Platooning has become a useful area for better transportation efficiency on highway driving. As Cooperative and Automated Vehicles continue to evolve and integrate , it is important to have insights into their implications, emphasizing the need for rigorous real-world assessments. In general, platoon formation is monitored by Cooperative Adaptive Cruise Control (CACC), which uses real-time vehicle-to-vehicle (V2V) communication to exchange vehicle status information, improving the control reaction as platoon members adjust to their surroundings. Automated systems can normally drive vehicles to perform planned behaviors based on the pre-setting by humans, but if the platoon encounters disturbances, the extent to which the automated system can still follow human intentions is still unknown. This research uses field operational test (FOT) data from the CACC platoon on an arterial corridor to assess the platoon's performance when disrupted during the test. This research applies the concept of meaningful human control (MHC) with focus on tracking condition. Additionally, this study will focus on human 'reasons', both distal and proximal. An evaluation framework for platoons is created by categorizing 'Tracking' into three main metrics: comfort, safety, and local stability. Furthermore, this study demonstrates that disturbance has variable degrees of detrimental impact on the platoon's tracking state, and that these effects may be recovered when the disturbance has concluded; however, different disturbance situations indicate different recoveries. The evaluation methodology of this paper provides insight into the tracking performance of CAVs, which can help road authorities build infrastructure for their wider deployment of CAVs. Last but not least, this study may provide guidance to automation technology organizations and automobile manufacturers on how to develop vehicles so that they follow human reasons more closely.

Stroke survivors often struggle with gait asymmetry post-therapy. Researchers are exploring Virtual Reality (VR) to address this problem with the help of visual feedback and virtual avatars. VR also makes repetitive tasks more enjoyable, improving patient compliance and outcomes. Motor adaptation, essential in rehabilitation, involves adjusting movements to new conditions. Studies have used forms of motor adaptation with visual feedback to distort participants' gait symmetry, making people walk asymmetrically. This approach is called implicit Visual Feedback Distortion (VFD), where visual feedback is manipulated without the user's awareness. This thesis explores using implicit VFD with avatars in immersive VR to address gait asymmetry. An experiment with 11 healthy participants tested implicit VFD by gradually increasing the step length of the avatar's right leg. Contrary to previous screen-based VFD studies, results showed no significant effect on gait symmetry. Additionally, no correlations were found between step symmetry and psychological states (presence, embodiment, motivation). We hypothesize that the high distortion level on the right foot, suboptimal virtual environment design, and reported neck pain contributed to these findings. Future research should explore different VFD designs, or look at multiple groups to see what conditions lead to more gait asymmetry during adaptation.

As autonomous vehicle (AV) technology progresses, the necessity for a comprehensive understanding of interactions between AVs and human-driven vehicles (HVs) becomes paramount, particularly in critical manoeuvres such as merging. Mastering merging interactions is essential for enhancing road safety. Existing research in this field focuses on how the AV performs the merging manoeuvre but often fails to assess how they influence these interactions. By drawing inspiration from Human-Robot Interaction and Human Aware Navigation, this study aims to bridge this gap by examining how these interactions influence driver workload, measured through fixations duration, perceived safety and drivers’ subjective perception during merging scenarios. We employed a Virtual Reality environment to simulate realistic driving conditions and measure driver responses. We conducted an experiment where participants engaged in merging manoeuvres with each other and, subsequently and without being informed, with the AV described in “Planning for cars that coordinate with people” [1]. This approach allowed for an unbiased assessment of natural driver reactions to AV behaviours. Our findings reveal significant increases in driver workload and decreases in perceived safety during HV-AV interactions, compared to HV-HV interactions. These results suggest that current AV algorithms may not fully account for the complexity of human-AV interactions, highlighting a need for interaction evaluation in the AV development. Participants’ subjective feedback indicates a recognition of and negative reaction to AV driving behaviours, emphasizing the importance of designing AVs that are both efficient and intuitive for human drivers. The study’s implications suggest improving AV controllers’ evaluations by including their interactions with human drivers. By integrating interaction evaluation, AV technologies can achieve smoother and more successful integration into existing road systems, enhancing predictability and driver acceptance. This study marks a step towards understanding the interactions between AVs and HVs, offering insights that could steer future research and development in autonomous driving technologies.

Robots can aid in post-stroke motor function recovery and motor learning through the use of haptic feedback during collaborative training. A clear objective in robotic-assisted motor learning is to adapt the haptic feedback to individual users, but personal characteristics are not yet considered in this adaptation.

We investigated the suitability of a haptic guidance feedback strategy, based on participants’ locus of control character trait, compared to training without haptic guidance. For this purpose, a motor learning experiment was conducted on 42 healthy participants, where the internal dynamics of a pendulum had to be learned in order to hit upcoming targets. For two groups, training either with or without haptic guidance, we assessed motor learning and its generalization to similar tasks through target hitting performance, as well as behavior during training and perceived user experience.

