Introduction: Unrestricted elbow range of motion (ROM) is essential for daily function, yet flexion and extension limitations frequently occur following elbow trauma. These restrictions may result from osseous deformities, soft tissue contracture, or both, requiring accurate identification of the dominant cause to guide treatment. Although CT imaging provides detailed evaluations of bony pathology, conventional single CT lacks functional information, and dynamic 4D-CT faces clinical limitations. Therefore, alternative approaches to assess osseous contributions to motion restrictions are needed.

Objective: This study aims to introduce and validate a 3D computational kinematic bone model based on segmented single CT scans. The model enables patient-specific simulation of elbow flexion and extension, enabling quantifiable assessment of osseous related motion restrictions to support clinical decision making.

Methods: Eight non-affected cadaveric elbows were scanned using computed tomography in seven positions ranging from full extension to full flexion and segmented into 3D models. Anatomical landmarks were automatically identified to define a landmark-based rotation axis (LMA), which was validated against a kinematically derived average helical axis (AHA). The primary outcomes were the angular deviation and the minimal distance between the axes. The simulated ulnar motion was generated by applying finite helical axis-derived rotations to the neutral ulnar pose. Predicted poses were compared to scanned poses at each flexion angle, with translational and rotational deviations calculated to assess simulation accuracy. Clinical applicability was evaluated by applying the model to bilateral CT scans of ten patients with elbow flexion and extension restrictions. Model-based ROM was determined by simulating flexion and extension and identifying the onset of osseous impingement, and compared to clinically recorded ROM.

Results: Alignment between the LMA and the AHA showed a mean positional difference of 0.41 mm (SD 0.22) and an angular deviation of 2.76° (SD: 1.32). LMA-based simulated ulnar poses demonstrated increasing translational and rotational errors with flexion, predominantly in distal, lateral and valgus directions. Specimens exhibited impingement exclusively in the ulnohumeral region at the trochlea and the greater sigmoid notch. In patients, model-based ROM showed a mean absolute difference of 6.7° for terminal extension and 14.2° for terminal flexion, with false-positive impingement primarily observed in the ulnohumeral region.

Conclusion: This study validated a 3D computational model for simulating elbow flexion and extension using single CT scans. The model accurately estimated ROM and identified osseous impingement locations. Although it demonstrated sensitivity to false-positive detections, it provides a clinically applicable tool for assessing osseous motion restrictions. Future improvement should focus on enhancing landmark accuracy and potentially restricting impingement detection to anatomically relevant regions.

This study investigates the key factors influencing the standing start in track cycling, aiming to identify the optimal technique for minimising starting time. High-speed video analysis and statistical methods were employed to analyse the standing starts of two cyclists, examining various movement and power variables to determine their correlation with the 15m split time. The gear ratio significantly impacted the 15m, 65m, and 125m split times.

Three hypotheses were tested. The first hypothesised that the more explosively a cyclist accelerates their body forward, as indicated by the maximum horizontal velocity of the hip and shoulder during the start, the faster the 15m split time. The results showed that the horizontal velocity of the hip did not significantly influence starting time. However, the horizontal velocity of the shoulder did have a significant impact, although the direction of the correlation differed between the two cyclists.

The second hypothesis proposed that better timing of hip acceleration would lead to a lower 15m split time. No significant results were found to support this hypothesis.

The third hypothesis stated that the optimal start movement would be associated with the highest power output during the first pedal stroke without resulting in slower split times. The normalised peak power output during the first pedal stroke demonstrated a strong, significant correlation with the 15m and 65m split times. Additionally, the maximum vertical hip velocity was significantly correlated with the 15m split time. Both vertical and horizontal hip velocity were correlated with normalised peak power, as was the angular velocity of the knee, partially supporting the third hypothesis.

These findings suggest that optimising the gear ratio and power production during the first pedal stroke is critical for a successful standing start in track cycling. Future research should investigate the optimal gear ratio by examining its effect on the 250m split time, as the ultimate goal for the starting rider is to complete the first lap as quickly as possible. Furthermore, the relationship between peak power production during the first pedal stroke and the 125m and 250m split times should be explored. If peak power production proves to be a reliable predictor, research should focus on identifying the movement that maximises peak power output during the first pedal stroke.

