a direct measure of the forces at the foot. That is, GRF data represents the force acting on the centre of mass, although it is applied at the foot/ground interface. Conclusions—GRF data have often been used to evaluate athletic footwear. However, given appropriate methods and statistical procedures, the data still must be viewed with caution because GRF data represent the accelerations of the center of mass. Thus the difficulty in interpretation of shoe differences is that GRF data are a “remote” measure of lower extremity action

Rearfoot and forefoot footfall patterns: implications for barefoot running

Approximately 75-80% of runners initiate contact with the running surface on their heel and thus have a rearfoot or heel-toe footfall pattern. The remaining 20-25% initially contact the ground with the foot flat with a subsequent heel contact (midfoot pattern) or on their forefoot with no heel contact (forefoot pattern). It is unclear why different footfall patterns exist or why some runners naturally use different patterns. Some contemporary training programs advocate the adoption of a mid- or forefoot footfall pattern but there is little scientific evidence that a particular strike pattern is more efficient or less injury-prone than other patterns. In this presentation, several studies that investigated differences among the footfall patterns relative to oxygen consumption, impact characteristics, surface alterations and lower extremity coordination will be presented. In addition, two modeling studies will also be discussed. One study will determine, using optimization techniques, the passive and active characteristics on the triceps surae. The other study is a forward dynamics study with different cost functions describing the different footfall patterns. Our basic premise in these studies is that different footfall patterns serve different functional roles in human running: a heel- or midfoot strike is used for endurance running, and a forefoot strike is used for sprinting. We propose that one’s footfall pattern is an intrinsic dynamic and thus difficult to alter. However, the change from shod to barefoot running often requires an alteration in footfall pattern that may ultimately lead to injury

THE EVOLUTION OF ATHLETIC FOOTWEAR

The purpose of this presentation is to discuss the evolution of athletic footwear and how biomechanics has influenced this evolution. Footwear has undergone a significant evolution from the Paleolithic period to modern times. The origins of footwear emphasized protection from the environment. During the Egyptian, Greek and Roman eras, the need for military shoes drove the development of footwear. It was not until the 19th century that specific footwear for athletic performance was designed. Footwear were improved significantly during the first half of the 20th century but it was not until the latter portion of this century that biomechanics truly had an influence on footwear design. The intersection of biomechanics, injury risk factors and footwear development paralleled the growth in lower extremity research. More recently, the interest in barefoot running has driven the development of minimalist footwear

KNEE POWER IN LOW BACK PAIN SUBJECTS DURING RUNNING

The purpose of this study was to examine lower extremity shock absorption between runners with and without low back pain. We compared data from three groups based on low back pain status: current low back pain, resolved pain after a single bout of low back pain and runners who never had low back pain (CTRL). All subjects ran at least 20 km per week and ran on a force treadmill at 3.8 m•s-1 while kinematic and kinetic data were collected. Work was determined from joint power histories during the shock attenuation portion of the stance phase. Individuals with a history of low back pain exhibited less peak knee negative power and negative work suggesting that they exhibited decreased eccentric muscle activity during foot-ground impact. The results of this study suggest that decreased eccentric activity of the muscles crossing the knee joint is associated with individuals who have low back pain and, to a lesser extent, with those who have residual low back pain. We suggest that the decreased eccentric activity can result in the footground impact shock wave moving through the lower extremity with little attenuation to the low back region

RUNNING INJURIES: FOREFOOT VERSUS REARFOOT AND BAREFOOT VERSUS SHOD: A BIOMECHANIST’S PERSPECTIVE

In recent years, there has been a debate regarding the use of different footfall patterns to reduce injury risk and enhance performance. Humans have three footfall patterns available to them when running: rearfoot, midfoot and forefoot. These different patterns are distinguished by the portion of the foot that’s makes initial contact with the ground. Interestingly, until very recently, there has been little research to show the pros or cons of the various footfall patterns. Here we will discuss several studies that have been carried out to distinguish footfall patterns in terms of kinematics and kinetics, running economy, the effect of surface and coordination on the risk of running injury

ESTIMATING LOWER LIMB JOINT MOMENTS IN GAIT USING COMMON MACHINE LEARNING APPROACHES

The aim of this study was to investigate the efficacy of common machine learning algorithmic approaches to estimate lower limb joint moments during fast walking gait. Kinematic and ground reaction force data on 19 participants were captured with a force-plate and motion caption capture system. Inverse dynamics was used to calculate the right lower limb joint moments and common machine learning algorithmic approaches, such as Random Forest (RF), Linear Regression (LR), Neural Network (NN), AdaBoost (AB) and Gradient Boosting, were used to predict the corresponding joint moments using only the kinematic data. High coefficient of determination values (R2\u3e0.9) for predicting moments using random forest, neural network and AdaBoost are observed in for the ankle, knee and hip joints in frontal, sagittal and transverse planes. The other approaches had R2 values between ranged 0.71 and 0.97. This suggests that common machine learning algorithms may be a feasible approach to estimate joint moments during fast walking in a clinical setting for monitoring sport injury prevention and management

MENSTRUAL CYCLE AND FRONTAL KNEE LOADING DURING A CUTTING MANOEUVRE – A PILOT STUDY

The aim of this study was to compare the knee internal adduction moment during an unanticipated cutting manoeuvre in different phases of the menstrual cycle. Data were collected using a motion capture system integrated with a force platform. The knee internal adduction moment of four young women were compared in menstrual and ovulation phase of the menstrual cycle. The results of this study showed that knee internal adduction moment is higher in the time of ovulation phase and may increase the potential risk for noncontact ACL injury during this phase of the menstrual cycle. These results could affect the development of training schedules for women athletes

THE RELATIONSHIP BETWEEN ACHILLES TENDON DIMENSIONS AND FOOT STRIKE INDEX IN REARFOOT AND NON-REARFOOT RUNNERS

The purpose of this study was to describe the relationship between Achilles tendon dimensions and foot strike index in rearfoot and non-rearfoot runners. 107 recreational runners were divided into a group of rearfoot (n = 88) and a group of non-rearfoot runners (n = 19). Achilles tendon dimensions were measured by a combination of ultrasonography imaging and kinematic analysis. To analyse the footfall pattern, each participant performed 8 successful trials of running at their stated self-preferred endurance speed. Partial correlation was used for statistical analysis. Runners in the group of non-rearfoot runners, whose footfall pattern is more over forefoot, have a longer and thinner Achilles tendon