SPRINT RUNNING BIOMECHANICS: PERFORMANCE AND INJURYRELATED VARIABLES

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Study four – Tibio-femoral joint constraints for multi-body optimization

To further improve the quality of knee joint models used in the MBO approach, the definition of non-rigid constraints which take into account the anatomy of the subject appear to be ideal. The aim of the study was to provide plausible, subject-specific values for the distances between the origin and insertion landmarks of the main knee ligaments (referred to as “ligament lengths”), during loaded continuous knee flexion-extension.
Two orthogonal digital radiographs of six knee specimens (femur, tibia, patella and fibula) were acquired using a low dosage X-ray system (EOS®, EOS-imaging, France). The 3D geometry of each specimen was then obtained by means of a reconstruction algorithm (Chaibi et al., 2011). The areas of origin and insertion of the anterior and posterior cruciate, lateral collateral, and deep and superficial bundles of the medial collateral ligaments (ACL, PCL, LCL, MCLdeep, MCLsup) were identified on femur and tibia templates using the mouse pointer by three expert orthopaedic surgeons (virtual palpation). Attachments sites were estimated for the six reconstructed knees by matching the bone templates to the low dosage stereoradiography images. Movement data of the specimens were obtained by means of a stereophotogrammetric system (Polaris, Nothern Digital Inc., Canada), using pins carrying a cluster of markers and inserted into the femur and the tibia. Data were, therefore, free from skin movement artefacts. For each knee, the centroids of the attachment areas of each ligament were determined and the Euclidean distance between the origin and insertion centroids computed (dc). The impact of the inaccuracies associated with the virtual palpation was assessed performing a Monte Carlo simulation. The Euclidean distance between each possible couple of the points thus generated (100*100 couples) was also computed during knee motion (dMC) (Fig. 5). Ligament length variations (dMC) were then calculated relative to the distances at knee full extension and expressed as percentage of the latter value for each sampled knee flexion angle.

Block start phase

The block start phase refers to the time when the sprinter is in contact with the starting blocks. Blocks have been regularly used in track competitions under the International Amateur Athletic Federation (IAAF) rules since 1948, the year of London Olympic Games. The block start phase starts when the track judge gives the “On your marks” command and ends with the athlete block clearing. After the “On your marks” command, the judge gives the “Set” order and finally a gun is fired (or else there is a final “Go” command by the judge) (Fig. 4). When the athlete hears the initial command, « On your marks », he/she moves forward and adopts a position with the hands shoulder width apart and just behind the starting line. The feet are in contact with the starting blocks and the knee of the rear leg is in contact with the track. On hearing the command « Set » the athlete raises the knee of the rear leg off the ground and thereby elevates the hips and shifts the body centre of mass (CoM) up and out. Then on the command « Go » or when the gun is fired the athlete reacts by lifting the hands from the track, swinging the arms vigorously and driving with both legs off the blocks and into the first running strides (Fig. 4).
The purpose of the block start is to facilitate an efficient displacement of the athlete in the direction of the run. The main objectives of the athlete during this phase can be summarised as follows (Tellez & Doolittle, 1984):
· To establish a balanced position on the blocks.
· To obtain a body position with the CoM as high as it is practical and slightly forward of the base of support.
· To apply a force against the blocks whose line of action goes through the ankle, knee and hip joints, the centre of the trunk and of the head.
· To apply this force against the blocks and through the body at an angle of approximately 45°.
· To clear the blocks with the greatest possible velocity.

Motion analysis (Stereophotogrammetry)

The most common method for collecting information about position and orientation of body segments in two- or three-dimensional space is the use of motion capture technology (often referred to as stereophotogrammetry), in which markers are affixed to the subject and tracked throughout the motion of interest. These systems typically use passive markers that reflect ambient or infrared light. Through manual or automatic digitization techniques, the coordinate location (two- or three-dimensional) of the markers can be determined. From these position data, the velocity and acceleration can then be calculated by taking the time derivative of the position and velocity, respectively.
Limitations inherent in both optical systems include the need of skilled operators and the fact that they can be prohibitively expensive, have a relatively small capture volume, and require a controlled environment in which to operate (Sabatini, Martelloni, Scapellato, & Cavallo, 2005). Moreover, even when the motor task of interest can be performed and analysed in a human movement laboratory, sports motor acts are particularly problematic, being characterized by high accelerations and forces. The movement of the soft tissues, and in turn of the skin-mounted markers, relative to the underlying bones (soft tissue artefact – STA) appears to be one of the major sources of error in the estimation of kinematic and, particularly, kinetic parameters in the analysis of sports techniques (de Leva & Cappozzo, 2006). In this respect, there is a need for research in the development of new methodologies specifically designed for sports applications.
Edge detection technology recently has shown promise in allowing gait parameters to be quantified directly from video sources alone, allowing the same information to be extracted as that from marker-based systems, without the associated limitations or need for markers.
As concerns the use of treadmills, although they are often used in the analysis of walking and running gait to overcome issues surrounding small capture volumes, their use is believed to induce gait adaptations, such as increased time in stance phase, that normally would not be observed in over-ground running (Dugan & Bhat, 2005). These changes appear to be speed dependent (Nigg, De Boer, & Fisher, 1995). Moreover, sprint running can be hardly reproduced on treadmills. To overcome problems related to the small capture volume, the use of new small and portable technologies seems to be effectively ideal.