This thesis discusses the potential of the RHIDE system, a device designed to measure
in-field wheelchair handrim push characteristics. The research question is if the RHIDE
can accurately discriminate propulsion/recovery and determine contact/release angles. This
research question is answered by determining push time (τ ), contact angle (α), release angle
(β) and push time (∆) for each push. To validate the RHIDE, these biomechanics are
measured with two different systems, one being the RHIDE and the other being the MarkerLess and Machine Learning Vision System (MLVS). Validation is conducted using data
from an experiment comprising 12 wheelchair propulsion-related tests on an ergometer, with
variations in speed (S1 and S2) and resistance (R1 and R2). For every push, τ , α, β and
∆ are extracted with both systems and the reliability of the RHIDE is calculated using an
ICC(2,1) on absolute agreement of the biomechanics for all pushes. For τ , ICC values are
0.65 for S1R1, 0.88 for S1R2, 0.98 S2R1 and 0.96 for S2R2 therefore reliability is considered
moderate, good, excellent and excellent respectively. For α, ICC values are 0.67 for S1R1,
0.59 for S1R2, 0.56 S2R1 and 0.51 for S2R2 giving moderate reliability for all tests. The
β ICC values are 0.57 for S1R1, 0.34 for S1R2, 0.35 S2R1 and 0.26 for S2R2 giving poor
reliability for all tests except S1R1. At last, ∆ ICC values are 0.17 for S1R1, 0.52 for S1R2,
0.25 S2R1 and 0.14 for S2R2, giving poor reliability for all tests except S1R2. Overall,
the RHIDE is able to accurately discriminate propulsion/recovery phases. Touch/release
angles are not reliable between systems. There are two potential causes for this. Firstly,
the RHIDE could improve its accuracy by increasing sample rate, streamline data pathways
and the introduction of a calibration protocol. Secondly, both systems measure different
contact/release angles and are therefore not comparable in this matter.
Overall, RHIDE has the potential to occupy a unique position in wheelchair propulsion
analysis by enabling the measurement of daily activities. With RHIDE, it would be feasible
to (1) provide information about propulsion technique by supplying push/release angles and
push times and (2) offer insights into daily wheelchair use over extended periods.

Manual wheelchair users, especially those with spinal cord injuries, often suffer from shoulder overuse and pain. Understanding the specific role of the muscles involved in wheelchair propulsion is essential to prevent these complications. Yet, there is uncertainty about which muscles are primarily recruited, and their contribution to propulsion power. For example, some studies have suggested that the rotator cuff muscles are the primary contributors to wheelchair propulsion. Several studies have explored muscle activation patterns using musculoskeletal models, but often omit glenohumeral stability and key muscles like the trapezius, serratus anterior, and rhomboideus. Investigating muscle work provides insights into the actual mechanical output, offering a more accurate assessment of efficiency by considering factors like muscle force and movement distance.

The aim of this study was to examine individual muscle contributions to mechanical work during the push and recovery phases of wheelchair propulsion using a musculoskeletal model and to identify the role of rotator cuff muscles. The thoracoscapular shoulder model featuring accurate scapula kinematics, inclusive of the rhomboideus, serratus anterior, and trapezius muscles, was employed. In a previous experiment electromyography, kinematic and pushrim forces of 5 persons with paraplegia were collected. The filtered pushrim forces and the kinematic data as well as the scaled musculoskeletal model were inputs to the rapid muscle redundancy solver, that enforced glenohumeral joint stability while estimating muscle activations and muscle power. Muscle work was integrated from the estimated muscle power and normalized by the total mechanical work. During the push phase, significant contributions came from the pectoralis major, anterior deltoid, infraspinatus, serratus anterior, triceps brachii, and biceps brachii. In contrast, the recovery phase primarily involved the teres major, subscapularis, trapezius, posterior deltoid, middle deltoid, and rhomboideus. The contribution of rotator cuff muscles to propulsion was noted but to a lesser extent than previous reports, with no contribution from the supraspinatus. Surprisingly, the teres major showed high work values, possibly due to insufficient activation of the latissimus dorsi. Our findings support the importance of incorporating muscles like the serratus anterior, trapezius, and rhomboids into musculoskeletal models. Furthermore, the contribution of reserve actuators to total work generation was below the threshold of 5%. The comparison between estimated muscle activations and measured electromyographic activations generally showed excellent to good magnitude matching. This study's outcomes can aid in designing ergonomic wheelchairs and guide the development of tailored rehabilitation and training methods to decrease upper extremity stress in wheelchair users.

