Abstract - Background Most widely used musculoskeletal models are predominantly based on male anatomy. This limits accurate biomechanical analysis in women, despite notable sex differences in occurrence of musculoskeletal pathologies. Aim This study aimed to develop a female musculoskeletal model of the lower extremity (YONI). The YONI is compared to the generic (male-based) model in simulation with a female participant. Methods The YONI model was developed in OpenSim Creator based on MR images. Comparisons were done between the YONI model and a scaled RAJAG model and a personalized model of using motion capture data in simulations. Results For comparison between YONI and RAJAG with another female subject, mean RMS error for the personalized model was 0.0096 m (SD = 0.0013), for the scaled RAJAG model 0.0247 m (SD = 0.0268) and for the scaled YONI model 0.0097 m (SD = 0.0010). Although SPM paired t-test showed significant differences for both YONI and RAJAG compared to the personalized model for all joint angles, the YONI model showed lower t-values compared to RAJAG. Joint moments reveal larger differences between YONI and scaled RAJAG models in the hip angles during the swing phase. Reserve moments were low in hip flexion and hip adduction, but higher in knee flexion and ankle flexion. Conclusion While observed kinematic and dynamic differences require cautious interpretation due to model limitations and data constraints, this work represents a crucial step toward the development of a female musculoskeletal model, essential for advancing biomechanical research and clinical applications for women.

Despite the recognized impact of sex on biomechanics, research remains biased toward male anatomy, raising concerns about the validity of musculoskeletal (MSK) model predictions for females. This study investigated whether sex-specific bone geometry variations predict differences in the proportional volumes of the Gluteus Maximus (GMAX) and Rectus Femoris (RFEM). Using an MRI-based nnU-Net segmentation model trained in this thesis, bone metrics were extracted, and muscle volumes were normalized to derive proportional volumes for 16 young adults (9F/7M). The segmentation model demonstrated high accuracy (DSC: 0.926 for bones, 0.954 for muscles), revealing significant sexual dimorphism in bone geometry. Males exhibited greater femoral offsets and knee widths, while females had larger posterior pelvic widths and depths. %RFEM was significantly higher in males (p = 0.01), but %GMAX showed no sex-related differences. Regression analysis identified femoral offset and femur length as partial predictors of %RFEM (R^2 = 0.478), with pelvis-femur length weakly predicting %GMAX (R^2 = 0.151). However, the low predictive power suggests limitations in using bone metrics to estimate muscle volume proportions. These findings indicate that femoral dimorphism may partially explain sex-related %RFEM differences, but its role in %GMAX remains unclear. Future research integrating additional biomechanical factors could enhance sex-specific MSK modeling accuracy.

The optimal technique for individual speed skaters remains poorly understood, due to the complex interplay of technique variables (like stroke frequency, skate trajectory and push-off mechanics). Optimization with a biomechanical model can help to identify the most efficient techniques for individual skaters. This research aimed to use a validated model of a speed skater (van der Kruk et al. 2017) within an optimization framework to investigate how the optimal speed skating techniques on the straightaways are influenced by individual characteristics and environmental conditions.

Finding the optimal technique that either minimizes effort at a target velocity or maximizes velocity, was formulated as an optimal control problem and solved using direct collocation. Across different optimizations, stroke frequency, mass, leg length, air and ice friction and limits on average and maximal power were incrementally varied.

Variations in velocity and stroke frequency most clearly influenced the optimal technique. Conditions requiring less energy (optimizations for low velocity, low ice or air friction), optimized towards energy-efficient strokes with longer gliding phases and minimal lateral forces. Conditions with higher speeds and frequencies converged to longer, forceful push-offs. These push-offs maximized leg extension by descending into a deep crouched position to emphasize a sideways push-off. Generally, optimized techniques adapted a small steer angle during the gliding phase to prioritize forward gain, and larger steering angles during the push-off to direct push-off forces forward. Optimizations for higher frequencies adopted more narrow strokes, and reached higher maximized speeds. Regarding personal characteristics, increasing the model's average and maximal power limits most significantly increased maximal velocity.

