Knee osteoarthritis (OA) is one of the most prevalent joint diseases worldwide, and mechanical loading plays an important role in its development and progression. Accurate knee joint kinematics are essential for computational models that estimate biomechanical parameters such as joint contact forces. Fluoroscopy enables accurate measurement of joint kinematics and may help overcome some limitations of conventional approaches, including soft tissue artefacts associated with optical motion capture and simplified model assumptions about knee joint motion.
This study aimed to develop and evaluate a modelling framework integrating fluoroscopy-derived knee kinematics into an existing musculoskeletal (MSK) model to assess whether this improves the prediction of tibiofemoral (TF) forces and medial-lateral load distribution, and how these outcomes are affected by different modelling choices.
Simulations were conducted using a generic MSK model and walking and squatting data from two subjects from the CAMS-knee dataset. TF flexion–extension (FE) rotations and anterior–posterior (AP) and superior–inferior (SI) translations were derived from fluoroscopy. FE rotation was prescribed during inverse kinematics (IK), while AP and SI translations were implemented as functions of FE rotation by replacing the model’s original coordinate-coupling functions. Subject-specific mean kinematic relationships were also constructed for each activity. Different kinematic prescription configurations were evaluated. Joint loading was estimated using the rapid muscle redundancy (RMR) solver. Total TF forces, as well as medial and lateral compartment forces, medial force peaks, impulse, and medial load ratio (MLR), were evaluated against in vivo measurements.
Fluoroscopy-derived kinematics showed larger excursions and distinct absolute magnitudes than the original model parametrisations. Their prescription led to changes in predicted TF forces and compartmental load distribution. For walking, the kinematic prescription had a limited influence on TF force predictions overall, although the FE-SI configuration showed the poorest agreement with in vivo data. For squatting, all configurations overestimated TF forces, but the FE-AP and FE-AP-SI configurations improved agreement with in vivo measurements. The FE-AP-SI configuration yielded the smallest total TF impulse differences relative to the in vivo reference in both subjects (1.663 BW$\cdot$s and 0.377 BW$\cdot$s), whereas FE and FE-SI produced the largest deviations. Lateral TF forces were predicted more accurately than total or medial forces. Subject-specific mean prescriptions yielded results nearly identical to trial-specific prescriptions.
The effect of prescribing fluoroscopy-derived knee kinematics in a generic MSK model depended on the activity and prescription configuration. No consistent improvements in walking were observed, whereas the FE-AP and FE-AP-SI configurations improved TF force predictions during squatting. Subject-specific mean prescriptions had a negligible effect on predicted forces, supporting a simplified implementation of the framework. Overall, this approach may improve the biomechanical fidelity of MSK models and advance understanding of knee joint loading.
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Background The trapeziometacarpal (TMC) joint plays an important role in routine tasks. However, little consensus exists on the composition of its ligaments and their contribution to joint stability. 
Aim This thesis aimed to develop a physical model of the TMC joint to investigate the influence of ligaments on joint stability. 
Method The model was constructed using 3D-printed bones and ligaments represented by Dyneema rope, actuated through tendon routing. Its kinematics were compared to subject-specific in-vivo data from 4D CT. Both active and passive actuation trials were performed. 
Results The model successfully reproduced circumduction motion, but its range of motion deviated from in-vivo results. Active and passive actuation produced comparable outcomes for circumduction, but discrepancies in pure motions revealed an oversimplification of the actuation system. 
Conclusion This thesis presents, to the best of the author’s knowledge, the first physical model of the TMC joint. Although simplifications in ligament representation and actuation reduced anatomical accuracy, it lays the foundation for further research and clinical application in thumb biomechanics. ... Read more

Introduction: Knee osteoarthritis (KOA) is a degenerative joint condition that affects both the medial and lateral femoral condyles, and is a leading cause of pain and reduced mobility worldwide. Low-impact exercises such as cycling are increasingly explored, as cycling reduces knee joint contact forces (KJCF) compared to high-impact activities, making it a suitable option for preserving joint health. Understanding the distribution of KJCF across the medial and lateral condyles during cycling is crucial for informing effective exercise interventions.

Objective: This study aims to validate an existing musculoskeletal cycling model using experimental electromyography (EMG) data, and to extend it to estimate the distribution of the tibiofemoral compressive force (TFC) across the medial and lateral condyles.