Evidence was found of a relatively better performance improvement in both training and long-term (generalization of) motor learning for participants with a more internal compared to external locus of control. Lower observed interaction force during training and increasingly better performance throughout training
in these participants may have caused these motor learning differences. More positive user experience in these individuals through a higher perceived control over the pendulum and lower perceived frustration with haptic guidance may have also contributed.

Combined, this suggests an intrinsically better compatibility
with haptic guidance for people with a more internal rather than
external locus of control, for motor learning, during training and
in user experience

Road traffic safety is a pressing global concern, with millions of yearly fatalities and injuries. This study aims to address the detection of abnormal driving behaviour. Traditional supervised approaches face limitations due to the need for labelled abnormal driving data. To overcome this challenge, semi-supervised machine learning models are explored and developed in this research.
Machine learning is utilized for abnormal driving behaviour detection because it offers a data-driven approach that adapts to different scenarios and captures subtle patterns. Furthermore, its scalability allows for efficient analysis of large datasets, leading to accurate identification of abnormal driving behaviour and valuable insights for enhancing road safety measures. Most existing machine learning (ML) based abnormal driving detectors rely on (fully) supervised ML methods, which require substantial labelled data. However, in the real world, labels are only sometimes available, and labelling large amounts of data is tedious. Thus, there is a need to employ unsupervised or semi-supervised methods to make the detection process more feasible and efficient. Luckily, it is possible with the advent of deep neural networks, especially autoencoder-based ones. This thesis develops and compares three ML methods: supervised (e.g. XGBoost and Random Forest), unsupervised ML (e.g. Isolation Forest and Robust Covariance), and semi-supervised ML (Hierarchical Extreme Learning Machines). Comparison results show that the semi-supervised deep learning model outperforms unsupervised methods exhibiting higher prediction accuracy and delivering acceptable results compared to the fully supervised models.
Moreover, previous ML-based approaches predominantly utilize basic car motion features (such as velocity and acceleration) to label and predict abnormal driving behaviours. In contrast, this thesis introduces Surrogate Measures of Safety (SMOS) as features for ML models to identify abnormal driving behaviour.
The results indicate that the supervised model performs best under the same conditions. However, relying on a large amount of labelled data in supervised models can pose challenges in real-life scenarios or when dealing with massive datasets. The study highlights the significance of Surrogate Measures of Safety (SMOS) and demonstrates the potential of HELM in effectively identifying abnormal driving behaviour. The introduction of SMOS significantly improves the performance of both unsupervised and semi-supervised models. The unsupervised model shows the most substantial improvement, increasing accuracy from less than 50% to over 90%.
While the Isolation Forest and Robust Covariance models fail to detect abnormal driving behaviour without including SMOS, the semi-supervised HELM model exhibits promising results even without SMOS. However, further research is necessary to address limitations and enhance the findings. While valuable, the current dataset used in this study may only encompass some types of abnormal driving behaviour. Future research should incorporate a more diverse dataset that covers a broader range of abnormal driving behaviours. The analysis should include multiple SMOS features, such as Post Encroachment Time (PET), to comprehensively understand abnormal driving behaviour and improve safety measures.

Severe muscle weakness is a symptom appearing in certain neuromuscular diseases (NMDs), such as Duchenne Muscular Dystrophy (DMD), affecting people's daily lives by reducing functionality, decreasing independence, and reducing the ability to perform essential daily activities. This patient group might benefit from using active-assistive devices by having the potential to provide precise support torque counterbalancing the passive forces acting on the arm, the movement intention of the user, and external forces exerted by lifted objects. However, the determination of support to counteract the weight of lifted objects is an ongoing challenge. This research aims to improve the understanding of external forces by using data classification algorithms to distinguish between different lifted weights in a human experiment. Fourteen healthy individuals participated in this experiment, lifting weights ranging from 0 - 1000 grams while an active-assistive device compensated for the passive torques acting on the arm. Data was collected using various sensors: a force sensor, an Inertial Measurement Unit (IMU), a joint encoder, and surface Electromyography (sEMG) electrodes. Subsequently, this data was processed and fed into a K Nearest Neighbour (KNN) classifier and a Support Vector Machine (SVM) classifier to determine the lifted weights during human elbow flexion and extension. The classifier showing the highest performance achieved an accuracy of 39.70% on the test dataset, indicating several misclassifications. However, a recall percentage of 76.95% for the 1000-gram class within the multi-class classification demonstrates the capability to distinguish larger weights. While demonstrating potential in weight discrimination, especially for larger weights, improvements in the compensation strategy, arm support alignment, and experimental design are crucial. Future research on the impact of picking and placing objects, the influence of muscle weakness, and the application of alternative data classification algorithms are essential to further enhance understanding of the interaction with objects and result in more accurate predictions.

