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This study provides an overview of the impact of the 2021 Summer floods in the Netherlands and the assessment of the effectiveness of various adaptation measures, evacuation strategies, and their impact on society. The floods were characterized by record rainfall in the cross-border region of the Meuse and Rhine basins and resulted in devastating losses in the Netherlands, Germany and Belgium. The study reports on a household survey conducted with 1,509 households in the wake of the 2021 flood event in the southern part of the Netherlands (province of Limburg). Using a descriptive approach, we present household experiences during several stages of the disaster management cycle, reporting on experienced flood hazard and impacts, evacuation, flood damage mitigation measures, the compensation progress, risk perceptions, and stress. Our findings highlight the role of early warnings and flood risk information provision in flood risk management. Risk perceptions influence both adaptation and evacuation behavior, as respondents who were aware of flood risks beforehand took significantly more flood damage mitigation measures compared with those who were not aware. Flood damage mitigation measures, such as building with water-resistant materials and elevating valuables, reduced flood damage by 20% to 50%. Our survey shows that of those who received warnings, the majority actually evacuated. However, residents not aware of any evacuation advice evacuated significantly less. Additionally, the majority (75%) of respondents experienced high or very high stress during and after the flood, which is most likely related to the destructive flood impacts and the slow and uncertain compensation experienced by many respondents. This paper describes the flood event and its consequences to provide insights into Dutch disaster management and what can be learned for potential future disasters in other contexts., Hydraulic Structures and Flood Risk

Accurately estimating the positions of multi-agent systems in indoor environments is challenging due to the lack of Global Navigation Satelite System (GNSS) signals. Noisy measurements of position and orientation can cause the integrated position estimate to drift without bound. Previous research has proposed using magnetic field simultaneous localization and mapping (SLAM) to compensate for position drift in a single agent. Here, we propose two novel algorithms that allow multiple agents to apply magnetic field SLAM using their own and other agents' measurements.Our first algorithm is a centralized approach that uses all measurements collected by all agents in a single extended Kalman filter. This algorithm simultaneously estimates the agents' position and orientation and the magnetic field norm in a central unit that can communicate with all agents at all times. In cases where a central unit is not available, and there are communication drop-outs between agents, our second algorithm is a distributed approach that can be employed.We tested both algorithms by estimating the position of magnetometers carried by three people in an optical motion capture lab with simulated odometry and simulated communication dropouts between agents. We show that both algorithms are able to compensate for drift in a case where single-agent SLAM is not. We also discuss the conditions for the estimate from our distributed algorithm to converge to the estimate from the centralized algorithm, both theoretically and experimentally. Our experiments show that, for a communication drop-out rate of 80%, our proposed distributed algorithm, on average, provides a more accurate position estimate than single-agent SLAM. Finally, we demonstrate the drift-compensating abilities of our centralized algorithm on a real-life pedestrian localization problem with multiple agents moving inside a building., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok, Offshore and Dredging Engineering

The floods in the Netherlands in the summer of 2021 led to severe economic damage and losses in the affected area. A first estimate in September 2021 showed that more than 2,500 houses, more than 5,000 inhabitants and around 600 businesses were affected. Using the Dutch standard Flood Damage and Loss Model (SSM2017), and based on figures from international literature, the total damage in the Netherlands is estimated in the order of € 350 – 600 million. Physical damage to houses and businesses, business interruption, damage to infrastructure and crop losses were the most substantial. The differences in damage to individual structures (residential and commercial) were large. The estimated damage in the affected area clearly exceeds the damage of the Meuse River floods in 1993 and 1995 which occurred in the same region (converted to 2021 prices: around € 200 million and € 125 million, respectively, excluding damage due to business interruption). It is important to note that the largest damages and losses in 2021 occurred in the smaller regional rivers, mainly in the Geul floodplain, while in 1993 and 1995 most damage and losses were recorded in the Meuse floodplain., Hydraulic Structures and Flood Risk

Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the magnetometer. The position of the magnetometer, however, is frequently only approximately known. This negatively affects the quality of the magnetic field map. In this paper, we investigate how an array of magnetometers can be used to improve the quality of the magnetic field map. The position of the array is approximately known, but the relative locations of the magnetometers on the array are known. We include this information in a novel method to make a map of the ambient magnetic field. We study the properties of our method in simulation and show that our method improves the map quality. We also demonstrate the efficacy of our method with experimental data for the mapping of the magnetic field using an array of 30 magnetometers., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for 10-calization algorithms in indoor areas. To compute such a map, GP regression is a suitable tool because it provides predictions of the magnetic field at new locations along with uncertainty quantification. Because full GP regression has a complexity that grows cubically with the number of data points, approximations for GPs have been extensively studied. In this paper, we build on the structured kernel interpolation (SKI) framework, speeding up inference by exploiting efficient Krylov subspace methods. More specifically, we incorporate SKI with derivatives (D-SKI) into the scalar potential model for magnetic field modeling and compute both predictive mean and covariance with a complexity that is linear in the data points. In our simulations, we show that our method achieves better accuracy than current state-of-the-art methods on magnetic field maps with a growing mapping area. In our large-scale experiments, we construct magnetic field maps from up to 40000 three-dimensional magnetic field measurements in less than two minutes on a standard laptop., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

A system of dikes in flood-prone areas continuously requires measures to mitigate changes such as ageing and climate change. Planning costly measures requires proper insight into system risk effects. Especially in a riverine dike system, the risk contributions of individual assets to the system flood risks are not independent, because reinforcement of a dike upstream increases the risks downstream. Tactical plans define the planning of consecutive measures to implement a flood risk reduction strategy, which may take decades. They may differ due to choices such as a prioritization metric, planning conditions and budget. In this study, a method is developed to compare different tactics to prioritize and plan measures in interdependent systems of dikes to reduce risks most effectively and efficiently. A case study meant as a proof of concept was carried out for the reinforcement of about 500 km of dikes along the Rhine River branches in the Netherlands. We studied the effects of 12 different tactical plans on the aggregated risks over time. The economic risks differ by up to about 40%, and the risks on victims differ by up to 70 %. We conclude that tactical planning and corresponding decisions are important for reduction of time-aggregated flood risks., Hydraulic Structures and Flood Risk

The condition of flood defence revetments is influenced by many different degradation processes such as animal burrowing, rutting and growth of weeds. Many of these processes are shock-based rather than progressive continuous. As shocks can cause a drop in performance, this means that the condition of a revetment can suddenly decrease, meaning that revetments can have significant initial damage at the beginning of a storm. Combined with the limited detection probability of common visual inspections of flood defences, this can have a significant influence on the reliability of flood defence systems, something typically not considered in reliability analysis. In this paper we study the reliability of a flood defence system subject to shock-based degradation. Various maintenance concepts are compared for a case study of a riverine flood defence of 20 kilometres length. This demonstrates that the current maintenance concept is insufficient to satisfy the reliability requirements for failure of the revetment. Overall, the joint influence of degradation and the existing maintenance concept leads to a 20 times higher failure probability estimate compared to a typical assessment without these aspects. Next, we demonstrate that both additional inspections, and targeted interventions to reduce the impact of for instance animal burrowing, can significantly reduce total cost and improve robustness of the considered flood defence system., Hydraulic Structures and Flood Risk, Engineering Structures

Hyperbolic localization measures the time difference of arrivals (TDOAs) of signals to determine the location of a wireless source or receiver. Traditional methods depend on precise clock synchronization between nodes so that time measurements from independent devices can be meaningfully compared. Imperfect synchronization is often the dominant source of error. We propose two new message based TDOA equations for hyperbolic localization which require no synchronization and meet or exceed state-of-the-art accuracy. Our approaches leverage anchor nodes that observe each other’s packet arrival times and a novel reformulation of the TDOA equation to reduce the effect of clock drift error. Closed-form equations are derived for computing TDOA in both self-localization and source-localization modes of operation along with bounds on maximum clock drift error. Three experiments are performed including a clock drift simulation, a non-line-of-sight (NLOS) simulation, and an indoor validation experiment on custom ultra wideband (UWB) hardware all of which involved eight anchor nodes and one localizing node in a 128m3 capture volume. Our source-localization approach achieved unprecedented accuracy with lower cost equipment and trivial setup. Our self-localization matched state-of-the art accuracy but with infinite scalability and high privacy. These results could enable economical and infinite density indoor navigation and dramatically reduce the economic cost and increase the accuracy of implementing industrial and commercial tracking applications., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