Methods: Eleven healthy recreational cyclists (mean age: 25.7 ± 1.7 years) participated in lab-based cycling trials. Reflective markers and EMG sensors captured lower limb kinematics and muscle activity at varying cadences (70–90 RPM) and power outputs (80–120 W). The musculoskeletal cycling model of Clancy et al. (2023) in OpenSim was modified to include separate medial and lateral knee compartments. A publicly available dataset was used to scale the modified model and run inverse kinematics (IK), static optimisation (SO), and joint reaction force (JRF) analysis. Predicted muscle activation was evaluated using five matched comparison pairs and cross-correlation analysis. Finally, the modified model was used to estimate the distribution of TFC across the medial and lateral condyles throughout the crank cycle.

Results: The modified model produced near-identical results compared to the original model in both muscle activation (R > 0.99) and total TFC (R > 0.98), verifying the model modifications.
Model-predicted muscle activations showed the highest correlation with EMG data for the vastus medialis (VM) (R = 0.88–0.99) and the vastus lateralis (VL) (R = 0.84–0.99). Lower correlations were observed for the rectus femoris (RF), gastrocnemii (GL, GM), biceps femoris (BF), and semitendinosus (ST). In all subjects, lateral TFC consistently exceeded medial TFC throughout most of the crank cycle.

Conclusion: This thesis successfully estimated compartmental TFC during cycling without affecting the muscle activations or total TFC predictions. Comparison with EMG data showed variable agreement, reflecting modelling limitations and inter-subject variability. The results provide valuable insight into asymmetrical knee joint loading during cycling and illustrate the potential of musculoskeletal modelling to guide more targeted rehabilitation strategies for individuals with KOA.

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Stroke is a major cause of disability worldwide, with two-thirds of survivors experiencing upper limb (UL) impairments that limit their ability to perform daily activities. Effective UL rehabilitation is crucial for maximizing recovery and improving quality of life. To guide therapy selection and track patient progress, UL function assessment tests are commonly used. However, these assessment tests are subjective, lack detail, and are time-consuming. Moreover, the increasing stroke burden and healthcare workforce shortages highlight the need for innovative rehabilitation approaches, including more home-based rehabilitation solutions. Wearable sensor technology, particularly inertial measurement units (IMUs), offers a promising solution for objective, detailed UL assessment, enabling at-home monitoring. In response, the Towards@HomeRehab project, led by the Erasmus Medical Center, was established to develop and evaluate an IMU-based upper limb assessment tool for home use.

This thesis, as part of the Towards@HomeRehab project, had two main objectives: 1) to explore clinicians’ perspectives on an IMU-based assessment tool that uses a standardized drinking task to evaluate upper limb function, and 2) to examine the technical feasibility of extracting kinematic metrics from IMU data collected during the drinking task.

Chapter 2 presents a qualitative study on clinicians’ perspectives regarding the potential IMU-based assessment tool. Three different rehabilitation clinicians were interviewed, all recognizing the tool’s value in objectively and consistently tracking patient progress over time. However, they emphasized the important requirement for the tool to generate a simple, concise overview of clinically relevant movement deviations compared to normal performance. Determining the most relevant output measures remained challenging, as interpreting numerical data requires a shift from their traditional reliance on visual movement assessment.

Chapter 3 presents a pilot study with sixteen healthy subjects examining the technical feasibility of using QSense IMUs to extract kinematic metrics from a standardized drinking task. The results demonstrate that it is technically feasible to derive movement time and joint angle metrics from the IMU data collected during drinking tasks in healthy subjects. The analysis showed excellent relative consistency (ICC ≥ 0.9, SEM < 5% of the mean) and successfully captured different movement patterns, as demonstrated by significant differences between normal and stroke-mimicked drinking task data (p < 0.05). However, a preliminary validation test revealed notable errors in shoulder angle estimations (MAE > 10°).

Chapter 4 summarizes final conclusions, future perspectives, and recommendations. We have demonstrated the potential of an IMU-based upper limb assessment tool for post-stroke rehabilitation. However, the presented data processing methods require refinement to improve metric accuracy and should be extended with additional metrics. Future research should focus on the clinical validation of the extracted metrics, test-retest reliability, and responsiveness of the metrics to functional changes in stroke patients. Additionally, a simple and concise method of presenting the tool’s outcomes should be developed in collaboration with clinicians to ensure alignment with clinical needs, preferences, and workflows.
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Background
Altering the foot progression angle (FPA) is a commonly researched approach to reduce the knee adduction moment (KAM) in patients with medial compartment knee osteoarthritis. However, little is known about kinematic strategies used to achieve specific FPAs, and the effect of different FPAs on the medial tibiofemoral contact force (TFCF).

