Visually induced motion sickness (VIMS) is a potential limitation to user comfort in gaming or training systems under an immersive virtual reality (VR) environment, yet objective indicators of individual susceptibility remain insufficiently established. The Postural Instability Theory proposes that a higher motion sickness (MS) incidence will occur when an individual is unable to maintain postural stability before an MS-inducing exposure. Postural instability has been associated with MS, predominantly focusing on whole-body postural responses under standing conditions, but responses under seated conditions and the role of individual head–neck control characteristics remain unclear. In addition, the individual MS severity was barely associated with postural instability during an MS-inducing exposure.

This study investigated whether pre- and in-exposure postural sway and head–trunk stabilization characteristics are associated with VIMS severity during VR exposure. Twenty-five participants completed standing and seated head-sway measurements under eyes-open and eyes-closed conditions, followed by a head–trunk stabilization test with yaw perturbation on a rotational seat and two VIMS-inducing VR exposures. VIMS severity was continuously recorded using the Misery Scale (MISC), supplemented by Simulator Sickness Questionnaire (SSQ) scores, while vection incidence was continuously reported during exposure. Head motion was characterized using time- and frequency-domain features, and head–trunk stabilization was quantified using frequency-dependent gain, phase, and coherence measures.

Pre-exposure head sway showed stronger associations with VIMS-related outcomes than head motion measured during exposure. In particular, the difference between eyes-open and eyes-closed sway in the seated condition (sit, EO-EC) exhibited the strongest overall associations, with the peak medial–lateral angular velocity showing a significant negative correlation with SSQ-nausea subscores in one of the VR exposures after correction for multiple comparisons (ρ=-0.721, p_bonf=0.01, m_bonf=288). Furthermore, the median sway frequencies calculated under the same condition (sit, EO-EC) were found to be highly positively correlated with all SSQ sub- and total scores (Nausea: ρ=0.605; Oculomotor: ρ=0.531; Disorientation: ρ=0.628; Total Score: ρ=0.626). These findings support the relevance of postural control mechanisms to individual MS severity proposed by the Postural Instability Theory in a seated VIMS context. They extend previous work by demonstrating that pre-exposure seated sway, rather than only exposure-induced postural motion, may contain information about individual VIMS susceptibility.
Selected stabilization features in the head–trunk stabilization test showed associations with inverse end time, but no consistent relationships with MISC or vection incidence were observed. Overall, the findings indicate that pre-exposure seated head sway, particularly its visual dependency, may serve as a useful objective marker of individual VIMS susceptibility, while task-dependent head–neck dynamics should be considered when interpreting cervical motion responses in immersive VR environments. ... Read more

Automated Vehicles (AVs) may improve road safety, traffic efficiency, and occupant productivity by enabling passengers to engage in Non-Driving Related Tasks (NDRTs). However, reduced control, limited anticipation of vehicle motion, and visual distraction can exacerbate Motion Sickness (MS). This work proposes a non-intrusive computer vision framework for estimating passenger head motion from depth-camera data to support real-time MS assessment.

A machine-learning-ready RGB-D dataset was created using a custom 6-DoF motion simulator, with an Azure Kinect depth camera and motion capture system providing synchronised measurements and ground-truth poses. An anatomically grounded head coordinate system based on the vestibular organs was introduced to align the measurements with human motion perception. The dataset comprises 42 trials from 17 participants and 323,424 frames, covering different driving speeds, glasses conditions, and instructed head movements.

Five machine learning architectures were evaluated using 5-fold participant-level cross-validation. A post-processing pipeline combining coordinate calibration and non-causal low-pass filtering reduced tracking errors by approximately 2.5–4 times across all models. The best-performing model achieved mean MSDV-weighted coherence values of 0.890 for orientation and 0.941 for position within the primary MS frequency range (0.05–1 Hz). These results demonstrate the feasibility of depth-camera-based head-motion measurement for mathematical MS prediction and non-intrusive passenger comfort monitoring in future AVs. ... Read more

Can we prevent motion sickness in the age of automated driving?

As automated vehicles position drivers as passive passengers and simulators become increasingly immersive, motion sickness has emerged as a critical barrier to user acceptance. Traditional models rely on group averages and focus on extreme outcomes, failing to capture the subtle, individual discomforts like nausea and dizziness that ruin the passenger experience.