Age-related decline in physical and neural capacity can make the sit-to-stand (STS) motion increasingly difficult for older adults, significantly impacting their quality of life. Despite these declines, humans adopt compensatory movement strategies to mitigate the effects of reduced capacity, maintaining functional mobility. Predictive simulations offer a tool for studying the relationship between capacity decline and compensation strategies. However, previous predictive studies have omitted the modeling and control of arm movements, thus neglecting key arm compensation strategies relevant to the STS motion. Therefore, this study aimed to develop a neuromuscular controller for a three-dimensional musculoskeletal model that includes the arms, enabling the simulation of STS arm compensation strategies. STS arm strategies were successfully simulated and displayed comparable joint kinematics with experimental data. However, the simulations revealed elevated leg muscle activations and an overestimated vertical ground reaction force. Additional simulations with changed conditions demonstrated the effective use of the armrest and thigh push-off strategies to adapt to lower seat heights and reduce peak knee joint load. Overall, the neuromuscular controller in this study provides a new basis for future STS research into uncovering the link between capacity decline and compensation strategies, potentially leading to improved methods for assessing and addressing age-related declines in crucial movements.

Transfemoral prosthesis users typically perform the sit-to-stand motion unilaterally, placing minimal load on the prosthetic side. This increases the risk of injury and accelerates the degeneration of the intact limb. To address this, understanding compensation strategies is essential. Predictive neuromusculoskeletal modeling offers a method to investigate this. The aim of this study was to develop and validate a neuromusculoskeletal model with reflex-based muscle control to simulate the sit-tostand motion in non-amputees and transfemoral amputees with a passive prosthesis. We developed a three-dimensional sit-tostand musculoskeletal model of a non-amputee (20 degrees of freedom, 24 muscles) and a transfemoral amputee (20 degrees of freedom, 19 muscles), both incorporating a two-phase stand-up controller based on an existing two-dimensional reflex controller. We compared the simulation framework to measured kinematics, muscle activations, ground reaction forces, and existing literature on degrees of asymmetry and muscle forces. The developed framework was used to optimize prosthetic knee and ankle stiffness and damping for the sit-to-stand motion with a passive prosthesis. The simulated kinematics of the non-amputee matched measured kinematics. The prosthesis model indicated compensation strategies involving increased thoracic and lumbar extension, lumbar bending towards the non-amputated side,
increased pelvic tilt combined with decreased hip flexion, and heightened muscle activation and force. Optimization results suggested a knee stiffness of 0.1432 [Nm/deg] and damping of 0.0246 [Nm·s/deg], while the ankle required a stiffness of 0.1968 [Nm/deg] and damping of 0.1350 [Nm·s/deg]. These values are recommended for testing in future experiments.

The aim of this Master’s thesis is to determine how user engagement and motivation can be improved when using the Athlete Management System (AMS) as a monitoring system. This system monitors athletes subjectively and objectively for short and long-term progress. Increased use of this monitoring system will result in a more complete and valuable data set for coaches to identify the risks of under- or overtraining for KNSB talented athletes in both long and shorttrack disciplines.

Several interviews were conducted with athletes, experts from the KNSB Talent Teams (KTTs), and other experts in the field of AMS, sports psychology,
and sports innovation centers. These interviews were used to explore how the target group reflected on their sports’ progression and how the feedback process from the KTT staff played a role in this, supported by a literature review of related topics in the context of the project aim.

After interviews with athletes, coaches, and embedded scientists, potential design directions were identified and one was selected by evaluating the directions for feasibility and impact concerning the project aim. The chosen design opportunity is to improve communication about AMS between athletes and their coaches and to provide more guidance in an athlete’s reflective process when they need to measure their recovery-stress state of ‘Mental Readiness’ in AMS, which can be described as the athlete’s ability to concentrate on the execution of a training session. Measuring the recovery-stress state of an athlete can help identify the risk of under- or overtraining. Athletes and coaches experience difficulties interpreting and assessing ‘Mental Readiness’.