Objective
This study aimed to investigate how walking with different FPAs affects lower-limb joint angles and medial TFCF, to assess whether a personalized target FPA can reduce medial TFCF, and to investigate the relationship between changes in KAM and changes in medial TFCF.

Methods
Motion capture data were recorded for healthy participants while walking with their natural FPA, as well as walking with four altered FPA conditions (toe-out and toe-in 5° and 10°, relative to the natural FPA). For each condition, the KAM was calculated using ground reaction forces and motion capture data, and joint angles and medial TFCF were obtained by performing musculoskeletal modeling. Peak values during the first and second part of the stance phase, as well as the area under the curve during stance phase (impulse), were extracted for the KAM and medial TFCF.

Results
Fourteen healthy participants (9 women, age: 57.8 ± 12.1 years) were included. Across all joint angles, changes for toe-out conditions occurred in opposite directions from toe-in conditions, with larger differences for 10° than 5° conditions. Hip rotation differed significantly throughout the stance phase, subtalar inversion/eversion showed significant differences during late stance. Changes in medial TFCF varied across FPA conditions without a consistent pattern. The personalized target FPA significantly reduced first peak (-0.22 ± 0.19 body weight (BW), p = 0.002), second peak (-0.23 ± 0.32 BW, p = 0.049), and impulse (-0.15 ± 0.13 BW, p = 0.002) medial TFCF. Changes in KAM were more strongly associated with changes in medial TFCF for the first peak during toe-out gait (R\textsuperscript2 = 0.64 and 0.82 for 5° and 10°, respectively) and for the second peak during toe-in gait (R\textsuperscript2 = 0.44 and 0.76 for 5° and 10°, respectively).

Conclusions
Lower-limb joint angles were consistently altered for different FPA conditions, with the largest changes observed in hip rotation and subtalar inversion/eversion. The change in medial TFCF for FPA alterations was highly individual, with no consistent pattern across participants. A personalized target FPA significantly reduced medial TFCF throughout the stance phase. The relationship between changes in KAM and medial TFCF was dependent on stance phase, consistent with prior research, and also varied across FPA conditions.
Studies including patients with knee osteoarthritis are needed to determine how FPA alterations influence medial compartment loading in the target population.

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Overhead pitching in baseball generates some of the highest angular velocities and joint loads observed in human motion, often leading to overuse injuries of the elbow and shoulder. Understanding the mechanics between joint loading and muscle activity is essential for understanding injury development and reducing injury risk. This study aimed to develop an upper-extremity musculoskeletal model in OpenSim capable of simulating the dynamic shoulder and elbow mechanics of a high-velocity baseball pitch.

Marker-based motion capture data from a collegiate pitcher were processed using inverse kinematics, inverse dynamics, and static optimization to estimate joint torques, muscle activations, and force profiles. An existing thoracoscapular shoulder model was extensively refined, as 28 out of 35 muscle paths were adjusted and 14 elbow and forearm muscles were added to enable analysis of the complete upper extremity musculoskeletal structure between stride foot contact and ball release.

The simulation generated in this study reproduced muscle fiber lengths, joint moments, muscle activations and force production estimates. Peak glenohumeral internal rotation torque of 145.5 Nm was found at maximum external rotation (MER), generated primarily by the subscapularis (≈67% of total torque) and supported by the teres major (≈13%) and pectoralis major (≈11%). Notably, the glenohumeral torque was larger than conventional humerothoracic values, indicating that ignoring scapular effects may understate true shoulder loading and highlighting the need for scapular tracking in pitching research. At the elbow, the flexor–pronator mass (FPM) produced its peak combined force output at MER, aligned with the peak elbow valgus moment (≈85 Nm), supporting the expectation of a major stabilizing role against medial stress on the ulnar collateral ligament (UCL) and suggesting a potential injury-preventive function of a well-conditioned FPM.