This book presents a framework for predicting and mitigating motion sickness at the individual lev el by moving b eyond a one-size-fits-all approach. The research introduces a personalized modeling method that adapts to specific user sensitivities using two key parameters and proposes critical updates to sensory conflict models to better align visual perception with reality. These culminate in a novel control algorithm for simulators that reduces motion sickness by over 50% without sacrificing realism. This work aims to bridge the gap between biological variability and mechanical design to create a more comfortable experience. ... Read more

Lower back pain (LBP) is the leading global cause of disability, with 619 million cases in 2020 projected to rise to 843 million by 2050. The societal and economic burden is substantial; in the UK, the National Health Service spends over £5 billion yearly on back-related care, while in the USA, costs from 2012 to 2014 for patients with a spine condition were $315 billion USD. Current treatments are limited, surgery is invasive and costly, while medication such as opioids offers only short-term relief with uncertain long-term benefit and considerable side effects. Spinal traction is widely used as a non-invasive treatment. It may alleviate pain through spinal decompression and improved
disc health, but existing devices are often expensive, uncomfortable, or require supervised use in a clinic.
This thesis describes the design, development, and evaluation of a novel traction device for the treatment of LBP, which is affordable, user-friendly, and suitable for unsupervised usage in physiotherapy practices or at home. A patient study of an early seated prototype (Prototype 2) with 20 patients revealed limitations in fixation and comfort: four patients could not be tested due to slipping through the belt fixation, and six of the remaining patients were unable to complete the 10-minute treatment. On average, patients reported only a slight reduction in pain (∆V AS of 0.47 ± 2.60), and a significant negative correlation was found between treatment-related discomfort and pain reduction (r = −0.57, p = 0.022).
Furthermore, market research at YES!Delft indicated that Prototype 2 would be too costly for the intended users. To optimize therapy, a literature review was conducted to determine how the therapist controlled parameters, specifically force, body position, and duration, to assess their potential influence on the treatment effect. The review suggested that traction is most effective at forces of < 20% body weight (increasing up to 40% if necessary), in prone or supine 90/90 positions, and with intermittent traction cycles lasting 10-20 minutes. Additional factors influencing outcomes included patient age, duration of symptoms, presence of sciatica, and timing of follow-up.
Based on these findings, a third prototype was developed and evaluated in a patient study comprising six treatments over a six-week period. The control group (n = 4) received manual traction in combination with mobilization and detonizing (which is standard physiotherapy treatment) and in the study group (n = 6) manual traction was replaced with treatment by Prototype 3. Both groups reported reductions in pain (VAS) and disability (ODI), with statistically significant improvement observed only in the study group (p = 0.0216). No significant differences between groups were detected, though effect sizes indicated individual improvement. These results demonstrate the viability of a patient-operated traction device and suggest therapeutic potential comparable to that of current physiotherapy treatments and reported literature. While further research with larger study groups and refined design parameters is needed, the device shows promise as a cost-effective solution for use in physiotherapy practices or home settings. ... Read more

This thesis examines approaches for motion sickness reduction in automated driving applications. It focuses on motion planning methods designed to limit the motions that contribute to motion-sickness onset, and presents an algorithm that incorporates motion-sickness considerations into trajectory generation. Beyond real-world automated driving scenarios, the thesis also examines driving simulators as a platform for evaluating motion sickness oriented planning strategies. Methods for increasing simulator realism are explored to narrow the gap between simulated and on-road experiences. Mitigating motion sickness within the simulator itself is also examined, aimed at improving both simulator fidelity and the reliability of motion-sickness assessments. ... Read more

This thesis addresses the challenge of controlling automated vehicles performing evasive manoeuvres at the limit of handling. Special attention is paid to the development of nonlinear controllers, which can prioritise obstacle avoidance over path tracking objectives while considering vehicle stability constraints, to improve passenger safety. The thesis develops the entire pipeline for obstacle avoidance controllers, focusing on three aspects: vehicle state estimation, collision avoidance and control beyond the stable handling limits, e.g. drifting. ... Read more