Objective
Insight into the three-dimensional (3D) healthy growth of the radius and ulna remains limited, as orthopedics rely primarily on simple two-dimensional radiographic measurements. In this study, we used statistical shape modeling (SSM) to explore 3D forearm shape variations during healthy growth and predict individual radial and ulnar anatomy over time.

Methods
We trained SSMs using CT segmentations of 117 healthy radii and 116 ulnae. Principal Component Analysis (PCA), with and without Procrustes scaling, extracted general shape variation, while we used Partial Least Squares Regression (PLSR) to create an average growth model and prediction models. We developed several methods using age, sex, bone length and/or initial anatomy to predict bone anatomy over time. The models were validated by comparing the predicted bone models to the original segmentations of follow-up samples of 22 radii and 19 ulnae. The primary outcome measure was Root Mean Squared Error (RMSE), complemented by bone length error and mean and maximum distance errors.

Results
Size differences dominated the unscaled PCA- and PLSR-based SSMs, while the scaled PCA captured more subtle shape variations. With PLSR, we extracted the longitudinal growth trajectory for boys and girls aged 4 to 18 in 3D from the cross-sectional data. Using initial anatomy and age as input, the validation samples were predicted with RMSEs of less than 1 mm. The lowest bone length errors were reached using age, sex, and bone length as predictors.

Conclusion
We successfully utilized SSM to investigate and predict healthy growth of the radius and ulna in 3D. The current models are clinically viable as reference anatomy in pre-operative planning, and the methodology shows promise to support diagnosing and treating forearm deformities.

Sport-related injuries occur due to a complex interaction of many internal and external risk factors gathered in a pattern of either positive adaptation (increased fitness), or negative adaptation (injury). The repetitive nature of the high-speed full-body pitching movement exposes the pitcher’s elbow to high loads. This thesis illustrates a novel approach to individualised injury risk prediction that accounts for the dynamics of the injury development process. The integration of advanced monitoring techniques plays an important role in the pursuit of high-level sports performance. The utilization of wearable sensors serves that purpose. It allows continuous athlete assessment and provides feedback on the relevant health and performance metrics in real–time. The methods established in the thesis offer solutions for dealing with different quality, time scale and hierarchical data structures collected with high-end wearable sensors, self-reported questionnaires and motion capture systems. Integration of the available data from different sources and implementation of the statistical models that can translate them to the relevant outcome provides actionable insights for performance improvement and injury prevention. Adding these statistical methods is a chance for training and injury-prevention programs to continue to improve.

Propulsion power is an important metric in wheelchair racing. For a flat surface, it can be estimated from the sum of friction power and kinetic power. Usually, to determine friction power, the rolling and air drag coefficient first need to be determined with coast-down tests or other time-consuming methods. The aim of this paper was to investigate whether friction power could be estimated from Inertial Measurement Unit (IMU) data during wheelchair propulsion without the need for previously determining these coefficients. Two approaches were investigated using the kinematic data of a wheelchair athlete measured by a wheel, frame and trunk IMU. Firstly, an approach was used that considers the recovery phase of the propulsion cycle to be a coast-down period. Secondly, a machine-learning approach (Random Forest Regressor) was implemented. Coast-down tests were used to calculate a reference power with which the results from the two approaches could be compared. Results indicate that the machine-learning approach is more promising than the recovery phase analysis. However, whether the current machine-learning model can predict friction power for unseen subjects and surfaces should still be determined with inter-subject validation. Otherwise, it is recommended that a machine-learning model is trained for multiple subjects and a variation in conditions affecting friction force (surface, tyre pressure, wind, slope) to achieve a more robust model.

Insight into in-field mechanical power estimation in wheelchair racing is useful for athletes and coaches. A non-invasive method to estimate mechanical power is by using inertial measurement units (IMUs) to estimate the power lost to resistive forces during wheelchair propulsion. During the recovery phase, no propulsive force acts on the athlete/wheelchair combination and therefore, the deceleration of the athlete/wheelchair combination during this phase is caused by the power lost to resistive forces. The aim of this study was to investigate whether using deceleration in the recovery phase for estimation of resistance using IMUs is applicable in wheelchair racing. To approach the instantaneous velocity of the athlete/wheelchair combination and therefore the deceleration during the recovery phase, the kinematics of the wheelchair and the upper body were measured and used for three different methods. The simplest method is to use the velocity of the wheelchair (v_wc), the second method takes the influence of the trunk movement into account (v_com,2seg) and the last method takes the influence of all upper body segments into account (v_com,tot). The results of this study indicate that using v_wc, v_com,2seg or v_com,tot for estimating the deceleration during the recovery phase is not yet suitable to provide accurate estimation of the total resistance compared to drag test based results. This indicates that application of this method of estimating resistance forces in the recovery phase is not as straightforward as initially anticipated. Despite the potential benefits of this approach, the results suggest that refinement of this method or development of a new method is necessary to obtain an accurate estimation of the total resistance.