These findings demonstrate that an appropriately extended OpenSim model can effectively reproduce the complex shoulder and elbow dynamics observed during baseball pitching. The developed framework establishes a foundation for future studies investigating upper-extremity biomechanics and injury mechanisms in overhead throwing and indicates the importance of incorporating scapular motion and elbow-spanning musculature into musculoskeletal simulations to obtain comprehensive joint loading estimates during pitching.
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Humans can adapt their hand compliance dynamically according to task demands by modulating arm endpoint impedance through changes in arm configuration and muscle co-contraction. This work introduces a multimodal physics-informed machine learning framework for estimating human arm endpoint impedance during multi-degree-of-freedom interaction with a collaborative robot. In this pilot study, data were collected during static and dynamic interaction tasks with the robot. During each trial, muscle activity was recorded via surface electromyography (sEMG), joint kinematics were measured via motion capture (MoCap), interaction forces were recorded with a force sensor, and end-effector positions were obtained directly from the robot. The neural network models were trained to predict the impedance parameters identified from a perturbation-based experiment using these multimodal inputs. Two models were developed and compared: a Temporal Convolutional Network with Multi-Layer Perceptron head (TCN-MLP) as a baseline and a Temporal Convolutional Network Physics-Informed Neural Network (TCNPINN) that integrates physical consistency through a physics based loss term.
Results show that introducing the physics constraint improved the prediction accuracy of the inertia (M), damping (D), and stiffness (K) parameters compared to the purely data-driven model. The inclusion of dynamic movement trials preserved model stability and generalization. While the estimated parameters are not yet accurate enough for direct implementation, the limitations are analyzed and used to identify directions for achieving more consistent and robust results. Nonetheless, the findings indicate that the proposed physics-informed multimodal learning framework has strong potential for estimating human arm endpoint impedance during multi-joint, dynamic physical human-robot interaction. ... Read more

IMU-to-Segment(I2S) calibration is a critical step in using IMU for human motion capture, as it determines the relative orientation and position between the IMU and the body segment it is attached to. Traditional constraint-based method rely on kinematic constraints to perform I2S calibration but fail in scenarios where no relative motion occurs between connected segments. To address this limitation, this thesis extends existing deep learning approaches for I2S calibration to the stiff case and investigates methods to further enhance calibration accuracy.

After completing I2S calibration using deep learning with a single IMU in stiff case, this thesis further explores joint training with dual-IMU model and integrates kinematic constraints into the model. Experiment results demonstrate that the joint training allows the models to leverage inter-IMU motion information, improving the model performance. Furthermore, integrating kinematic constraints with appropriate weights into the loss function of deep learning model improves calibration accuracy by guiding predictions to satisfy physical constraints. However, overly large constraint weights may result in larger calibration error.

This thesis provides insights into how deep learning can be adapted to address the challenges of I2S calibration in stiff joint scenarios. It also combines deep learning with kinematic information through joint training and the integration of kinematic constraints, achieving improved calibration accuracy. Future work will explore the application of this approach to real-world motion data and the integration of diverse kinematic constraints. ... Read more

Background: Stroke contributes significantly to the global rates of disability and mortality. A widely recognised consequence of a stroke is motor impairment, including upper extremity impairments. Rehabilitation has a significant role in the improvement of upper extremity functioning. However, the field of rehabilitation care is currently confronted with several challenges. Telerehabilitation, which includes wearable motion sensors such as Inertial Measurement Units (IMUs), offers a promising solution to improve rehabilitation care in a home-based environment. Therefore, the ArmCoach4Stroke (AC4S) project, initiated by the Erasmus Medical Centre and Rijndam Rehabilitation Institute, was established to develop and evaluate a wearable system for upper extremity rehabilitation at home for post-stroke patients.

Aim: As part of the AC4S project, the aim of this thesis was to investigate the test-retest reliability and criterion validity of an IMU-sensor based method, compared to an optoelectronic system, for measuring kinematic metrics of the upper extremity in post-stroke patients.

Methods: The data from two protocols were subjected to analysis: a reliability and validity protocol. During both protocols, participants were equipped with IMUs and executed predefined calibration movements, as well as reaching and lifting exercises. For the validity protocol, patients also wore markers of the gold standard marker-based Vicon system. The measurements of the validity protocol were performed in a laboratory setting, whereas the reliability protocol simulated measurements in an ecological environment. The indices of calibration movements and exercises were selected manually. The orientations of the IMU sensor were estimated and joint angle metrics were calculated for the reaching and reach-and-return phase of movements. A statistical analysis of the metrics was performed to investigate test-retest reliability and criterion validity separately. This involved the use of the intraclass correlation coefficients (ICC) and Spearman correlation coefficients, respectively.