Flood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty, and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique data set with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure, and vulnerability indicators are estimated and allow for additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an instrumental variable-estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Providing information on flood hazard, as well as implementing early warning systems, is crucial for ensuring effective flood risk management., Hydraulic Structures and Flood Risk

Structural reliability analysis often considers failure mechanisms as correlated but non-interacting processes. Interacting failure mechanisms affect each others performance, and thereby the system reliability. We describe such interactions in the context of flood defenses, and analyze under which conditions such interactions have a large impact on reliability using a Monte Carlo-based quantification method. We provide simple examples and an application to levee failure due to landward slope instability and backward erosion piping (BEP). The examples show that the largest interaction effects are expected when the trigger mechanism is relatively likely to occur and the affected mechanism has a relatively large contribution to the system reliability. For the studied levee example, interactions between slope instability and BEP increased the failure probability up to a factor 4. Implications for the assessment and design of flood defenses are discussed., Hydraulic Structures and Flood Risk, Geo-engineering

This study evaluates three recursive Bayesian input and state estimation algorithms, as introduced in the field of Structural Health Monitoring, for estimating modal contributions for high-tech compliant mechanisms. The aim of estimating modal contributions is the use for active vibration control. High-tech compliant motion stages allow for different sensor configurations, making new and interesting performance evaluations of these filters possible. The algorithms used, namely, the Augmented Kalman Filter (AKF), Dual Kalman Filter (DKF) and Gilijns de Moor Filter (GDF) are implemented on a compliant motion stage for guidance flexure deformation estimation. Our results show the GDF performs overall best, with good estimation performance and real-world tuning capability., Mechatronic Systems Design, Team Manon Kok

This paper presents a method for approximate Gaussian process (GP) regression with tensor networks (TNs). A parametric approximation of a GP uses a linear combination of basis functions, where the accuracy of the approximation depends on the total number of basis functions M. We develop an approach that allows us to use an exponential amount of basis functions without the corresponding exponential computational complexity. The key idea to enable this is using low-rank TNs. We first find a suitable low-dimensional subspace from the data, described by a low-rank TN. In this low-dimensional subspace, we then infer the weights of our model by solving a Bayesian inference problem. Finally, we project the resulting weights back to the original space to make GP predictions. The benefit of our approach comes from the projection to a smaller subspace: It modifies the shape of the basis functions in a way that it sees fit based on the given data, and it allows for efficient computations in the smaller subspace. In an experiment with an 18-dimensional benchmark data set, we show the applicability of our method to an inverse dynamics problem., Team Manon Kok, Team Kim Batselier

The Wettelijk Beoordelingsinstrumentarium (WBI) is the legal set of instruments for flood risk analysis in the Netherlands. Often, engineers have the impression that some failure probabilities of flood defenses resulting from these instruments are overestimated. In an effort to better estimate the failure probabilities of dikes along the Dutch river Rhine, this study sets out to assess them with experts and compare them to model results. We used Cooke's method for combining experts’ estimates in a structured way and follow two approaches to estimate a system failure probability. In the first approach, experts estimate discharges that lead to at least one dike failure. This gives plausible results; failure probabilities between 1/30 and 1/17.000 in a year. The second approach is based on adjusting existing model-based assessment results, by estimating the model-bias and incorporating additional dependencies. This mostly leads to large, implausible, failure probabilities: Experts tend to give more conservative answers as they are asked for detailed estimates without clear reference values. This results in large uncertainty and consequently (too) high failure probabilities. Our research shows that when applied in a clear frame of reference, structured expert judgments can be successfully used for estimating the reliability of Dutch flood defenses., Hydraulic Structures and Flood Risk