A baseball pitch is an explosive and dynamic motion during which one of the highest angular rates achieved by the human body in sports activity was noted. The nature of the baseball pitch puts shoulder structures at risk, and one of the most common injuries among pitchers is a rotator cuff tear, frequently connected with internal impingement. It has been found that the amount of internal impingement increases for the angle of horizontal abduction beyond the coronal plane. To better understand the mechanics in the shoulder that leads to excessive horizontal abduction, it was decided to study muscle behavior (activation and produced torque) during the acceleration and deceleration phases of a baseball pitch. The study focused on muscles that are believed to have the most significant contribution during the aforementioned phases: pectoralis major and rotator cuff muscles (infraspinatus, supraspinatus and teres major). The analysis was performed with the use of the Thoracoscapular Shoulder Model implemented in OpenSim software. About 20 pitches of 5 different baseball pitchers were simulated based on the collected marker motion data. Visible differences in the horizontal abduction/adduction angle were found between participants, with the angle of horizontal abduction between -10 degrees to 18 degrees at the moment of maximal external rotation (MER). Next, the muscle length was analyzed. The normalized muscle length of the rotator cuff muscles at MER was found to be similar to one found in the literature, as well as the changes in muscle length over time. However, discrepancies were found in the pectoralis major behavior. The conclusion was drawn that the thoracic part of the pectoralis major is too long. Moreover, it was found that the pectoralis major does not wrap around the humeral head and shaft for the horizontal abduction angle exceeding daily-live tasks. Next, static optimization was done to find the activation of the muscles in the glenohumeral joint and, consequently, to calculate the contribution of the individual muscles to the total torque in the joint. The contribution of the rotator cuff muscles was in accordance with the information found in the literature.
However, the pectoralis major was not as active as expected. Instead of it, high activity of the deltoideus muscle and coordinate actuators was found. The results indicate that the pectoralis major was not working as expected, and other muscles needed to compensate for its normal function. Several improvements to the model and data collection were proposed. For the model development, it is suggested to ensure wrapping around the humeral head and shaft for the pectoralis major. Moreover, further evaluation of the model scaling is recommended, as problems with scaling the thoracic part of the pectoralis major were found. It is believed that after their implementation, it will be possible to find a potential correlation between the range of horizontal abduction and muscle activity while using the methodology and tools presented in this study.

Activity classification in sports is a powerful tool for athlete monitoring, enhancing performance and injury prevention. In handball, detection and classification of throws during a practice or a (practice) match has not been done. Therefore, the aim of this study is to use machine learning algorithms to detect handball throws and to classify between different throwing types and wind ups in handball based on wrist IMU data during a practice or practice match. A total of 2475 throws from 16 players were used for the detection and classification. Multiple algorithms were tested for the binary (throw versus no throw) event detection. The k-Nearest Neighbours algorithm provided the highest accuracy and F1 score and is therefore the best fit. For classification, all throws were labelled with one of the 17 throw types. Five categories were made to test on what scale the classification is possible. The categories consisted of all 17 throw types, shots versus passes, wind up type, a 7-class category and an intensity-based category. Even though multiple algorithms were tested, for all categories Support Vector Machines gave the highest F1-score and accuracy and was therefore the best fit. The categories based on intensity and wind up type scored higher than the categories with all 17 throw types and with 7 classes. Future research should focus on balancing out and enlarging the dataset, preferably with lab data.