Results: Eventually, 18 participants were analysed for the reliability protocol and 7 for the validity protocol. The reliability protocol yielded ICCs indicating both good reliability (values >0.75) and poor reliability (values <0.5) for some metrics. Significant test-retest differences were identified for certain metrics. The Spearman correlation coefficients for the validity protocol indicated strong correlations (values >0.7). However, the p-values were above the level of significance. The results further demonstrated considerable differences in absolute values between IMU and Vicon joint angles across various metrics.

Conclusion: A large variability and poor-moderate reliability were observed for multiple metrics, indicating underlying issues with the measurements or metric calculations. Substantial differences in absolute joint angle values were evident, not only between Vicon and IMU, but also between test-retest sessions or between patients. This indicates suboptimal orientation estimation, angle definitions or other fundamental problems that need further investigation.
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Accurate kinematic analysis in speed skating is crucial to understand and improve skaters’ unique technique during training. Few research methods have captured joint kinematics in the past, using Inertial Measurement Units (IMUs) or manual annotation of filmed data or marker-based motion capture methods. The large motion capture volume and ice-rink environment hinders these methods to be actively adopted on rink. Thus, with growing accuracy in kinematic estimation algorithms, demand for a biomechanically accurate dataset is high. In this research, we aim to generate a virtual video dataset called ODAH-SpeedSkater, from experimental motion capture data. First, we examine the impact and accuracy of markers during motion capture. After selection of accurate inverse kinematics data, we use the SMPL-X human body model to achieve individual skater body shape and pose throughout the motion, rigging it to a skater-specific scaled OpenSim skeletal model. We render the finalized mesh sequences in an ice rink scene with skater outfits through realistic camera set-ups and configurations. Thus, we successfully create a dataset of 1,326 biomechanically annotated virtual videos of speed skating. Finally, we test our dataset on a pre-trained 3D kinematic estimation algorithm to evaluate its performance on speed skating data. In spite of limited testing, we conclude that training a network exclusively on our dataset may improve its performance, with the ultimate goal of actively implementing such networks in the rink. ... Read more

In biomechanics, human movement studies are carried out to assess the subject’s kinematic and kinetic variables for a healthy gait. Currently, marker-based systems are the standardized method to extract the kinematic variables of subjects. The marker-based systems pose some serious challenges like cost and portability, and the calibration and synchronization of multiple cameras and sensors are among the other practical challenges. The AI technique often referred as markerless pose estimation methods can overcome these challenges and aid biomechanists and clinicians. Thus, there is a need to develop new deep-learning models that can regress the musculoskeletal model directly from images and videos. However, the deep-learning models are dependent on the quality and quantity of training data. In the current scenario, training data for markerless pose estimation are dependent on the redundant marker-based systems and the challenges persist. To aid this, it is necessary to create a statistical human model or a skinned human animated motion from a biomechanical model to build more training data. From the skinned virtual data consisting of realistic movements, deep-learning models can be trained. Therefore, the aim of the research was to build a pipeline to develop a human-animated model from a musculoskeletal model i.e., the OpenSim model. Two different motions such as walking and running are illustrated as qualitative results. The gait pattern for walking and running motions are realistic from both the frontal and sagittal planes. Furthermore, the deep learning model (D3KE) built by Marian et. al was also evaluated on the animated human motions eg. walking motion from the above pipeline to validate the model. The performance of D3KE is evaluated from different planes of camera views and also a comparison between the upper and lower extremities. The evaluation and comparison are based on two metrics MAEangles (Mean Absolute Error of angles, in radians) and MPBLPE (Mean Per Bony Landmark Position Error, in cm). The MAEangles and MPBLPE are better when observed from the frontal plane rather than from the sagittal plane as the plane of view. Also, the joint angles in the upper extremity show better results compared to the lower extremity. Although, the predictions of the joint angles are way off from the ground truth. This opens the way to perform a feasibility study to optimize joint angles by a pixel loss refinement technique. The findings and remarks on the pixel-loss refinement is tabulated as results. ... Read more