Brainstorming and concept validation sessions are conducted to develop a final design: a workshop session consisting of a presentation with three assignments to allow athletes and their coach to share their interpretations of the ‘Mental Readiness’ scale and to give first steps of guidance on how to reflect as an athlete on this scale. The final design is an addition to the kick-off meeting at the beginning of the speed skating season. It also proposes a roadmap for the long-term implementation of the final design in the context of the target group, including additional suggestions for other workshops and presentations to improve communication and behavior around AMS based on the insights from the interviews.

Further research should investigate how the final design leads to behavioral changes in user engagement and motivation in long-term implementation.
In addition, other aspects of the recovery-stress state, such as ‘emotional state’ and ‘motivation’, could be explored to broaden the communication and enhance the self-reflection of the athlete. In future research, it is important to expand guidance for athletes in their reflective capacity and for KTT staff in the correct interpretation and next steps when receiving ‘Mental Readiness’ data. This will help to motivate athletes to work with the final design and increase user engagement with the Athlete Management System.

Achilles tendinopathy (AT) is a common Achilles tendon injury, yet its exact cause and the factors influencing progression of individuals remain unclear. Strain distribution is indicated to play a significant role in the progression, possibly linked to the twist of the subtendons. Pizzolato et al. (2020) proposed an integrated framework for studying Achilles tendon mechanics, including a finite element (FE) model estimating local displacements in the Achilles tendon. However, before im- plementing this in AT research, further testing and validation is necessary. Therefore, to make a start for future improvements, this study aims to build a foundational FE model of the Achilles tendon, verify it with in vivo local displacement data and assess the sensitivity to subtendon twist. 3D ultrasound and X-rays of the ankle provided the geometry and the moment arm of the Achilles tendon, respectively. By minimizing the error between the tendon’s elongation during contraction and the FE model prediction, the material properties were optimized. Local displacements in the sagittal and coronal plane were computed using estimated forces from in vivo studies. Simulated subtendon twists (11◦, 37◦, 65◦) examined the effect of the amount of twist on the displacement. Comparing the FE estimated local displacements to in vivo data indicated that additional substructure details are needed to accurately calculate the displacement behavior. Modifying fiber twist angles altered the uniformity of the displacements in the FE model. Therefore, further development of the FE model of the Achilles tendon is recommended before incorporating it into an Achilles tendon mechanics study.

This study investigates different strategies that the central nervous system might adopt to solve the motor redundancy problem in young and older adults, focusing on metabolic costs, head accelerations, and gait adjustments during overground walking at various speeds. The study addresses gaps in previous research, which primarily focused on younger participants and treadmill-based trials, potentially overlooking natural gait patterns. Ten younger adults (aged 23–28) and five older adults (aged 69–77) completed eight overground walking trials at different speeds, including their preferred walking speed (PWS), predetermined speeds constant for each subject, and variations of their preferred walking speed.

Results showed that younger and older adults had similar preferred walking speeds and comparable metabolic costs when walking at their chosen pace, while younger adults exhibited higher metabolic costs at higher speeds. The PWS did not minimize the metabolic cost for either age group. At their PWS, younger adults both reduced head accelerations and maximized stability, whereas older adults prioritized stability over movement smoothness. Both groups primarily adjusted step frequency rather than step length to accommodate changes in walking speed. Additionally, no significant differences were found in maximum arm swing velocity between the two groups. These findings challenge previous assumptions about age-related differences in walking efficiency and suggest that stability may play a more critical role in gait optimization for older adults. Further research is necessary to uncover the mechanisms driving these adaptations and their impact on gait across the lifespan.

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.

Running is one of the most practiced sports worldwide, offering numerous health benefits, but also carrying a risk of injury, mainly at the knee and ankle joints. The origin of running injuries is not fully understood. With predictive neuromusculoskeletal simulations, more insight could be gained into the biomechanical mechanisms that may lead to injuries.
However, in predictive simulations of gait, hyperextension of the knee during stance phase is often encountered. This limits their applicability in research into running-related injuries. It is unclear what causes these unrealistic kinematics, with various studies coming to conflicting conclusions.
This study aims to identify the cause of knee hyperextension in predictive models of running and subsequently, to determine the essential modeling elements for accurately simulating stance knee flexion.