Classification of pitch types outside the laboratory or game environment provide benefits in designing and outlining training routines to reduce injury and improve performance. Current classification approaches are based on vision-based optical data, however these optical data is often not available in training sessions. The aim of the current study is to use machine learning algorithms to classify pitch types, i.e., fastball, change-up and curveball, based on pelvis and trunk IMU data in training sessions. A total of 406 successful pitches thrown by 19 pitchers were used to classify pitch types. Three conditions were tested and evaluated in a binary (fastball versus others) and multi-class (fastball versus change-up versus curveball) classification approach. Condition A included the direct output of the PitchPerfect software, whereas condition B included features normalized by fastball characteristics. Condition C is a combination of both condition A and B. The random forest algorithm demonstrated the best predictions in both the binary and multi-class classification approach based on the highest accuracy and F1 scores, i.e., the harmonic mean between sensitivity and precision. Therefore, the random forest algorithm is the best fit for classifying pitch types based on pelvis and trunk IMU data in training sessions. In addition, the performance of the classification algorithm improved when using a binary classification approach. There were no relevant improvements when using additional features. The random forest algorithm can directly be implemented in the PitchPerfect application. Pitchers can use the pitch type data to track and tune their performances, whereas coaches can use the data to design match and training routines. Future research should focus on larger datasets, i.e., more pitchers and pitches, to pre-classify pitchers with similar pitching characteristics to improve the classification algorithms.

Ulnar collateral ligament (UCL) injuries are common in baseball pitching. The elbow external valgus torque is assumed to be indicative for the applied load on the UCL. This study investigated the maximum external valgus and extension torque and their corresponding load variability during a baseball pitch and the relationship between the magnitude and the within-pitcher load variability of these elbow torques. Furthermore, this study investigated to what extend the elbow torques and the ball speed are related. Eleven Dutch AAA pitchers each threw 25 fastballs. The motion was captured with an optical motion capture system. The ball speed was measured with a radar gun. The data of the upper body, in particular the elbow torques, were analysed using a custom-made 3D inverse dynamics model. The results show that the within-pitcher load variability differs among pitchers. A higher applied elbow torque compared to other pitchers indicates a higher within-pitcher variability. From these results, both a higher valgus torque and a higher within-pitcher load variability are expected to lead to higher injury risk. It is advised not to take one pitch per pitcher into account since it cannot represent all the pitches, especially if only the fastest is selected. Among the pitchers, ball speed is found not to be a good indicator for the elbow torques. Within a pitcher, the ball speed serves better as an indicator. This study emphasises the importance of analysing each pitcher’s results individually instead of comparing them to the whole group.

The main resistive force acting on the wheelchair during manual wheelchair propulsion is the rolling resistance. The magnitude of this rolling resistance is heavily influenced by the actions of the wheelchair user. By optimizing how wheelchair athletes move, this rolling resistance can be minimized, thereby improving their effective power output. However, in scientific literature, there are no dynamic models that analyze the direct effect of the wheelchair user’s actions on the rolling resistance. To fill this research gap, this master thesis was initiated. In this master thesis, a simulation model is created that can calculate the rolling resistance directly from the wheelchair user’s actions. This inverse dynamic model was created by combining a dynamical model that calculates the rolling resistance with a segmented model of the wheelchair user. To test this simulation model data were gathered using coasting tests and manual wheelchair propulsion tests. The first results were promising. The results of the calculated rolling resistance seemed plausible and the effect of the wheelchair user’s actions was clearly visible. This makes this simulation model a useful tool to analyze the effect of the wheelchair user’s actions on the rolling resistance. The most important factor in determining the magnitude of the rolling resistance appeared to be the thorax angle. This is in accordance with scientific literature, as leaning forwards shifts the weight distribution forwards. Multiple studies show that shifting the weight distribution forwards causes a significant increase in the magnitude of the rolling resistance. Further research to confirm the validity of this simulation model is still required.

Aerodynamics are an important factor in competitive cycling and easy and realistic aerodynamic measurement methods are helpful for the riders and coaches. An indoor velodrome, minimizing the environmental effects, could be a suitable location for quick, reliable, and easy measurements on cyclist CdA. However, the effect of banked straights and bends on the CdA determination remains unclear. The goal of this study is to investigate how the track geometry impacts the CdA measurements. Two methods for the calculation of CdA where compared, one that does and one that does not take track characteristics into account. For the measured CdA, no difference between the two methods was found. Additionally, a moderate intraclass correlation was found between the two methods and the drag measured in a wind tunnel (method 1: ICC = 0.76, method 2: ICC = 0.77). A difference in drag of 7.6 Watts and more at a speed of 50 km/h was found to be accurately measured by both methods. In the bends, a significantly lower power output was found, resulting in a different CdA value between the bends and straights. This difference remains after taking the track characteristics into account. It can be concluded that for velodrome aerodynamic drag measurements, the banking and bends do not have to be considered. In this study, no conclusion can be drawn on the found differences in CdA between the bends and the straights. Future research could determine whether these differences are due to incomplete dynamics or a real difference in aerodynamics.