Electrogoniometers

Electrogoniometers allow for the direct measurement of joint angles during continuous dynamic activities. They offer a simple, affordable alternative to motion capture systems and allow joint angle data to be collected and viewed instantaneously. The end blocks of the electrogoniometers typically are affixed to the skin on either side of the joint axis of rotation using double-sided adhesive tape, as specified by the manufacturer. This method has been found to result in excessive sensor motion (Rowe, Myles, Hillman, & Hazelwood, 2001), which potentially could be magnified during the high-speed changes in joint angle commonly experienced during running. This unwanted sensor motion can be effectively reduced through the use of additional adhesive tape (Piriyaprasarth, Morris, Winter, & Bialocerkowski, 2008) or application of pre-wrap and athletic tape (Dierick, Penta, Renaut, & Detrembleur, 2004). For prolonged periods of data collection, special suits have been fabricated, which facilitates attachment of the electrogoniometers using hook and loop fasteners (Pierre et al., 2006). Failure to prevent sensor motion and improper alignment of the sensor during the application procedure has been found to be the greatest potential contributor to measurement error (Rowe, Myles, Hillman, & Hazelwood, 2001; Wong, et al., 2007; Piriyaprasarth, et al., 2008). Measurement error from electrogoniometers has been shown to be as small as 0.04 degrees (Piriyaprasarth, et al., 2008) and has been validated using both human (Rowe, Myles, Hillman, & Hazelwood, 2001) and mechanical (Piriyaprasarth, et al., 2008) testing protocols, with results comparable to those obtained using motion capture systems (Rowe, Myles, Hillman, & Hazelwood, 2001). Although studies have shown inter- and intra-tester reliability to be relatively high, it has been suggested that the same tester be used when possible to ensure the highest repeatability (Piriyaprasarth, et al., 2008). When applied correctly, electrogoniometers have proven to be highly accurate and highly sensitive for detecting changes in joint angles over time, providing a simple, small, portable, and affordable alternative to motion capture systems (Rowe, Myles, Hillman, & Hazelwood, 2001).

Sports biomechanics and in-field performance evaluation

Athlete’s performance evaluation is one of the main issues of coaching. An optimised performance is obtained when accurate, useful, and timely feedback about athlete proficiency is provided. A successful coaching outcome is related to the ability of coaches and trainers to correctly analyse all the deterministic details of the performance. In this way, training programs can be organized to specifically target performance defects. Failure in providing such feedback implies a reduction of chances of improvement (Carling & Williams, 2005). Therefore, athlete monitoring, evaluation and training planning should be based on systematic, objective and reliable approaches to improve data acquisition and information processing. In this respect, a gap exists between sport science research and coaching practice (Goldsmith, 2000). The link between research and coaching practice needs to be reinforced, especially in élite sports, where coaches are progressively going to incorporate the outcomes of sports science research in their in-field activity (Williams & Kendall, 2007).
Sport coaches aim naturally at improving athlete’s performance and at reducing injury risk. These two objectives are also shared by sport biomechanists. The science of biomechanics is concerned with the forces that act on human body and the effects these forces produce. Physical education teachers and coaches of athletic teams, whether they recognise it or not, are likewise concerned with causes (forces) and effects (movement). In fact, a sport technique can be defined as the way in which body segments move in relation to each other during a movement task. Coaches’ ability to teach sport techniques depends largely upon their understanding of both the effects they are trying to produce and the forces that cause them. Thus, the main goal of sport biomechanists should be to provide information to coaches and athletes about sports skill technique that will assist them in obtaining a good technique, characterised by effective performance (the purpose of the movement) and decreased risk of injury (distribution of forces in muscles, bones, and joints so that no part is excessively overloaded). Poor techniques are characterised by increased risk of injury, even though performance may be effective, at least for a while.
Sport scientists basically use two approaches to analyse athletes’ technique: qualitative and quantitative. A qualitative analysis is based on the systematic observation of the motor task, directly and/or via film or video (Knudson, 2007). The effectiveness of this approach depends on the operator ability to observe accurately the task and to know what to look for, i.e. on the operator knowledge and experience and, in particular, his/her ability to identify the mechanical requirements of the movement under analysis. Since human observation is generally not sufficient to provide accurate and objective information, the use of objective data, specific measuring tools, and correct interpretation and application of the findings are required to optimise the coaching process. This circumstance calls, therefore, for the use of quantitative analysis. A quantitative analysis is based on measurements of the kinematic (displacement, velocity, acceleration) and kinetic (force, moment, power) variables that determine performance. As regards the mechanical determinants of performance, relevant assessments have been, so far, typically constrained into laboratory settings. Laboratory-based instrumentations are accurate, but the volume of capture is limited and may even vitiate the execution of the motor task under analysis. Here is where sports biomechanics fails in providing useful and exploitable information to coaches and trainers.
A perfect example of the “breakpoint” between sports biomechanics and performance evaluation is represented by Mr. Pistorius with his emblematic 400 m race during the 2007 Golden League in Rome. After his brilliant vamp in the last 200 m, a group of researcher was requested (by the International Association of Athletics Federations, IAAF) to investigate about his sprint performance ability and determine whether or not he could have had mechanical advantages from his prostheses. Two groups of researchers performed different mechanical and physiological tests on Mr. Pistorius. The first expressed its positive opinion regarding the advantages coming from the prostheses, while the second group expressed the opposite attitude. This report was then followed by the sentence emitted by the Court of Arbitration for Sport (Lausanne, May 2008) which stated “Having viewed the Rome Observations […] the IAAF’s officials must have known that […] the results would create a distorted view of Mr. Pistorius’ advantages and/or disadvantages by not considering the effect of the device on the performance of Mr. Pistorius over the entire race”. In fact, the researchers’ investigation was performed using lab-based instrumentations and, thus, was limited to few steps. Conversely, in cases like this, a mechanical characterisation of the motor task must be carried out for the entire race. The abovementioned circumstance might be overcome when adequate technological advancements are made available.
Technological advances and improvement of procedures have been and continue to be an important issue in sport and exercise science (Winter, et al., 2007). To cope with the fast development of new technological advancements, sports scientists and coaches are asked to continuously account for new developments and include them in the everyday assessment of training and competition. Specifically, technologies used to measure performance are moving forward and improved methods based on “state of the art” computer technology and robotic automation are being continually developed and commercialised to support the pursuit of success in elite sports (Carling, Reilly, & Williams, 2009). Systems of measurement benefitted of such trend with a progressive device miniaturisation, increased reliability and cost reduction. Simultaneously, data processing took advantages of greater calculation power, speed and interactivity. Last but not least, the development in the use of expert systems such as, for example, Artificial Neural Networks, as diagnostic tools for evaluating faults in sports techniques is advocated as promising in the near future (Bartlett, 2006).