Hydraulic Structures and Flood Risk

Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applications. In recent years, it has been shown that, by placing inertial sensors on segments that are connected by rotational joints, the motion of that kinematic chain can be tracked accurately. These methods specifically avoid using magnetometer measurements, which are known to be unreliable since the magnetic field at the different sensor locations is typically different. They rely on the assumption that the motion of the kinematic chain is sufficiently rich to assure observability of the relative pose. However, a formal investigation of this crucial requirement has not yet been presented, and no specific conditions for observability have so far been given. In this work, we present an observability analysis and show that the relative pose of the body segments is indeed observable under a very mild condition on the motion. We support our results by simulation studies, in which we employ a state estimator that neither uses magnetometer measurements nor additional sensors and does not impose assumptions on the accelerometer to measure only the direction of gravity, nor on the range of motion or degrees of freedom of the joints. We investigate the effect of the amount of excitation and of stationary periods in the data on the accuracy of the estimates. We then use experimental data from two mechanical joints as well as from a human gait experiment to validate the observability criterion in practice and to show that small excitation levels are sufficient for obtaining accurate estimates even in the presence of time periods during which the motion is not observable., Team Manon Kok

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using Laplace basis functions defined in a cubical domain, analytic expressions of the gradient of the learned magnetic field become available. An existing approach for magnetic field SLAM with reduced-rank GP regression uses a Rao-Blackwellized particle filter (RBPF). For each incoming measurement, training of the magnetic field map using an RBPF has a computational complexity per time step of O(NpN2m), where Np is the number of particles, and Nm is the number of basis functions used to approximate the Gaussian process. Contrary to the existing particle filter-based approach, we propose applying an extended Kalman filter based on the gradients of our learned magnetic field map for simultaneous localization and mapping. Our proposed algorithm only requires training a single map. It, therefore, has a computational complexity at each time step of O(N2m). We demonstrate the workings of the extended Kalman filter for magnetic field SLAM on an open-source data set from a foot-mounted sensor and magnetic field measurements collected onboard a model ship in an indoor pool. We observe that the drift compensating abilities of our algorithm are comparable to what has previously been demonstrated for magnetic field SLAM with an RBPF., Team Manon Kok, Offshore and Dredging Engineering

The last years have seen a growing body of literature on data-driven pedestrian inertial navigation. However, despite this, it is still unclear how to efficiently combine classical models and other a priori information with existing machine learning frameworks. In this paper, we first categorize existing approaches to data-driven pedestrian inertial navigation, including approaches where a machine learning algorithm is embedded into an overarching classical framework and purely data-driven frameworks. We then propose an estimation framework where navigation estimates obtained by classical means are fed to a machine learning algorithm which is trained to correct and improve the estimates. Further, we describe three symmetries that can be used to constrain the proposed estimation framework and thereby improve its performance. These are 1) the rotational symmetry of pedestrian dynamics, 2) the rotational symmetry of the sensors, and 3) the temporal symmetry of pedestrian dynamics. To demonstrate the usefulness of the proposed framework, we use data from foot-mounted inertial sensors utilizing zero-velocity updates under mixed walking and running. Machine learning corrections are implemented using both neural networks and Gaussian processes., Team Manon Kok

Multiway data often naturally occurs in a tensorial format which can be approximately represented by a low-rank tensor decomposition. This is useful because complexity can be significantly reduced and the treatment of large-scale data sets can be facilitated. In this paper, we find a low-rank representation for a given tensor by solving a Bayesian inference problem. This is achieved by dividing the overall inference problem into subproblems where we sequentially infer the posterior distribution of one tensor decomposition component at a time. This leads to a probabilistic interpretation of the well-known iterative algorithm alternating linear scheme (ALS). In this way, the consideration of measurement noise is enabled, as well as the incorporation of application-specific prior knowledge and the uncertainty quantification of the low-rank tensor estimate. To compute the low-rank tensor estimate from the posterior distributions of the tensor decomposition components, we present an algorithm that performs the unscented transform in tensor train format., Team Manon Kok, Team Kim Batselier

Risk-informed decision-making needs methods for assessing flood risk, and often the flooding probability and the consequences of floods (such as economic damage and fatalities) play an important role in the risk assessment. Also, measures to reduce the flood risk need to be formulated, in order to make a risk-informed decision, whether to accept the risk or to implement measures to reduce or to transfer the risk. These measures can be technical (for example, storm surge barriers) but can also be in the area of spatial planning or crises management. The outcome of such a process might be formalized in a “safety standards,” which can be used for the design of measures. These standards show remarkable differences between the United States, the United Kingdom, and the Netherlands., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Hydraulic Structures and Flood Risk