Energy harvesting has become a popular topic for battery substitution in recent years. In particular, mechanical vibrational energy, one of the most common energy sources, has been intensely studied. Piezoelectric materials play a crucial role in vibrational energy harvesting as they can directly convert the mechanical energy to the desired electric energy. In particular these are attractive when they are made into a cantilever form allowing the harvester to harness larger energy. This project aimed at optimizing the performance of single-layered piezoelectric cantilevers, also known as the unimorphs, from the point of view of output power and operation lifetime with the use of a stroke limit stopper. \\
In this study, a cantilever beam consisting of PZT (PZT5A4) bonded to an elastic substrate (Pernifer 45) with a tip mass attached to the free end is used as the harvester. To reduce the cracks generated in the piezoelectric material and subsequently increase the lifetime, a limit stopper is introduced to constrain the deflection of the unimorph.The influence of the stroke on the lifetime and power output is shown by a series of vibration tests under different operation conditions with varied unimorph lengths, configurations, attached tip masses and stroke distances. The result confirms the possibility of the limit stopper to improve output energy of longer unimorphs in certain distance ranges. It also points out that current designs do not allow performance optimization in both output and lifetime simultaneously. One ought to choose one of the two, as the methods which increased the instant power output also resulted in a sharp reduction in lifetime as well as total power capacity.

Baseball pitching is a movement wherein an external valgus moment around the elbow joint regularly causes an ulnar collateral ligament (UCL) injury. A proven relationship between the muscle activation around the elbow joint and its capacity to compensate for the external valgus moment during a baseball pitch allows predicting the likelihood of a UCL injury within pitchers in the future.

The present study establishes the generic muscle activity around the elbow joint during a fastball baseball pitch to investigate the capacity of the muscles to compensate for the external valgus moment actively and thereby prevent a UCL injury.
Six uninjured, experienced recreational adult pitchers, participated in this study (age: 25 ± 2 years; body height: 188 ± 10 cm; body mass: 77 ± 15 kg). 2000 Hz surface ElectroMyoGraphy (sEMG) was used to measure the muscle activity around the elbow joint in 15 fastball pitches for each participant. Before the pitch measurement, participants had to perform maximum voluntary contractions (MVC).

The signals were corrected to an electromechanical delay of 50 ms and normalized to either MVC, or to the maximum of the signal itself. After that, the mean values were calculated for the instance of foot contact, maximum external rotation and ball release separately over the participants. A repeated measures ANOVA was performed to observe whether the mean activity is significantly higher at maximum external rotation compared to the moment of foot contact and ball release to compensate for the peak external valgus moment.

Significant peak activity was found at maximum external rotation for all muscles, compared to the instance of ball release. The parallel activity of the flexor pronator mass and m. pronator teres at maximum external rotation enhances a compensating effect to the external valgus moment by its directly counteracting tension. Furthermore, the results support a co-contraction between the flexor pronator mass:extensor supinator mass and m. triceps lateral head: m. biceps brachii muscle pairs to compensate for the external valgus moment by a compression force to the elbow joint. The generic function of the m. anconeus in the fastball baseball pitch is still debated due to the inconsistent results over the different pitchers in this study.

This study provides evidence that the muscles around the elbow joint can compensate for the external valgus moment during a fastball pitch. These results provide possibilities for using sEMG to assess the muscle activation pattern of individual pitchers as support to predict and prevent UCL injuries in pitchers in the future.

The relay in short track skating is a team effort with the possibility to regain strength during the race by performing an exchange with a team member. Couples can gain time and conserve kinetic energy with a well performed exchange. However, the exchange is a difficult operation. With few studies done to find out what makes a good relay exchange, the exchange is rather based on experience than on knowledge. Former studies investigated the kinetic energy efficiency and the straight time with and without exchanging to find positive and negative influencing factors. To study the influence of skating velocity on relay efficiency, both the Dutch National short track team and RTC North participated in a test setup. Two inertial measurement units, worn in a waist belt during the relay training exercises, provided acceleration data during the relay exchange. The acceleration data from both the pushed and pushing skater were studied to find out what the influence of the skating velocity is on the performance of the exchange. Horizontal accelerations of the pushing and pushed skater were transferred into total horizontal forces with the difference between these total horizontal forces (DF) deemed as an efficiency value for the relay push. A weak positive correlation is found between: DF and the skating velocity of the pushed skater and the deceleration of the pushing skater and the skating velocity of the pushed skater. Camera images revealed the accelerations of the pushed skater to be a combination of the exchange push and the first stride, with no distinction visible between them in the corresponding acceleration data. The influence of the first stride is unknown, making the acceleration data difficult to interpret.