Bicycles are among the simplest and at the same time most fascinating vehicles. Despite their apparently basic design, their stability, dynamics, performance, and safety are still largely unknown. In a world where cities suffer from critical air pollution levels and are often congested by car traffic, bicycles can represent a cost-effective and ready solution to address part of the problem. However, the increasing use of bicycles enlarges the risk of bicycle-related accidents and injuries. Many of these accidents involve falls, highlighting the need of proper studies on bicycle-rider dynamics. While research has been conducted on the effect of bicycle parameters on the handling quality properties, we still do not know enough about the specific role of tyres on bicycle-rider dynamics. To better simulate what happens in the real world, existing bicycle mathematical models need to be integrated with robust tyre models that include side slip, as they can change both the lateral and longitudinal dynamics. Actually, the use of nonlinear tyre models allows taking into account the lateral force and self-aligning torque saturation. This is needed for instance to model the wobble as a non-divergent vibration modes, contrarily to what happens when using a simple linear tyre model. These features relate directly to predict critical situations for which we need a deep knowledge of bicycle tyres, thus proper test-rigs to measure their mechanical characteristics... ... Read more

The primary objective of this thesis is to develop a new model that accurately predicts vertical mo- tion sickness by closely mirroring the neural dynamics and statistical properties of the otolith pathway. Unlike existing models, our approach incorporates elements such as noise, nonlinearities, and realistic sensory data, enhancing the accuracy and biological relevance of motion sickness predictions. This re- search aims to create a biologically plausible approach that derives a proxy metric for Motion Sickness Incidence (MSI). This metric will naturally emerge from sensory conflict, providing a more accurate prediction of vertical motion sickness and contributing to the development of systems designed to mit- igate its effects. This has become increasingly critical with the growing prevalence of self-driving cars and virtual reality systems, where effectively addressing motion sickness is of great value. To achieve this goal, we developed a novel model from the ground up, incorporating detailed dynam- ics and statistical properties of otolith hair cells and afferents in the inner ear. Three spiking models were developed based on Leaky-Integrate-and-Fire, Hodgkin-Huxley, and Generalized Linear Model frameworks. Each model presents unique advantages in capturing neural behavior. Our findings indi- cate that while all models offer valuable insights, the Leaky-Integrate-and-Fire model most effectively captures the dynamics, statistics, and temporal characteristics of otolith afferents. The spiking models were used in a scheme that improves upon existing models in three ways. Firstly, and most fundamentally, we added noise and probability elements to our modeling. This makes the models more realistic by reflecting the natural randomness and variations in human behavior and per- ception. By more closely mimicking real-world conditions, our models better represent how humans perceive and respond to stimuli, making the simulations more accurate overall. Additionally, we incorporate nonlinearities to accurately represent sensory saturation and have imple- mented a realistic organ definition, including hair cell tuning. The model relies on actual sensory data rather than simply aligning with subjective experimental results, allowing for a more accurate and bio- logically relevant simulation of sensory processing. Lastly, the spiking models are integrated within a framework that utilizes Bayesian inference to esti- mate vertical acceleration, taking into account the inherent noise in the brain and aligning with modern neuroscience theories. By bridging the Free Energy Principle with the Sensory Conflict Theory, our model provides a more robust framework for understanding and predicting motion sickness. The model gives results that are representative in terms of human motion perception sensitivity, while also being in line with current knowledge about frequency and amplitude dependence of Motion Sickness Incidence. The resulting model gives us insights into how otolith dynamics influence motion sickness and is a proposal on how the brain resolves estimating vertical motion perception. Using different model configurations we were able to create a model that estimates motion accurately and propose a proxy metric for motion sickness incidence. This model is created in a modular way and can be extended on the basis of new objectives or new research findings. Additionally, with further enhancements, it holds potential applications in validating otolith theories on statistics and information transmission characteristics and even in the development of prostheses. ... Read more