A structured analysis was conducted to investigate the potential impact of the model components within the predictive simulation framework. This framework was divided into four main categories: the objective function, the musculoskeletal (MSK) model, the foot contact model, and the controller. The analysis resulted in numerous hypotheses regarding the element that might be responsible for the simulation of realistic knee kinematics. SCONE, an open-source package for neuromusculoskeletal predictive simulation, was used to test the effect of each hypothesis on the simulated running kinematics. The simulation outcomes were compared to experimental data to assess possible improvements.

The results demonstrate that, in contrast to previous literature, adaptations to the objective function, the MSK model, and the foot contact model have negligible effects on predicted running kinematics. This leads to the conclusion that the controller is essential to focus on when improving knee kinematics. Due to time constraints, multiphase control could not be implemented. Therefore, the exact reflex pathways and phase transitions should be further investigated for the predictive simulation of running before implementation is possible.

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.

At Basalt rehabilitation clinic The Hague, gait analysis is currently mostly observational. No objective measures are used and there is no systematic use of recordings. The aim of this research is to find and verify an accessible instrumented gait analysis method that could be implemented in the current care path at Basalt The Hague. The current instrumented gait analysis systems are marker-based. Recently more research has been done with regard to markerless systems, since markers bring along artefacts like the soft-tissue artefact. Markerless systems work with pose detection through artificial intelligence. A limitation of these pose detection systems, is that it is still in a research phase and did not make its way into the clinics yet. In a comparison of the accuracy of open-source pose estimation methods for measuring gait kinematics, OpenPose has been found most accurate.

For this research a program of requirements is made for implementation of instrumented gait analysis in the clinic at Basalt The Hague. The coordinates of anatomical landmarks obtained with OpenPose are processed to kinematic parameters. Besides, spatiotemporal parameters are looked into. A gait analysis set-up (consisting of tripods and mobile phone cameras) with OpenPose is proposed and tested to verify the accuracy. The OpenPose outcomes are compared with the the Simi motion capture system at Basalt Delft. By determining the Pearson correlation coefficients between OpenPose and Simi motion capture system, the kinematic parameters are verified. The mean standard deviations of the repeated recordings with OpenPose are used to determine the repeatability of OpenPose.

With this research is verified that OpenPose is repeatable and that kinematic parameters of the hip and knee are highly correlated to the standard method of instrumented gait analysis used at Basalt. Therefore can be concluded that OpenPose pose detection seems a promising method for determining kinematic and possibly also spatiotemporal parameters of gait. With follow-up research, especially clinical validation, and further development of the data processing, the first steps can be made towards implementation of accessible instrumented gait analysis in the current care path at Basalt The Hague, and possibly other locations and/or institutions.

Humans perceive a pulling or pushing sensation when subjected to an asymmetric vibration. This so-called pseudo force has great potential to guide human movement. Previous research has exclusively focused on the effect of pseudo forces in open-loop environments, in which the user’s joint angular velocity cannot be corrected. As the latter is essential for providing movement guidance, this paper proposes the first closed-loop system in the field of pseudo forces, using amplitude-modulated pseudo forces as haptic feedback. With this feedback, the user was assisted in moving towards a specific target angular velocity. In a human factors experiment, the amplitude-modulated stimuli were compared to constant-amplitude stimuli. The results showed that amplitude-modulated pseudo forces significantly decreased the error between the user’s and the target angular velocity when continuous movement in the desired direction was achieved. Therefore, the study demonstrated that amplitude-modulated pseudo forces can effectively guide human movement, representing an essential step towards developing a wearable movement guidance device.

