A musculoskeletal model of the shoulder region, which uses kinematic data as an input an estimates muscle forces and joint loads as an output, can give valuable information on the pitching motion. This helps us to get more insight into the pitching motion and its biomechanical interactions and may also help in reducing the injury risk and increasing pitching velocity. Currently, there are three problems that impede proper simulations: the lack of proper kinematic recordings, the fact that maximum force of the model could be too limited and the extreme character of the motion. An experimental study was performed to create a dataset including upper limb kinematics and PCSA scaling factors to scale maximum force in the DSEM. An acromion cluster was used to track the scapula. PCSA scaling factors ranged from 1.11 to 2.02. Following the experimental study, a case study was performed simulating this dataset in the DSEM. During simulation, the main problem arose in the optimization of the clavicle and scapula angles relative to the thorax, because the optimized angles contained large jumps, which are not realistic. This impeded proper simulation, because the jumps in muscle length caused both unsolved frames in the kinematics as well as in the dynamic model. Using a soft constraint instead of a hard constraint reduced these jumps and allowed for a complete solution in the kinematic model and an increase in the number of frames solved by the dynamic model. PCSA scaling also increased the number of frames solved by the dynamic model, however still unsolved frames were present, even after extreme scaling. Because of a change in range of motion as reported for pitchers, optimum muscle length might be different. This has a large impact in the model. If this would be the case, scaling optimum muscle length is recommended. In addition, segment scaling used in combination with using the soft constraint is recommended to improve the match between input angles and optimized angles, while still being compatible to the model. To study the motion, the kinematic model of the DSEM was used to estimate muscle length and velocity for all muscles during the pitching motion. Comparing these values to the force-velocity and the active force-length relationship showed whether muscles were limited by one or both of these relationships to produce force. This was the case for the teres minor, triceps (all three heads), infraspinatus, anconeus and serratus anterior. The triceps showed a ‘stretch effect’, meaning shortening in the acceleration phase preceded by lengthening in the cocking phase. This means that there is a possibility for elastic energy to be stored for this muscle.

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.

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.

Inertial Measurement Unit (IMU)-based motion capture has gained interest over the years due to its potential to measure human movement in the clinic and on the sports field at low cost. Still, IMU-based motion reconstruction remains a challenging task as these IMU measurements are corrupted by noise and bias. There have been many filtering and sensor fusion algorithms developed to address this problem, but limited attention has been paid to including the system dynamics of the subject to estimate kinematics and kinetics from multiple IMUs. I propose a novel motion reconstruction algorithm for capturing 3D motions in a markerless and unconstrained environment using gyroscope and accelerometer measurements. The algorithm is based upon an Iterated Extended Kalman Filter for state estimation and the 3D dynamical model of the subject created in OpenSim (IEKF-OS). The IEKF-OS algorithm consists of two stages. The first stage incorporates the dynamics of the subject to predict the motion. The second stage tracks measurements from multiple IMUs to improve model estimates of joint angles and speeds. The goal of this thesis is threefold. Firstly, the IEKF-OS is derived and verified using simulations performed on a six-link robot manipulator including sensor placement errors. Secondly, experiments are performed to validate the algorithm’s estimations against the robot’s ground truth joint encoder values. Thirdly, the algorithm is compared against an inverse kinematics method to track IMU estimated orientations (OpenSense) provided by OpenSim. The IEKF-OS algorithm showed lower motion tracking errors compared to OpenSense for various motions performed by the robot manipulator. The joint angle estimations as computed by both methods are compared against the gold-standard ground truth robot encoder values. OpenSense joint angle values were in the range of 0.6-6.4 [deg] RMSE, whereas the IEKF-OS algorithm estimated joint angles and speeds in the range of 0.4-2.8 [deg] and 0.3-2 [deg/s] RMSE, respectively. These results highlight accurate 3D motion reconstruction on a six-link robot manipulator. Contrary to OpenSense, the IEKF-OS is able to accurately reconstruct motions regardless of magnetic disturbances because it does not rely on magnetometer data.