The advancements in automated driving hold the potential to improve our daily lives by reducing traffic accidents, minimizing congestion, and allowing more time to spend on other activities. However, realizing these benefits depends heavily on widespread public acceptance, which, according to current research, remains low. Trust and comfort have been indicated as key factors to increase acceptance, highlighting the importance of finding methods of measuring these concepts. This thesis contributes to the development of objective methods for evaluating the emotional state of drivers and passengers in automated vehicles.
In this thesis, an experiment was designed and conducted using a driving simulator to allow participants to experience a ride in an automated vehicle. The aim was to elicit varying levels of comfort by altering the driving style and introducing the presence of a pedestrian, while simultaneously collecting a comprehensive dataset to analyse these comfort levels. This dataset includes continuous subjective comfort ratings given by the participant, vehicle dynamics from the real-world drive on which the simulation was based, webcam footage monitoring the person’s facial expression, Galvanic Skin Response, heart rate (variability), and eye-tracking from 32 participants. Such comprehensive datasets are rare in the literature and provide valuable opportunities for future research to compare different signals and explore their interrelations. From the subjective comfort ratings, it was found that the driving style was a bigger factor than the presence of a pedestrian. Even though the pedestrian did cause a decrease in comfort, the difference between the two driving styles was found to be significantly bigger. For facial expression recognition, a state-of-the-art model was successfully implemented. With minimal lighting conditions, the face could always be detected, and expressions were successfully classified with corresponding emotion labels from the universal set of emotions. Out of the 32 participants, 24 were included in the analyses. Most of these participants (15/24) did not show any detectable reaction in their facial expressions to the critical event. Amongst the 9 participants who did, 8 of them showed a Happy expression, and only 4 showed a Surprise expression. Fear was never dominant. This result shows that, in the current experiment, Facial Expression Recognition is not a reliable method for discomfort detection in Automated Vehicles. Additionally, a neural network was implemented to predict a person’s subjective comfort based on vehicle dynamics and their Galvanic Skin Response (GSR). The model was validated using Leave-One-Out Cross-Validation (LOOCV), where one participant was excluded from the training set and their data was used for testing. The results were promising, as the self-reported comfort and the model-predicted comfort showed a positive correlation across all participants. These findings demonstrate the potential for objective comfort assessment in automated vehicles, reducing the biases inherent in subjective evaluations and paving the way for further research in this field. ... Read more

This thesis explores how automated vehicles will interact with pedestrians in the urban environment through augmented reality technology. Nine distinct AR interfaces were designed, developed, and evaluated to assess how different design elements (symbols, text, colour) and distinct mappings of the AR (on the road, on the vehicle, or head-locked) would affect comprehension, and ultimately whether the pedestrian would trust and be convinced to cross in front of an automated vehicle displaying a safe message. Using increasing levels of ecological validity, from an online questionnaire to a CAVE simulator and an AR HMD experiment, the evaluation also explored how different AR anchoring (and mapping) positions affect pedestrians' crossing initiation times and the intuitiveness of the message. The thesis also explores the use of diminished reality (removal of information) to assist pedestrians in occluded scenarios, as well as the utilisation of Large Language Models in evaluating qualitative data in experiments. The outcomes of the thesis are a set of guidelines based on empirical evidence on how to design effective AR interfaces which promote safe and transparent interactions between pedestrians and automated vehicles. ... Read more

Automated vehicles (AVs) represent a significant leap forward in transportation, aiming to enhance road safety, increase comfort, and improve traffic efficiency. As technology progresses to SAE Level 3 and higher, drivers are increasingly able to engage in non-driving-related activities. However, this (r)evolution raises challenges concerning driver's perceived risk and trust in AVs, which are crucial factors influencing the acceptance of AVs. This dissertation aims to enhance perceived safety and trust in AVs through experimental studies, computational modelling and user interfaces (UIs) design.

The initial phase of this dissertation focused on how drivers' perceived risk and trust when using AVs evolve in close encounters with other road users. We developed regression-based perceived risk and trust models based on a simulator study with 25 participants involving merging and hard braking scenarios on motorways. The proposed models reveal that perceived risk is dynamically influenced by driving conditions and sensitive to individual factors such as driving experience and gender with experienced and male drivers generally perceiving lower risk. Notably, a decrease in trust after high-risk encounters was observed, indicating a close relationship between perceived risk and trust in AVs. Additionally, physiological responses were observed as potential indicators of perceived risk in critical driving scenarios.

To develop a tool for gaining insights on perceived risk, we put forward a novel computational model called potential collision avoidance difficulty (PCAD) model. Drawing inspiration from Fuller's Risk Allostasis Theory and the looming phenomenon, PCAD evaluates the difficulty of avoiding potential collisions by calculating minimal control effort through braking or/and steering needed to navigate safely. By integrating visual looming, factors in the uncertain behaviour of surrounding vehicles, control inaccuracies of the subject vehicle, and potential collision severity, PCAD provided an accurate population-level fitting of perceived risk in our own dataset on highway merging and a published dataset on obstacle avoidance. The findings highlight the need to account for both the longitudinal and lateral dimensions of driving condition, and uncertain behaviours of surrounding vehicles when interpreting perceived risk.