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

Trunk motor control is essential for the proper functioning of the upper extremities and is an important predictor of gait capacity in children with delayed development. Early diagnosis and intervention could increase the trunk motor capabilities in later life, but current tools used to assess the level of trunk motor control are largely subjective and many lack the sensitivity to accurately monitor development and the effects of therapy. Inertial measurement units could yield an objective quantitative assessment that is inexpensive and easy-to-implement. We hypothesized that root mean square of jerk, a proxy for movement smoothness, could be used to distinguish age and thereby presumed motor development. We attached a sensor to the trunks of six young children with no known developmental deficits. Root mean square of jerk decreases with age, up to 24 months, and is correlated to a more established method, i.e., center-of-pressure velocity, as well as other standard inertial measurement unit outputs. This metric therefore shows potential as a method to differentiate trunk motor control levels., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Biomechatronics & Human-Machine Control, Team Manon Kok, Biomechanical Engineering

In this paper, a simultaneous localization and mapping algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter to correct the pedestrian position and orientation estimates from the pedestrian dead-reckoning. The pedestrian dead-reckoning is computed using an extended Kalman filter with zero-velocity updates. The algorithm is validated using experimental sequences and the results show the efficacy of the algorithm in localizing pedestrians in indoor environments., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

In this work, our focus is on indoor localization using the indoor magnetic field as a source of position information. This relies on the fact that ferromagnetic materials inside buildings cause the magnetic field to vary spatially. We jointly estimate the pose of a combined sensor module (containing a magnetometer) as well as the magnetic field map. We show that our previously developed algorithm for magnetic field-based simultaneous localization and mapping can be adapted and extended into a general framework where a multitude of measurements can be included. We exemplify this using a foot-mounted inertial measurement unit where we additionally assume the availability of range measurements., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

Hydraulic Structures and Flood Risk

Dikes are an effective flood risk reduction measure in deltaic areas. Present risk analyses consist often of decoupled calculations of probabilities of dike failure and calculation of consequences of flooding given dike failure. However, the flood defense design determines not only the probability of failure, but influences the consequences of flooding as well. Especially when the dike has a ductile failure and breach growth behavior, due to a structural robust design, the consequences of flooding reduce. In this article, we present a novel assessment of risks and investments, valuing structural robustness of a construction type, represented by its ductile behavior during high loads. Therefore, the consecutive occurrence of initial dike failure mechanisms, failure path development, breach growth, and consequences is modeled integral and time-dependent. The investments consist of the costs to reinforce or reconstruct the flood defense to behave relatively ductile. This integral assessment enables to compare flood impacts of different construction types and different dimensions of designs. We applied it on a case in a riverine area in the Netherlands. The results show that the total societal costs and the individual risks on victims are very dependent on the construction type. The risk profile of a polder protected by a brittle or a ductile dike differs significantly. The brittle sand dike in the case requires larger dimensions than the more ductile dike with a clay core., Hydraulic Structures and Flood Risk

Flood prone areas are often protected against flooding by an extensive network of flood defenses. To ensure their structural integrity, these flood defenses are periodically assessed. Many levees have been functioning well for decades, and have survived several relatively high hydraulic loads within their lifetime. However, information on survived load conditions is seldom included in levee safety assessments. Observed degradation from levee inspections is also not taken into account. That way, information that is useful to improve the accuracy of estimations of the actual strength of the levee remains unexploited. This study proposes a pragmatic approach to include observations of survived loads and levee degradation in the levee safety assessment. This approach consists of three steps: (1) a prior estimation of the failure probability, based on levee characteristics, (2) a posterior estimation of the failure probability, based on observed hydraulic loads, and (3) correction of the posterior failure probability estimation, based on levee inspections. In a case study, the estimated failure probabilities using this approach were much lower than when information on levee performance was not included. This study demonstrates the value of levee performance observations and how they could be included to improve levee safety assessments., Coastal Engineering, Hydraulic Structures and Flood Risk

Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applications. In recent years, it has been shown that, by placing inertial sensors on segments that are connected by rotational joints, the motion of that kinematic chain can be tracked accurately. These methods specifically avoid using magnetometer measurements, which are known to be unreliable since the magnetic field at the different sensor locations is typically different. They rely on the assumption that the motion of the kinematic chain is sufficiently rich to assure observability of the relative pose. However, a formal investigation of this crucial requirement has not yet been presented, and no specific conditions for observability have so far been given. In this work, we present an observability analysis and show that the relative pose of the body segments is indeed observable under a very mild condition on the motion. We support our results by simulation studies, in which we employ a state estimator that neither uses magnetometer measurements nor additional sensors and does not impose assumptions on the accelerometer to measure only the direction of gravity, nor on the range of motion or degrees of freedom of the joints. We investigate the effect of the amount of excitation and of stationary periods in the data on the accuracy of the estimates. We then use experimental data from two mechanical joints as well as from a human gait experiment to validate the observability criterion in practice and to show that small excitation levels are sufficient for obtaining accurate estimates even in the presence of time periods during which the motion is not observable., Team Manon Kok

Multiway data often naturally occurs in a tensorial format which can be approximately represented by a low-rank tensor decomposition. This is useful because complexity can be significantly reduced and the treatment of large-scale data sets can be facilitated. In this paper, we find a low-rank representation for a given tensor by solving a Bayesian inference problem. This is achieved by dividing the overall inference problem into subproblems where we sequentially infer the posterior distribution of one tensor decomposition component at a time. This leads to a probabilistic interpretation of the well-known iterative algorithm alternating linear scheme (ALS). In this way, the consideration of measurement noise is enabled, as well as the incorporation of application-specific prior knowledge and the uncertainty quantification of the low-rank tensor estimate. To compute the low-rank tensor estimate from the posterior distributions of the tensor decomposition components, we present an algorithm that performs the unscented transform in tensor train format., Team Manon Kok, Team Kim Batselier

The Wettelijk Beoordelingsinstrumentarium (WBI) is the legal set of instruments for flood risk analysis in the Netherlands. Often, engineers have the impression that some failure probabilities of flood defenses resulting from these instruments are overestimated. In an effort to better estimate the failure probabilities of dikes along the Dutch river Rhine, this study sets out to assess them with experts and compare them to model results. We used Cooke's method for combining experts’ estimates in a structured way and follow two approaches to estimate a system failure probability. In the first approach, experts estimate discharges that lead to at least one dike failure. This gives plausible results; failure probabilities between 1/30 and 1/17.000 in a year. The second approach is based on adjusting existing model-based assessment results, by estimating the model-bias and incorporating additional dependencies. This mostly leads to large, implausible, failure probabilities: Experts tend to give more conservative answers as they are asked for detailed estimates without clear reference values. This results in large uncertainty and consequently (too) high failure probabilities. Our research shows that when applied in a clear frame of reference, structured expert judgments can be successfully used for estimating the reliability of Dutch flood defenses., Hydraulic Structures and Flood Risk

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using Laplace basis functions defined in a cubical domain, analytic expressions of the gradient of the learned magnetic field become available. An existing approach for magnetic field SLAM with reduced-rank GP regression uses a Rao-Blackwellized particle filter (RBPF). For each incoming measurement, training of the magnetic field map using an RBPF has a computational complexity per time step of O(NpN2m), where Np is the number of particles, and Nm is the number of basis functions used to approximate the Gaussian process. Contrary to the existing particle filter-based approach, we propose applying an extended Kalman filter based on the gradients of our learned magnetic field map for simultaneous localization and mapping. Our proposed algorithm only requires training a single map. It, therefore, has a computational complexity at each time step of O(N2m). We demonstrate the workings of the extended Kalman filter for magnetic field SLAM on an open-source data set from a foot-mounted sensor and magnetic field measurements collected onboard a model ship in an indoor pool. We observe that the drift compensating abilities of our algorithm are comparable to what has previously been demonstrated for magnetic field SLAM with an RBPF., Team Manon Kok, Offshore and Dredging Engineering

