Technology is being used more and more to aid elite athletes in improving their performance. Recently there have been promising developments in the research using IMUs regarding wheelchair rugby and basketball. Applying this same measurement setup to wheelchair tennis could give better insight in the wheelchair mobility of wheelchair tennis athletes. This knowledge could aid the athletes in causing less sport related injuries, altering the focus of their training or even training entirely new aspects of the sport. This is why this research will be answering the research question: “What is/are the most influential factor(s) regarding the mobility of wheelchair tennis athletes to discriminate between the winning and losing player?”. We define the wheelchair mobility performance as all the measurable parameters related to the movements of the athlete. These mainly consist of the velocities and accelerations reached by the athletes. The measurements resulted in three different ways to output variables. Match statistics, these include information about the distance travelled, turns made and points played during the match. This was chosen to give general insight into the match. The wheelchair mobility performance (WMP), shows information about the velocities and accelerations during the match. This was chosen to compare the velocities and accelerations of athletes, they are plotted against the average value of the entire database of all measurements, this will be a standardized baseline to compare the results to. Lastly the speed zone plots, the plots give an indication of how long the athlete has been driving within a certain velocity range. To give better insight in the dynamic of the match all outputs also includes the constraints of winning/losing and serving/receiving. It is therefore possible to compare the differences between won and lost points and served and received points. There was also experimented with the possibility of making a plot that shows the location of the athlete on the field as a heatmap. However, because the IMUs only record accelerations the cumulative error to distance had to be manually adjusted for, therefore this was only done for one match as a pilot case. The match statistics, WMP plots and speed zone plots showed no significant differences between winning and losing points. Therefore, this study found that the mobility performance has no deciding effect on the outcome of a tennis match. The technique used to hit the ball and place it on the right spot in the field will probably be the determining factor here. This said, the mobility does however have to be above a certain threshold for the player to compete at a certain level of play, so neglecting it completely is not recommended, however the focus should be on the technique that involves hitting the ball. The heatmap case shows that the winning player plays more aggressive, but whether he is playing aggressive because he is winning or winning because he is playing aggressive cannot be concluded by this research.

In football, a lot of hip and thigh muscle injuries occur as a result of high muscular loads due to accelerative leg movements. To prevent muscle damage and optimise performance, it is essential to continuously identify when and how frequent local hip and thigh muscular loads develop in the explosive and dynamic football environment. The currently used method is an acceleration index based on two-dimensional position data of the whole global body measured by the Local Positioning Measurement system. The problem is that this system does not correspond with the experienced load of players because leg movements are excluded. Therefore, this study introduces a new local concept of gathering local three-dimensional leg acceleration data by inertial measurement units.
This pilot study aims to use a big data analysis approach to translate leg acceleration data into a measure to indicate local muscle load and compare this new local and the current global method to the players’ experienced load. Five participants performed specific football drills with an intensity increase from jogging to sprinting and by adding a pass and shot. Measures are developed, based on the pelvis, upper leg, and lower leg accelerations, by a peak and cumulative data analysis approach. By evaluating trend percentages of the intensity increase, it is obtained that a local acceleration measure is comparable to the players’ experienced load if it considers the sum of normal or peak data points weighted per zone and per travelled distance. Furthermore, a similar result is obtained when only the upper leg or lower leg accelerations are considered.
It can be concluded that local three-dimensional acceleration of the lower extremities, processed with a big data analysis approach, represent the football players’ experienced muscular load more accurate than the current global method. Further research, including a higher number of participants, should prove the significance.

In Bicycle Motocross (BMX) racing, a fast gate start is crucial for overall race performance (Rylands2014). The goal of this study was to (1) provide a better understanding of how cyclists effectively propel the bicycle during BMX gate start, and (2) identify pedalling variables which are good predictors of a fast start."