One interesting part of the application of human activity recognition is sports motion recognition and classification. In recent years, many commercial wearable devices have been used for recording and supervising motion data information during sports. However, their claimed high-accuracy results but motion recognition and classification method have not been proven. This thesis project presents work special related to tennis stroke detection and classification. An automated and comprehensive tennis stroke recognition and classification method based on the inertial measuring unit sensor (accelerometer and gyroscope) and machine learning algorithm (Support vector machines) was proposed in this study. Seven tennis players with a different level of tennis skills were tested and recorded using a self-made IMU sensor system with four sensors (forearm, upper arm, trunk, and pelvis). Video footage from Playsight was manually notated as the golden standard for stroke type identification. SVMs was constructed to train the classification model to classify true shots to eight types of tennis strokes from the IMU signals. Across leave-one-out seven-fold cross-validation, the SVMs classification models were trained with data from a single IMU sensor on the forearm and upper arm with the prediction accuracies of 0.69 and 0.70 respectively. And further, both SVMs models were trained by enlarged training data, resulting in improved prediction accuracies of 0.75 and 0.77. Noticeably, the best prediction accuracy was achieved by training the SVMs classification model with fused data from the previous two sensors and with the enlarged training data. The final prediction result was 0.79. Even though there exist deficiencies such as skill level different of subjects, insufficient training datasets which may lead the results of validation and prediction less credible, the IMU sensor and SVMs machine learning algorithm still played well in the tennis stroke classification task. And we expect to have better accuracy results by feeding enough training data and using data-fusion combination of different IMU sensors to the upper extremity to SVMs classification model in future work.

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 study of human motion consists of the analysis of kinematics, dealing with joint angles, velocities and accelerations, and kinetics, which deals with joint torques and interaction forces. Traditionally, kinematics and kinetics are estimated from optical marker data and sometimes measured interaction forces. Inertial measurement units (IMUs) were introduced as a low cost alternative that makes measurements outside the laboratory possible. Measurements are often used in a loosely coupled combination of marker-based inverse kinematics (IK) or orientation-based inverse kinematics (OBIK) and inverse dynamics (ID). The orientations for OBIK are first estimated from IMU data. Recently, tightly coupled estimation methods based on nonlinear state space models have been introduced for both sensor modalities to improve accuracy. Sensor fusion of marker and IMU data is a relatively unexplored area of research. Markers provide a measurement on the position level whereas IMUs measure on the velocity and acceleration levels. Fusing the two sensor modalities is hypothesized to result in the most accurate estimation of kinematics and kinetics. The objective of this thesis is to investigate the effects of the sensor modality and the estimation method on the accuracy of estimated kinematics and kinetics. An iterated extended Kalman filter (IEKF) was developed which estimates joint angles, velocities and torques using any combination of marker data, IMU data and measured interaction forces. To validate the system with respect to ground truth, two experiments (fast motion and interaction force) were carried out on a robot equipped with all three sensor types. For comparison, IK, OBIK and a marker/IMU-based kinematic extended Kalman filter (EKF) were used together with ID to estimate kinematics and kinetics as well. The IEKFs estimated joint angles with a root mean square error (RMSE) between 0.33◦ (markers and interaction forces only) and 1.58◦ (IMUs only, fast motion). The marker-based IEKF and the IMU-based IEKF outperformed IK and OBIK respectively in both experiments. The IMU-based IEKF improved joint velocity RMSE from 4.13 deg/s to 1.10 deg/s in the fast motion experiment. Sharp peaks in the joint torque signal were only estimated accurately using IMU data directly. The marker/IMU IEKF was the only method that estimated joint angles, velocities and torques with RMSEs below 1◦ , 1.5 deg/s and 1.5 Nm respectively in both experiments. It is concluded that using IMU data in a tightly coupled system improves joint velocity and torque estimation. Assuming accurate sensor registration and no soft tissue artifacts, a tightly coupled IMU-only method can be sufficiently accurate in practice.

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.

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.@en