Introduction: During the sit-to-stand (STS) motion, thigh push-of (TP) is frequently used, yet the biomechanical advantage for the upper extremity, is relatively unknown. In this thesis, the STS motion is analyzed for three different techniques; TP, armrest push-off (AP), and no arm aid (NA). The aim of this study is to determine the biomechanical advantage of the TP strategy through examining the joint moments (JM), and muscle forces (MF). Furthermore, the study aims to find whether age or gender affects the JM and MF generated in the TP, AP, and NA strategies. Method: Time to stand (TTS), JM and MF exerted on the upper extremity were examined for TP, AP and NA strategies for 34 participants across 3 groups: EM, elderly female (EF), and young males (YM). The metrics were obtained through inverse kinematic (IK), inverse dynamic (ID), and static optimization (SO) simulations in a 3D musculoskeletal model. Results: The time-to-stand (TTS) in elderly participants is significantly longer in the TP strategy than in the AP and NA strategies. For elderly people, the TP strategy results in upper extremity JM lower than during AP and equal as in NA. Similarly, the TP strategy results in significantly lower MF than the AP strategy, and equal MF as in the NA strategy. Conclusion: The TP strategy takes longer than AP and reduces the JM and MF for elderly participants. Moreover, the TP strategy does not yield higher JM and MF than the NA strategy for any participant group. Thus, the biomechanical advantage of the TP strategy for elderly people, are lowered JM and MF in the upper extremity

The Dutch speedskating federation expressed the need for a feedback system for elite long-track speedskaters that can aid them in finding the optimal technique for an individual athlete. To contribute to this goal, this research aims to develop an optimization workflow that can reproduce realistic steady state speedskating behaviour. This is done with use of the simple skater model (SSM) (Van Der Kruk, Veeger, van der Helm, & Schwab, 2017). The research consists of two phases. The first to verify if the optimization can produce realistic speedskating motion, the second optimizes the speedskating technique to minimize the duration of one stroke. The optimization is solved with IPOPT. Even though the first phase can reproduce realistic speedskating motion, the optimal technique found in the second phase was unrealistic in terms of both trajectory and applied forces. This is caused by an inconsistency in the heading of the skate. This research shows the capabilities and limitations of optimizing the speedskating technique with the SSM.

As the population aged 65 and above increases, falls among these older adults emerge as a significant public health concern, leading to disabil- ities and economic burdens. Preventative strategies and personalized fall risk assessments are essential for mitigating fall risks. Human Activity Recognition in early fall risk detection by monitoring everyday ac- tivities in older adults could assess patients fall risks. However, current literature has overlooked the older adult demographic by only measuring adults younger than 65, under representing the older population. This research specifically focuses on identifying key features from head-mounted Inertial Measurement Unit (IMU) data using machine learning to classify Sit-to-Walk (STW) and Walk-to-Sit (WTS) move- ments, which are commonly associated with high risk of fall. In addition these movements can be essential in monitoring changes in performance to asses fall risk. We analyzed five activities STW, WTS, Sitting, Swing phase, and Others. Using three feature selec- tion methods (Mutual Information Gain, ANOVA, Recursive Feature Elimination) on 116 extracted fea- tures we were able to rank the features and select the top ten. The study then evaluated the accuracy of three classifiers (Logistic Regression, Random For- est, and K-Nearest Neighbor or Support Vector Ma- chine) with these features. Results indicated that the ANOVA and Random Forest classifier combination achieved the highest total accuracy of 95%, with Ran- dom Forest performing exceptionally well in STW and WTS classifications, reaching up to 81% accu- racy. Commonly selected features across all methods included the accelerometer’s maximum x-axis mea- sured and its energy in both time and frequency do- mains. This model’s performance is comparable with existing literature and validates its effectiveness in fall risk detection.

Muscle fatigue's indirect link to higher athletic injury risks is a key focus of this study. It highlights how fatigue-induced shifts in muscle resource allocation and movement patterns can lead to biomechanical imbalances, subsequently heightening injury susceptibility. Addressing high injury rates in athletics, this study was conducted in two pivotal phases: the development of a wearable, textile-integrated surface electromyography (sEMG) garment, and the identification of the most effective real-time fatigue metric for true wireless detection for dynamic exercise. While traditional sEMG methods provide valuable insights in laboratory settings, they fall short in dynamically and individually monitoring muscle fatigue in real-world scenarios.