Running-specific prostheses enable amputee athletes to perform at the highest level. However, current prosthesis design still impairs sprinting performance in multiple ways. An example is the constant mechanical stiffness of the prosthetic devices. The dynamic behaviour of the blade is determined by this property, but it is static and cannot change according to the gait phase-specific sprinting dynamics.
During acceleration a different stiffness might be required as opposed to steady state running. Therefore, it is hypothesised that amputee athletes can improve their performance with prostheses that have a gait phase-specific stiffness.
In order to investigate the effect of stiffness on amputee sprinting performance a novel and unprecedented modelling approached is used. Modelling is economical and straightforward in comparison to other research methods such as laboratory experiments. In this thesis, an extension of the established Spring-Loaded Inverted Pendulum model is proposed that qualitatively describes amputee sprinting motion. The Actuated Spring-Loaded Inverted Pendulum (ASLIP) is capable of predicting stable forwardly integrated sprinting motion with the inclusion of the start and acceleration phase. Optimisation of the model predicts that phase-specific spring stiffness leads to a significant time reduction on the 100m sprint for given physiological parameters. In general it can be said that a stiffer spring results in better performance. More specifically, the model benefits from a stiff spring during acceleration and a more compliant one in steady state. Although it has its limitations, the ASLIP model additionally provides a valuable insight into the mechanics of amputee sprinting. For example, it seems that optimal phase-specific stiffness is strongly dependent on biomechanical parameters such as touchdown angle, force angle and CoM velocity. Future work in this direction can provide a better understanding of the
underlying mechanisms that determine amputee sprinting performance.
The outcomes of this study suggest that amputee sprinters might be able to achieve a reduction in finishing time with prosthetic devices that have a phase-specific stiffness. The modelling approach used in this thesis is promising and lends itself well to investigate this opportunity in more detail.

The purpose of this thesis project is to design a new concept of sprinting prosthesis to enhance the acceleration phase performance of bilateral transtibial amputees (BTAs) competing in sprinting races. Amputee sprinters are hampered in the acceleration phase of a sprint race by the compliance of their prostheses. Increased contact times, and small and too vertical ground reaction forces limit the acceleration performance of BTAs. A variable stiffness prosthesis, with a higher stiffness at the initial steps of the acceleration phase, would reduce contact times and increase forward propulsion. A new variable stiffness design is proposed in this report. A prototype of this new design is created and characterized by retrieving the stiffness matrices and ellipses. Ottobock's 1E90 prosthesis is also characterized, and the results are compared. For this, a new testing methodology to obtain the stiffness ellipses of running prostheses (RPs) is proposed.

Performance in wheelchair court sports is to a large extent determined by the wheelchair mobility performance, the performance measure for the wheelchair-athlete combination. So far, wheelchair mobility performance is mostly utilized as concept, rather than a well quantified measure. However, in order to gain insight in the interaction between athlete, wheelchair and sport, it should be an objective and well quantified outcome that is easily measured. An inertial sensor-based “Wheelchair Mobility Performance Monitor” (WMPM) was developed that met the demands of objective quantification of mobility performance in an easy to use manner. This WMPM is believed to be a valuable tool for wheelchair court sports practice and research. All research done with the WMPM showcases its opportunities and commenced the unravelling of the complex interactions between athlete, wheelchair and sport. It will be a matter of time before the use of the WMPM will be common practice in wheelchair sports and sports research.

During team time trials in road cycling changing schemes are used to spread the workload over the cyclists in the team. Models that provide predictions of race performance already exist for individual time trials. It is proposed that with a performance model for team time trials, the performance of different strategies can be compared and optimised.
In literature combinations of mechanical resistance models and physiological models are used to determine the performance of individual time trials. The aerodynamic interaction between cyclists is very important to the effectiveness of a strategy. Coefficients of drag reduction between cyclists in a team time trial are presented in several studies, however most studies use groups of only four cyclists, which is not useful for a team time trial with eight cyclists. Only two studies report data for groups up to eight cyclists. These two models show different behaviour and are both used to asses the performance of strategies. Also two physiological models were used.
In the model provided in this study the resistances are calculated from the kinematics resulting from the evaluated strategy. The mechanical resistance model, including the aerodynamic interaction model calculates the power required to perform the strategy. The physiological model calculates the physiology during the race, which determines if the cyclists are able to sustain the prescribed strategy.
Genetic algorithm optimisation is used to optimise the strategy parameters, such as initial position and times spend in first position. The velocity is optimised for each evaluated strategy configuration. A convergence test was performed to determine the parameters for the genetic algorithm, which are used in the optimisation of strategy.
Using the standard strategy, where cyclists only change from first to last position, different orders are compared. From this study it was determined that the mean velocity over a 30 km team time trial could be raised by a maximum of 0.228 m/s by improving the order, depending on the model configuration. It was found that the best performing orders were those where the mean performance difference of following cyclists was lowest.
Two different strategies have been assessed where cyclists still always change from first, but not necessarily last position have been assessed on their performance. With those more complex strategies the mean velocity could be increased with 0.358 m/s over a 30 km team time trial.
The model still lacks validity, but gives a relevant insight in the performance of different team time trial strategies. The validity can either improved by using track test to validate the drag reduction coefficients or by using power data from a team time trial to show that the model predicts realistic physiology. Of those two methods the last is preferred.