Further exploration of perceived risk was achieved through the creation of a large-scale dataset of perceived risk using an online survey. This new dataset provided time-continuous perceived risk in dynamic driving conditions. A total of 105 events was created including merging, hard braking and lane changes on motorways, while systematically varying multiple control parameters (such as relative speed and distance) to achieve different levels of event criticalities. Deep neural networks (DNNs) were then trained on this dataset to fit perceived risk, and SHapley Additive exPlanations (SHAP) was used to identify the key contributors to perceived risk in the continuous time domain. Aligned with the PCAD model developed previously, the results highlighted the importance of the relative motion information, particularly the distance to other road users and the uncertainty of surrounding vehicle behaviour in shaping perceived risk. This approach not only discerns the dynamics of perceived risk by systematically analysing interactions with other road users but also provides a guide for future modelling of perceived risk. The development of this extensive dataset fills the gap by providing the lacking continuous perceived risk data, thereby supporting further research on perceived risk.

The last contribution of this dissertation was on enhancing perceived safety and trust through optimised design of UIs. A simulator experiment demonstrated that multimodal UIs incorporating both visual and auditory modalities enhanced perceived safety and trust the most. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The findings indicate the benefits of the UIs in enhancing perceived safety and trust but also showed the limitations of using UIs alone during highly critical events. This part of the work suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities to enhance perceived safety and trust.

This dissertation makes several contributions to the field of perceived risk research in AVs. First, it provides foundational insights into perceived risk, demonstrating the significant influences of driving conditions, manoeuvre uncertainties and individual personal characteristics. The computational perceived risk models demonstrate strong predictive power in perceived risk and offer a deep understanding of how perceived risk is shaped in dynamic driving conditions. Additionally, the rich dataset obtained in this dissertation, which includes event-based discrete data and time-continuous data on perceived risk, serves as a new and open resource for future perceived risk research. Lastly, the practical evaluation of the design of UI provided actionable recommendations in enhancing trust and perceived safety, particularly through manoeuvre information delivered using auditory modality. These contributions advance the understanding, modelling, and practical application of perceived risk in automated driving environments, supporting the broader acceptance and integration of AVs.

The dissertation presents various opportunities for the advancement of AV technology and its integration with human factors. Building on the comprehensive datasets, computational models and insights gained in this dissertation, future studies should focus on further refining computational models to capture perceived risk in general scenarios. Expanding data collection efforts to include on-road tests, and more diverse participants will also enhance the generalisability of the findings. Additionally, the design of adaptive UIs that fit individual preferences remains a promising direction for future research. ... Read more

In the near future, travelling in vehicles will no longer be in regular vehicles, but in automated vehicles. The share of automated vehicles is predicted to increase significantly within 20 years. Passengers in automated vehicles will engage in non-driving tasks, such as sleeping, reading, working or just otherwise spending time on their phone. This will result in motion sickness becoming more prevalent, as passengers will no longer pay attention to the road. Therefore, there is a need for research in motion sickness. To further our understanding of motion sickness and possible mitigation strategies, mathematical models of motion sickness need to be developed. The temporal dynamics of motion sickness can be captured in the so called 'Oman model' \cite{Oman90}. However, most literature use group averaged parameters and motion sickness incidence to describe motion sickness. These methods do not capture well enough how individuals respond to sickening stimuli, as recent studies showed that individuals have strongly varying responses to various frequencies. Only, estimating individual motion sickness parameters is costly thus far, requiring multiple experiments to estimate the parameters. This study explains an optimal experiment design, where the input is varied in real-time closed loop manner such that the information content in the input is maximized for estimation of parameters, rusulting in the fact that individual motion sickness parameters could be estimated in a single experiment. Results show that on average, within the first 63 minutes, most parameter estimations have converged. The resulting RMSE is 1.06 on the MISC scale, comparing to other literature. This shows that the frequency and temporal dynamics of motion sickness and an individual level can be estimated at a drastically faster rate than previous methods. To our knowledge this is the first use of optimal experiment design techniques to asses the dynamics of human responses to stimuli in general, which is an important milestone for cybernetics research. ... Read more