Patients with neuromuscular diseases that are unable to speak, but whose cognitive ability has been maintained, can be benefited from Brain Computer Interfaces (BCIs). The decoding of inner (covert) speech from EEGs consists of one of the state of the art methods that aim to tackle this issue. High variability between subjects, as well as low signal to noise ratio (SNR) undermine the methods used, and introduce the need for computer assisted solutions. Thus, machine learning models as well as large amounts of recorded data are required to design effective algorithms and produce substantial results. In this study, covert vowel classification was performed in a systematic way, by making use of two openly shared databases from literature; the Coretto database, that contains EEG recordings of native Spanish speakers, and the DAIS dataset, which includes EEG recordings of native Dutch speakers. Six classifiers were initially selected to perform 5-class classification: a Random Forest (RF), a k Nearest Neighbours (kNN), a Gaussian Naive Bayers (GNB), a Deep Convolutional Neural Network (DCNN), a Shallow Convolutional Neural Network (SCNN) and a Long Short Term Memory Recurrent Neural Network (LSTM). The DCNN outperformed the other methods, with average intra-subject accuracies of 35% for Coretto and 39% for DAIS (chance level 20%). Afterwards, an Overt versus Covert trials experiment was implemented, to test the limits of overt speech decoding from EEGs. The overt result was slightly higher than covert, with an intra-subject average value of 37.8% for Coretto and 40.5% for DAIS (chance level 20%). Finally, binary classification was performed to identify those pairs of vowels that can be classified more efficiently. Vowels /a/ and /u/ seemed to perform better in average in both datasets (average of 64.8% for Coretto and 64.4% for DAIS with a chance accuracy of 50%). Future work should focus on identifying the useful parts of the EEG recordings, increasing the SNR and the resolution of the electrodes, and defining the most appropriate dictionaries of words/vowels for a BCI. Also, more studies should follow systematic ways of comparisons between datasets, to obtain less ambiguous insights and lead this field to improvements. ... Read more

People need to have a comfortable experience in vehicles nowadays. However, they are continuously exposed to vibrations from the vehicle. Madymo active human models (AHM) can be used for comfort analysis and to learn how vibrations influence the human body in several aspects. However, existing AHM are very time-consuming due to their complexity, and the correspondence with human comfort data is only reasonable. In order to more effectively analyze motion comfort, a computationally efficient simplified human model (SHM) is developed and validated. The human body model has 36 degrees of freedom (DoF) considering the following segments: pelvis, two thighs, two lower legs, two feet, lower torso, upper torso and head. The model is validated in fore-aft, lateral and vertical vibrations. The model's postural stabilization parameters are tuned manually, by gradient search and grid search in sequence. Manually tuning gives a group of initial values of parameters for further optimization. According to the results, failure of the gradient search illustrates that this optimization problem is non-smooth. At the same time, grid search gives a relatively better result but also shows that the current cost function does not perfectly represent a good fit and needs improvement. A comparison between AHM and the SHM shows that the SHM has a similar or even better fit for most signals while being a largest factor 116 faster. A comparison of parameters between SHM and multibody human models in the literature shows that the structure of models affects the values of the same parameters. A comparison of different time steps illustrates that shorter time step does not necessarily give higher accuracy for this SHM. Suggestions such as adding muscles and changing body shapes are given for further improvement. ... Read more

Slipped capital femoral epiphysis (SCFE) is a condition in adolescents affecting the proximal femur. Increased mechanical forces result in fracture of the growth plate. Factors increasing the mechanical forces include for example obesity and endocrine disorders. As a result of the fracture a shift of the bone shaft towards the femur head occurs, causing mobility issues and pain. A treatment option for these patients is an intertrochanteric osteotomy with plate fixation. Restoring range of motion and pain reduction are the main aims of the surgery.

This intertrochanteric osteotomy alters the femur anatomy, resulting in a mismatch between bone and conventional plate fixation. These plates do not fit to the reconstructed bone, resulting in high chances of plate failure. The best option for these patients is to use patient-specific designed bone plates for femur fixation. Optimal mechanical properties of the patient-specific plate are important in limiting plate failure. These properties are dependent on design parameters, which can be adapted dependent on patient criteria and anatomy. Investigations towards patient-specific bone plates and the optimal design parameters for efficient and stable fracture fixation is lacking.