Background: The procedure to fit a prosthetic socket to a patient, which can assure the patient’s comfort during activities of daily living, is labour intensive. Such a lengthy procedure could benefit from an automated and more efficient data-driven method capable of automatically tracking the relative movement between the patient’s tibia and the prosthetic socket. To investigate such a method, we acquired in-socket bone displacement data during the physical activities of the prosthetic user. Manually tracking the location of the tibia from, e.g., B-mode (imaging) ultrasound (US) sequences might be a solution, but this is time-consuming, and the interpretation of the sequences is highly operator dependent. Therefore, an automated and efficient method to assess socket fit in US sequences is needed.
Methods: We used an existing 3D U-Net with a long short-term memory module (LSTM) and compared its ability to track a landmark location point on the tibia in US recordings by comparing the displacement and similarity in shape with data obtained from a semi-automatic single-point tracker. To evaluate the performance of the developed automated workflow, we obtained experimental data from three participants who performed three repetitive stepping tasks with their prosthetic leg in a sideways, forward, and backward motion. Three deep learning models were trained with a varying hold-out method (66% training data, 34 % test data) to test the ability to track a landmark location on the tibia in unseen data from one participant. To find the similarity of the deep learning models compared to a semi-automated single point tracker, the normalised root mean squared error (NRMSE) was calculated. We also evaluated the normalised maximum cross-correlation (NMCC) to account for the maximum similarity in displacement trajectory when a delay occurred between the true trajectory and that from the automated model. We analysed the repeatability of each step task per participant with the standard deviation from the mean tibia’s landmark location trajectories.
Results: Due to the delay between the semi-automated single-point tracker and the DL model, the NRMSEs ranged between 27% and 90%. The similarity threshold (0,95) was reached for five trajectories of the tracked point on the tibia in the anterior-posterior direction, with a delay between 1,5% and 8,5% of the step duration. The similarity in the anterior-posterior direction of the tibia’s landmark location trajectory was higher than that in the lateral-medial direction. The SD for all participants was around 1 mm but varied proportionally to the amount of movement observed per participant. The SD of the DL models was similar to that of the semi-automated single-point tracker.
Conclusion: We conclude that a DL model from a 3D U-Net with an LSTM module has the potential to assist prosthetists and researchers in tracking in-socket tibial bone movement in the anterior-posterior direction.

Neurological disorders in the nervous and neuromuscular systems affect approximately 260 million people annually and among these 255 million would benefit from rehabilitation [4]. Patients with neurological disorders usually require multi-dimensional rehabilitation, involving physical, cognitive, psychological, and medical help. Children with trunk control problems arising due to some of these neurological disorders also require such multi-dimensional rehabilitation. A major part of this is administered to the patient through the activities of a physiotherapist in the clinical context. But the limited number of physiotherapists result in exercises often being prescribed for patients as in-home rehabilitation. During in-home rehabilitation, the patient and the primary care-giver may not be able to comply with the prescription without feedback from a physiotherapist.
To address this challenge, this paper proposes an automated method for assessing movement quality of children during trunk control rehabilitation exercises. We adopted a Human-centered AI approach to the development of our system. We identified the needs of physiotherapists for assessing patient’s functional abilities through semi-structured interviews with six physiotherapists. As a result, we co-designed and developed an artificially Intelligent decision support system that automatically assesses the quality of motion. We created a trunk-control rehabilitation exercise movement dataset based on a protocol co-designed by the authors and the physiotherapists. The data was collected
from 15 typically developing children (mean age 7 years, range 4–10 years) using a ZED-mini stereo-camera and the quality scores as ground-truth were obtained from a physiotherapist. The exercises involved reaching targets kept on a table in front
while being seated away from the table on a stool. We investigated the performance of Random Convolutional Kernel transform and XCM, two state-of-the-art multivariate
time-series classification algorithms on this dataset and achieved a quality prediction f1-score of 65% on the test dataset and similar promising results on the detection of compensatory movements in the exercise motion. In addition, to increase the trust-worthiness of our AI solution, we have provided explanations on the predictions of the black-box algorithms, which can aid the users of the system to understand the causal relationships between the input and output to the AI algorithm.

Current commercial Driver Steering Assistance Systems (DSAS) focus on path-tracking performance without taking into account driver intentions. Improved driver-automation interaction can be achieved by sharing vehicle lateral control through torques. Furthermore, integrating a driver steering-torque model allows to better match driver intentions. In this research, an existing driver model is adapted and parametrized for estimating driver steering-torque. Driver behaviour is modelled by learning the parameters of a Hidden Markov Model (HMM) and estimation is performed with Gaussian Mixture Regression (GMR). A parameter selection framework enables to select model hyper-parameters objectively. First, feature relevance is determined with an extensive feature
selection step. Thereafter, an iterative overfitting criteria is employed to select the number of hidden states. Final model behaviour is determined by adjusting the metric weights of a linear cost-function with the aim to trade-off estimation accuracy and smoothness. Naturalistic driver steering-torque data from seven participants was gathered in a fixed-base driving simulator at Toyota Motor
Europe for the training, evaluation, and testing of the proposed model. The results demonstrate that a 92% model accuracy can be achieved while the estimated steering-torque is 37% smoother and requires 90% less data compared to a baseline model.