Table of contents :

CHAPTER 1 – THEORETICAL BACKGROUND
ABSTRACT
1.1 INTRODUCTION
1.2 SPRINT RUNNING BIOMECHANICS: PERFORMANCE AND INJURYRELATED VARIABLES
1.2.1 Block start phase
1.2.2 Acceleration or pick-up phase
1.2.3 Maintenance phase
1.2.4 Deceleration phase
1.3 METHODS FOR SPRINT RUNNING ANALYSIS
1.3.1 Electromyography
1.3.2 Motion analysis (Stereophotogrammetry)
1.3.3 Force plates
1.3.4 Pressure sensors
1.3.5 Accelerometers
1.3.6 Gyroscopes
1.3.7 Electrogoniometers
1.4 THEORETICAL BACKGROUND: DISCUSSION
CHAPTER 2 – AIM OF THE THESIS
CHAPTER 3 – LOW RESOLUTION APPROACH
3.1 INTRODUCTION
3.1.1 Sports biomechanics and in-field performance evaluation
3.1.2 Wearable inertial sensors
3.2 STUDY 1: TRUNK INCLINATION DURING THE SPRINT START USING AN INERTIAL MEASUREMENT UNIT
3.2.1 Introduction
3.2.2 Materials and methods
3.2.3 Results
3.2.4 Discussion
3.3 STUDY 2: INSTANTANEOUS VELOCITY AND CENTER OF MASS DISPLACEMENT OF IN-LAB SPRINT RUNNING USING AN INERTIAL MEASUREMENT UNIT
Abstract
3.3.1 Introduction
3.3.2 Materials and methods
3.3.3 Results
3.3.4 Discussion
3.4 STUDY 3: TEMPORAL PARAMETERS OF IN-FIELD SPRINT RUNNING USING AN INERTIAL MEASUREMENT UNIT
Abstract
3.4.1 Introduction
3.4.2 Materials and methods
3.4.3 Results
3.4.4 Discussion
3.5 LOW RESOLUTION APPROACH: DISCUSSION
CHAPTER 4 – HIGH RESOLUTION APPROACH
4.1 INTRODUCTION
4.1.1 Sport biomechanics and injury prevention
4.1.2 Joint dynamics estimation
4.2 STUDY 4: TIBIO-FEMORAL JOINT CONSTRAINTS FOR BONE POSE ESTIMATION DURING MOVEMENT USING MULTI-BODY OPTIMIZATION- 94 –
Abstract
4.2.1 Introduction
4.2.2 Materials and methods
4.2.3 Results
4.2.4 Discussion
4.3 HIGH RESOLUTION APPROACH: DISCUSSION
CHAPTER 5 – CONCLUSIONS
AKNOWLEDGEMENTS
REFERENCES

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