The focus of this study is on a specific SCFE patient case treated with corrective osteotomy using a patient-specific Ti-6Al-4V plate and screws for bone fixation and stability. A valid numerical model (i.e., finite element analysis (FEA)) is created, with physiological loading corresponding to two leg stance and walking, for an initial designed (ID) patient-specific bone plate to analyse plate performance. Screw configuration analysis and topology optimization of the ID plate were performed to design an alternative topology optimized (TO) plate. Biomechanical experiments with digital image correlation (DIC) techniques were performed for mechanical strength analysis and validation of the finite element model (FEM).

Both ID and TO plate were able to withstand two leg stance loading conditions according to both FEA and experimental testing. For walking loading conditions the ID plate showed instability, which could result in higher probability of failure in the plating construct. Quasi-static compression experiments showed failure in the most lateral proximal screw in all tests. The failure in the screws was caused by high bending moments. Optimization of the ID plate resulted in an alternative plate design in SCFE treatment. The TO plate consisted of six screws, decreasing plate length by 18.1%. The TO plate had a variable thickness, where thickness was increased around the lateral side of the plate compared to the ID plate.

The steps followed in this study to design a patient-specific implant and evaluation plate mechanical behaviour with FEM and additional experimental testing have shown to be adequate in investigating plate performance and can be used in the evaluation of newly designed patient-specific bone plates in SCFE patients.
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Duchenne Muscular Dystrophy (DMD) is a neuromuscular disorder characterized by progressive muscle weakness as well as progressive muscle fat replacement. Absence of functional dystrophin in muscles is believed to cause membrane instability, making the sarcolemma susceptible to damage when subjected to mechanical stress. However, the process leading from dystrophin-absence to muscle degeneration, and ultimately fat replacement, is still not well understood. Therefore, several hypotheses currently co-exist. Interestingly, muscles of DMD patients are not equally affected, and a more or less consistent successive pattern of muscle fat replacement exists in the lower extremities. The aim of this study was to investigate a possible mechanical role in explaining differential muscle fat replacement in the legs of DMD patients. Based on a published preliminary study, it was hypothesized that eccentric contraction, during which a muscle generates force while lengthening, is associated to muscle fat replacement during gait.

3D gait analysis data of a small cross-sectional sample of typically developing children was used to predict muscle contributions during gait in OpenSim. In one analysis, negative work, the time-integral of force multiplied by lengthening velocity, was normalized to account for differences in physiological cross-sectional area (PCSA) and fiber length, and was subsequently tested for a relation with muscle fat replacement. Furthermore, since muscle architecture parameters PCSA and fiber length are associated to muscle fat replacement, separate effects of total negative work and muscle volume (PCSA*fiber length) on muscle fat replacement were also analyzed.

Normalized negative work was significantly associated with muscle fat replacement, where upper leg muscles generally showed higher fat fractions than lower leg muscles. Furthermore, both the effect of total negative work as well as muscle volume on muscle fat replacement were significant. The effect of total negative work on muscle fat replacement was stronger if muscle volume was smaller. In contrast, normalized and total positive work (i.e., concentric contraction) was not significantly associated with muscle fat replacement.

Eccentric contraction during gait helps explain the differential progression of muscle fat replacement seen among leg muscles of DMD patients. The results of this study generate more insight regarding a mechanical role in the pathophysiology of DMD, and may potentially help in generating or improving therapies that focus on prolonged muscle function preservation.
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To better understand and predict osteoarthritis, researchers are developing so-called coupled modelling workflows. Coupled workflows convert data from gait analysis studies to subject-specific tissue mechanical response estimations through the use of musculoskeletal and finite element models. The tissue mechanical response inside the joint is thought to play an integral role in the onset and progression of osteoarthritis. To study this, the design of coupled workflow was proposed. This design contained subject-specific gait data which was processed by a musculoskeletal model with a single degree of freedom knee joint. Musculoskeletal output was transferred through an adjusted generic finite element model of the knee to calculate maximum principal stress and shear strain values in the tibial cartilage of the knee. Proper marker placement is crucial for making an accurate assessment of the patient’s function in gait analysis studies. It has been claimed that marker misplacement is the main cause of measurement variability in gait analysis studies. It however remains unclear how potential marker misplacement propagates to the coupled modelling workflow results. To investigate this, in addition to the design of a coupled workflow, a sensitivity analysis was performed. With this sensitivity analysis we tried to answer the following question: How does marker placement of knee joint markers in gait analysis influence the tissue mechanical response calculated by a coupled workflow? For the sensitivity analysis, knee joint marker placements were virtually perturbed along anterior-posterior, proximal-distal and medial-lateral direction, to mimic marker misplacement. Corresponding knee biomechanics were estimated from the perturbed input data in the coupled workflow. Peak maximum principal stress values varied by up to 0.60MPa and peak shear strain varied by up to 0.08% as a result of perturbed knee marker placement. For cumulative stress levels, broader relative ranges were found. Moreover, the results showed that marker placement along the anterior-posterior direction had the greatest influence on corresponding tissue mechanical response estimations. In future studies a standard error of measurement margin is proposed in the assessment of coupled modelling worfklow results. In conclusion, the proposed workfow was relatively easy to build and provided similar tissue mechanical response result to those as reported by more complex models which were more computationally intensive. This implies that in the future, coupled modelling processes may very well be incorporated into the clinical decision-making process for musculoskeletal disorders like osteoarthritis. ... Read more