Bicycle safety is quickly becoming an increasingly important field as the number of electric bicycles on the streets grows faster each year. E-bikes are able to accelerate quicker and travel at faster speeds than conventional bicycles, increasing the severity of injuries in case of an accident. There are a number of ways to improve safety, such as building better infrastructure, implementing active safety systems, improving bicycling skills, or better protective gear. In this thesis, a controller based on Model Predictive Control has been designed to explore whether it could be used as a haptic guidance system that would improve motor learning of a cycling task, and whether it could assist the cyclist during cycling manoeuvres. The cycling task of lane change manoeuvres performed at a constant forward velocity was investigated. The controller was implemented on a desktop PC, which wirelessly controlled the TU Delft's Steer-by-Wire bicycle -- a bicycle where the steering is enabled by the use of electric motors instead of mechanical coupling between the front fork and the handlebars. To test the controller, ten participants took part in a pilot study. The study was designed following a counterbalanced measures design and the participants were split into two groups that experienced the controller's haptic guidance in different order. During the study, the participants were asked to ride the bicycle on a treadmill and hit virtual targets, shown on a display mounted in front of the treadmill, by carrying out lane change manoeuvres. The hypothesis stated that the controller improves performance while it is assisting the participants. It was found that the controller did not significantly improve immediate performance and this result is likely caused by too low of the task difficulty. However, it is likely that the controller was more effective at improving motor learning of lower skilled participants compared to higher skilled participants, but a small number of lower skilled participants limited the analysis. A short post-hoc no-hands riding test of the same cycling task was carried out to investigate whether the controller is able to carry out lane change manoeuvres with minimal rider input. A significant performance improvement was found during the no-hands test. In conclusion, due to limitations of the study, no performance or motor learning improvement caused by the controller was found. Yet the controller showed promising results in a no-hands riding test, which suggests that the controller could be used as a starting point for advanced safety systems for bicycles. ... 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

The objective of this study is to simulate lumbar stabilization in autonomous vehicles through the utility of OpenSim. Considering sufficient isometric strength is required for dynamic analysis, validity of the full-body model developed by Schmid et al. [43] in isometric conditions is examined first. This encompasses the establishment of maximal isometric force exterted by the trunk in sagittal and frontal plane. The model is solved using an inverse-dynamics based static optimization and the model-predicted strength was taken to be the highest external force for which the Static Optimization Tool successfully solved [46]. Finally, isometric model conditions are validated against literature. Predicted strength correlated within 1 standard deviation (SD) of the in vivo measurements and therefore, yielded realistic results in forward, rearward and lateral direction. ... Read more

There is an increasing need for transfemoral prostheses that provide gait support, stability, safety and comfort. Although there are many prostheses available in different levels of complexity and price, there is still room for improvement. It has been proved that the cost of transport (CoT) for walking is significantly increased for transfemoral amputees with respect to their healthy peers. Assisting push-off is one of the main challenges in prosthesis design. Push-off is normally achieved by plantarflexion of the ankle joint. Prosthesis designs should aim to restore this function in order to lower the amount of energy needed for walking.

This study aims to investigate the effect of prosthesis design on the gait pattern through musculoskeletal modelling and predictive simulations. Two prosthesis designs are modelled for these purposes, after which several variations on these models are made. It is hypothesised that the prosthesis that assists in push-off through ankle plantarflexion, should result in a gait pattern that is closer to a healthy one. It should also decrease the CoT. Furthermore, we aim to evaluate the use of modelling and simulations in the customisation of prostheses.

OpenSim was used to create a total of eight models based on a model with 9 degrees of freedom and 18 muscles: a healthy person, a conventional prosthesis model, two scaled versions of the conventional prosthesis model, the walkMECH prosthesis and three variations on the walkMECH. SCONE was used to find an optimal gait pattern for each of the models through the CMA-ES method. CoT-, gait-, degrees of freedom- and reaction force objectives were minimised. The results were evaluated by comparing the CoT, joint angles, ground reaction forces and muscle activation of each model.