The initial phase focused on creating a smart garment with integrated textile-based electrodes named the RunWave. The second phase concentrated on analyzing muscle fatigue during dynamic running activities, employing an incremental treadmill exercise test. Fatigue was assessed using cardiorespiratory metrics and Borg's Rate of Perceived Exertion (RPE), alongside the evaluation of six fatigue metrics: Average Rectified Value (ARV), approximate and sample entropy, instantaneous mean and median frequencies, and Dimitrov's Spectral Fatigue Index. Significant differences between fatigued and non-fatigued states were observed, especially noted in shifts in entropy, mean and median frequencies, and most prominently in ARV. These findings underscored the necessity for personalized fatigue monitoring strategies, given the variation in fatigue onset and subjective exhaustion experiences among individuals.

The RunWave, with its focus on the ARV metric, emerged as particularly promising for fatigue detection. ARV's computational simplicity and interpretability make it ideal for real-world applications. Despite initial challenges such as fitment issues, electronic limitations, and garment robustness, the RunWave garment was positively received for its comfort and practicality. With targeted improvements, the RunWave garment, leveraging ARV, shows great potential for effectively monitoring muscle fatigue in runners, suggesting a substantial step forward in reducing injury risks in athletic contexts.

Predictive simulation is a powerful tool that can be used to examine the impacts of aging on complex movement behaviors. These models rely on neuromuscular controllers that modulate sensory feedback, including vestibular feedback, in order to transition between different movement phases. Current models, however, define the phase transitions based on the kinematics of movement without consideration for the underlying neurophysiological feedback mechanisms driving actual behavior. Here, we studied sit-to-walk movements, a challenging task commonly faced by aging populations, and examined how vestibular feedback is modulated for the control of balance. We estimated the coupling between an electrical vestibular stimulus and ground reaction forces in healthy participants (N = 16) while they performed a sit-to-walk task. Because sit-to-walk transitions are thought to be comprised of simultaneous transitions of standing up and walking, we also compared the sit-to-walk (STW) task to sit-to-stand (STS) (N= 8) and gait-initiation (GI) tasks (N = 8). Four main phases of vestibular control were identified for STW: quiet sitting, flexion, transition, and gait. Similarly, four main phases were identified for STS, though they differed after the first two: quiet sitting, flexion, rising/stabilizing, and quiet standing. In contrast, five main phases were identified for GI: quiet standing, adjustment I, adjustment II, transition, and gait. Importantly, the timings of the identified phases differed from the timings of the events used to define kinematic phases, and the magnitude of the vestibular responses was modulated gradually between phases. We also found that the vestibular modulation observed in STW could be explained as a sharp shift from an STS task just after flexion, around seat-off, into a GI task starting at transition. These results demonstrate that defining the timing of neuromuscular controllers in predictive simulation based on neurophysiological events may be better suited to improving their accuracy.

The ability of musculoskeletal models to acquire metrics and simulate human motion enables researchers to perform biomechanical analysis of daily activities. The improved analysis of human motion could for instance help recognize physical decline early. The sit-to-stand movement, which is performed 60 times daily on average, is one greatly affected by physical decline. Predictive simulation could enhance comprehension of the influence of physical decline, but most musculoskeletal models are not suitable for predictive simulation as they lack computational speed. In this project, a simplified musculoskeletal model called the H1126 with 11 joints and 26 muscular actuators suitable for sit-to-walk simulations is developed. Muscle moment arms at vertebral levels T12 through S1 are based on literature and optimized in OpenSimCreator using via points. Muscle-tendon minus tendon slack length, normalized fiber length, and maximum joint moments of the H1126 are compared to three state-of-the-art OpenSim models containing torso musculature. Results show that the H1126 performs well in terms of muscle geometry and force output. The H1126 muscle moment arms are within a 2 SD margin compared to the literature. The H1126 muscle-tendon length minus tendon slack length and normalized fiber length are within a 10\% margin compared to the state-of-the-art comparison models. Maximum joint moments of the H1126 are also within a 2 SD margin compared to the state-of-the-art torso models and literature. The H1126 is more suitable for predictive simulation than the comparison models as it is computationally faster due to the minimized number of muscle fascicles and the use of purely via points while performing similarly in terms of force output.