Total Hip Arthroplasty (THA) is a procedure where a defective hip joint with pain symptoms or functional impairment is replaced by a hip prosthesis. During this procedure, the optimal hip prosthesis should be chosen and placed in the correct orientation to achieve a stable hip joint. Failure to do so can lead to dislocation, mechanical failure or infection of the hip joint. To improve the outcome of the THA, range of motion (ROM) of the hip joint can be evaluated during the surgery after placement of the hip prosthesis. Current methods of measuring hip ROM have some limitations. For example, there is a low intra- and inter-test reliability between identification of the bony landmarks and goniometric alignment. The goal of this project was to develop an instrument that can measure the ROM during THA without the limitations of current instruments. An instrument was developed that determines its orientation via an inertial measurement unit (IMU). The IMU is connected on the upper leg via a strap and aligned with the bony landmarks via laser beams. A test has been performed to validate the usage of the instrument. The ROM of the hip for flexion and abduction motion was measured with the instrument and compared to a reference measurement made with two video cameras. A significant difference was found: the instrument did not stay aligned with the hip joint. For further development of the instrument, the drift should be minimised and multiple straps should be tested to allow a better fixation. Additionally, the added-value of the laser requires further research. ... Read more

The track bound sliding sport of Skeleton was permanently added to Winter Olympics programme in 2002. This has led to increased interest in the sport. Engineering has already proved to be a vital contributor to improved performance in the related sport of Bobsleighing. We hope that engineering can do the same for Skeleton. This report describes an attempt at developing a platform to be used as a real-time training simulator for the sport of Skeleton. For a multitude of reasons athletes are, on average limited to a total of two hours of practice and competitive on-track time in any given year. When compared with time spent practising and in competition in most sports, this is extremely low. It is hypothesized that a simulator can augment track time by providing a realistic environment to practise in, even when access to a track is not available. This work is guided by simulators that have been developed for Bobsleighing. The main components are the models to describe the dynamics of the sport, an input method and visualization of the simulation. The main considerations for the dynamic model are of the track surface, the sled and contact between sled and track surface. These models lead to a system of equations which when solved provide accelerations and contact forces. The accelerations are integrated over a fixed time interval to determine changes in velocities, position and orientation. The position and orientation obtained after the integration is passed on to a game engine which provides the user with real-time visual output of the position and orientation along a digitally recreated track surface. A video game controller was chosen to serve as the input device. It has two joysticks, which can be mapped so as to mimic the forces applied by an athlete. A number of descents were performed using this platform both at real-time speed and at a slower speed to give the user, unfamiliar with the sport, a better chance to steer the sled. We were able to consistently reach the exit of curve 2 in real-time speed and curve 4 at the slower play speed before failure of the simulation. In most cases the algorithm used here proves to take lesser time for computation than the chosen integration time step, which is a great sign for future development as we did not make any attempts to optimise its omputation
time. We made an attempt at validation using time elapsed to traverse a certain distance and the sum of magnitude of Lagrangian multipliers. We had poor results with the time elapsed comparison, with simulated runs being 15% slower than competitive descents. While the sum of Lagrangian multipliers showed good relation to expected behaviour. This first attempt was reasonably successful, and we believe that the lessons learnt from this work has brought us one step closer to realizing a training simulator that can be useful to Skeleton athletes. ... Read more