The CoT for the healthy model was found to be higher than reported before, based on both experimental and simulation studies. As a result, we have little confidence in the CoT estimation of our models. This is further exacerbated by the finding of a lower CoT for the conventional prosthesis than for the healthy model, in contrast to earlier reports. The results for most other measures were irregular, making it difficult to draw conclusions from them. It is expected that the predictive optimisations did not reach a global minimum, and that the results are therefore not accurate. Future research should aim to solve this problem. It should also be attempted to find the cause of the difference in CoT between our simulations and those of others.

No conclusions could be drawn from the results. Nonetheless, there is a clear potential for the use of musculoskeletal modelling and predictive simulation in the investigation of the effects of prosthesis design on gait. ... Read more

Increased susceptibility to motion sickness, due to the transition away from driving, will be one of the major hurdles in the widespread use of automated vehicles. Sustained exposure to motion sickness can lead to the disuse of automated vehicle technology among users. Thus, there is a need to mitigate motion sickness. To do so, a robust model is desired which can predict motion sickness levels while also accounting for individual differences in susceptibility. Studies have been carried out to study the different dynamics of motion sickness and its development. However, the effect of motion amplitude has not yet been quantified.
This study investigated the amplitude dynamics of motion sickness. This was done by perturbing 17 participants along the fore-aft direction with acceleration amplitudes of 1, 1.5, 2 and 2.5 ms^-2 in four separate sessions. In the experiment, both subjective sickness scores and Galvanic Skin Response (GSR) was recorded. Using the subjective sickness scores, we explored variations of the Oman model of nausea that could capture the time-amplitude dynamics that were observed. Along with this, a neural network prediction model was proposed to predict motion sickness levels using GSR as a predictor, which could then be used as an objective measurement of motion sickness. Lastly, to better understand the MISC as a measure of motion sickness, we determined the functional mapping between subjective discomfort and the MISC scale.
Our findings show a monotonous increase in the rate of motion sickness development, but this increase is not linear, with a sudden change in slope after 1.5 ms^-2. This nonlinear increase was captured best by applying a power law to the input conflict vector instead of at the output as proposed in the original Oman model (Oman, 1990) Further, it is found that GSR can indeed be used as a predictor for motion sickness with an accuracy of 77% in the training, 66% in the validation and 62% in the holdout set. Finally, it is found that the subject discomfort has a power-law relation with MISC with a mean exponent of 1.28.
The developed prediction model as well as the variation of the Oman model can be used in other experiments to control the level of sickness to the desired trajectory. The developed models can enable adaptive control algorithms for path and motion planning to mitigate motion sickness. This is will in turn lead to a significant improvement in the experience in commuting with an automated vehicle. ... Read more

Following the literature review, our goal was to study the effect and interaction of motion sickness and motivation on cognitive performance in a reading comprehension task and the associated workload with the task. We chose UCKAT reading tasks for our cognitive task, monetary incentive and ranks as our motivator and a multisine sickening motion profile on a simulator as
our motion variable. We exposed participants to 4 conditions, employing a within-subject experiment design, manipulating our independent variables motion and motivation. We collected motion sickness data via the motion sickness susceptibility questionnaire, misery scale and motion
sickness assessment questionnaire; motivation data via the situational motivation scale; workload data via the NASA TLX workload scale and task performance data via the total score obtained, the total time spent on the task and the average time spent per question. We found that our motion profile caused motion sickness in participants, with some evidence for habituation. We also found some evidence for training effects present in our data. Performance decrements, associated workload and motivation scores across the 4 conditions were statistically similar and we could not conclusively prove our hypotheses. Further analysis showed that amotivation scores almost showed significant effect on task performance which does match anecdotal evidence. MSAQ scores also negatively affected how much time people could spend on a cognitive task. We found that workload scores of participants increased significantly with increase in motion sickness which could give
us an insight on performing cognitive tasks under sickness. Overall, our experiment design could not show the trends that we had hypothesized, and we obtained partial results via our secondary analysis. Our findings indicate that further attention is to be given to the motivation variable to make it more robust. Further, a much large sample size is needed to better test our hypotheses, with perhaps, a mixed subject design for our study. Our study also showed an unexpected interaction of lateral and londitudinal motion profiles, causing significantly higher levels of sickness than what was predicted using existing models, which warrants further research into the same. ... Read more