Dutch children achieve an insufficient amount of physical activity. For that reason, Delft University of Technology is looking to develop a wearable that contributes to motivating children to move more frequently and more intensely while collecting data on the movements made by its wearer. In this thesis it was investigated whether it would be possible to identify the activities performed by free-living children and what type of data from which body placement should be obtained in order to do so. Due to restrictions resulting from COVID-19, triaxial accelerometer- and gyroscope measurements of typical child play activities were carried out on various body parts of eight adults. A Long-Short Term Memory algorithm was applied to short sequential sections of summarized accelerometer data. This algorithm gives a prediction of the activity executed by the wearer of a triaxial accelerometer for every 10 seconds in time. The effects of the wear-site (left wrist, right wrist, right hip, left ankle or right ankle), type of accelerometer (low noise or wide range) and epoch length (0.25, 0.33, 0.5 or 1 second) were studied. The shortest epoch length was found to result in the most precise predictions per activity and the highest overall accuracy for classifying the activities. The hip and right wrist placement perform better than the other locations. A wrist placement is favored over the hip because a heart rate sensor can be added to the former. Measuring the heart rate in combination with classifying the activities performed gives insight in both intensity and variety of movements made by children. To further increase the performance of these classifications, predictions made with a score below a certain threshold, 0.775 in this thesis, can be excluded. This will decrease the amount of classifications made but it improves the accuracy as well as causing the precision with which each activity is recognized to rise. Without excluding results with a score below this threshold value, classifications of low noise 0.25 second epoch data from the right wrists have an accuracy of 74.8% and a precision per activity of 54.5% - 82.1%. Removing the more uncertain predictions yields an accuracy of 84.0% and a precision between 57.8% - 91.5%. Clustering specific activities, such as sitting and lying down, increases the precision considerably. The accuracy and precisions when applying sitting and lying down as one cluster in combination with removing the uncertain predictions for the low noise sensor become 85.4% and 70.1% - 92.2%. Before this algorithm can be successfully implemented in combination with the intended wearable, the precision with which each individual activity is identified should be <90%. To transfer the collected accelerometer- and heart rate data to a smartphone, it is recommended to use NFC technology. Through a smartphone application it can then be made available for research purposes and the children can get feedback on the variety and quantity of their physical activity of the past two days. A battery that is appropriately sized for a child's wrist wearable will only be able to power the wearable for a few days. This is an insufficient battery life for the intended use, so the battery has to be charged. NFC technology additionally offers energy harvesting capabilities, making it possible to wirelessly charge the wearable via a smartphone. From this exploratory research it can be concluded that it will be challenging to develop a low-cost wearable that can identify activities and measure how frequently free-living children are physically active. This is the case for both software, the algorithm that predicts which activities were performed, and hardware, where the most pressing challenge is the battery life. However, it is believed that with more extensive research it is possible to create a fully operational wearable.

The purpose of this study was to determine whether a change of an accuracy restriction (AR) leads to a change in the separation time between the peak rotational velocities of the pelvis and the trunk during a pitching motion, while the ball velocity is kept constant. The peak rotational velocities of the pelvis, trunk, the separation time and ball velocity were measured on ten players (age: 20.4±2.4 years, length: 187.6±4.7 cm, bodyweight: 81.3±9.7 kg, pitching experience: 10.7±4.2 years). They performed two tasks twice. First, they had to perform ten fastballs on a large target with a low accuracy restriction. Secondly, they had to perform ten fastballs on a small target with a high accuracy restriction. A week later they performed the same tasks but in reversed order. The associations between the kinematic variables and the accuracy restriction were separately determined using generalized estimating equations. The rotational velocity of the pelvis was significantly associated with the accuracy restriction. The separation time and rotational velocity of the trunk were not significantly associated with the accuracy restriction. It cannot be concluded that the separation time was affected by a change in accuracy restriction.