Hydraulic Structures and Flood Risk

The last years have seen a growing body of literature on data-driven pedestrian inertial navigation. However, despite this, it is still unclear how to efficiently combine classical models and other a priori information with existing machine learning frameworks. In this paper, we first categorize existing approaches to data-driven pedestrian inertial navigation, including approaches where a machine learning algorithm is embedded into an overarching classical framework and purely data-driven frameworks. We then propose an estimation framework where navigation estimates obtained by classical means are fed to a machine learning algorithm which is trained to correct and improve the estimates. Further, we describe three symmetries that can be used to constrain the proposed estimation framework and thereby improve its performance. These are 1) the rotational symmetry of pedestrian dynamics, 2) the rotational symmetry of the sensors, and 3) the temporal symmetry of pedestrian dynamics. To demonstrate the usefulness of the proposed framework, we use data from foot-mounted inertial sensors utilizing zero-velocity updates under mixed walking and running. Machine learning corrections are implemented using both neural networks and Gaussian processes., Team Manon Kok

Prioritisation of flood defence maintenance is typically based on visual inspection. However, literature shows that the Probability of Detection (PoD) of visual inspection can vary significantly. Here we investigate the PoD for visual inspections of flood defence structures, the consistency of damage classification, and the influence of different variables on the PoD, such as past experience. Four flood defence sections were inspected by 22 different inspectors for a variety of damage types, such as animal burrowing and damage to block revetments. It is found that the PoD varies significantly both per damage type and inspector. Additionally, the estimated severity of damages varies significantly in comparison to the reference situation: over half of the registered damages is assigned a different severity compared to the reference, which potentially leads to incorrect maintenance measures. A likely explanation for the variation in results is the complexity of inspection guidelines and task definitions. Therefore it is advised to simplify inspection guidelines and use more focussed inspections for the most important types of damage. This likely leads to both a reduction of the number of false negatives associated with an increase in flood risk, and better risk-based asset management and maintenance prioritisation in general., Hydraulic Structures and Flood Risk, Integral Design and Management

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7° root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situation, Biomechanical Engineering, Team Manon Kok

A major shortcoming in kinematic estimation using skin-attached inertial sensors is the alignment of sensor-embedded and segment-embedded coordinate systems. Only a correct alignment results in clinically relevant kinematics. Model-based inertial-sensor-to-bone alignment methods relate inertial sensor measurements with a model of the joint. Therefore, they do not rely on properly executed calibration movements or a correct sensor placement. However, it is unknown how accurate such model-based methods align the sensor axes and the underlying segment-embedded axes, as defined by clinical definitions. Also, validation of the alignment models is challenging, since an optical motion capture ground truth can be prone to disturbances from soft tissue movement, orientation estimation and manual palpation errors. We present an anatomical tibiofemoral ground truth on an unloaded cadaveric measurement set-up that intrinsically overcomes these disturbances. Additionally, we validate existing model-based alignment strategies. Modeling the degrees of freedom leads to the identification of rotation axes. However, there is no reason why these axes would align with the segment-embedded axes. Relative inertial-sensor orientation information and rich arbitrary movements showed to aid in identifying the underlying joint axes. The first dominant sagittal rotation axis aligned sufficiently well with the underlying segment-embedded reference. The estimated axes that relate to secondary kinematics tend to deviate from the underlying segment-embedded axes as much as their expected range of motion around the axes. In order to interpret the secondary kinematics, the alignment model should more closely match the biomechanics of the joint., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

We propose an application of magnetic field norm simultaneous localisation and mapping to measurements from a foot-mounted sensor for pedestrian navigation. The algorithm is, to the best of the authors’ knowledge, the first three dimensional drift-compensating indoor navigation method using only accelerometer, gyroscope and magnetometer measurements that does not rely on assumptions about the spatial structure of the indoor environment. We use a Rao-Blackwellized particle filter to simultaneously and recursively estimate the magnetic field norm map using reduced rank Gaussian process regression, and the position and orientation of the sensor. Our experiments demonstrate that our algorithm results in a drift-free position estimate using measurements collected from a foot-mounted sensor while walking around inside a hallway., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

We present a novel approach to estimate the relative sensor orientation from inertial sensors placed on connected body segments. Drift in the relative orientation estimates obtained by integrating the gyroscope measurements is corrected solely by incorporating common information in the inertial sensor measurements due to the connection of the body segments. We solve the estimation problem using a complementary filtering implementation to reduce the computational complexity. We study its robustness under common real-world model violations, e.g., soft tissue artifacts and spikes in the acceleration signals due to impacts. The efficacy of the method is illustrated with numerical simulations and is compared to a multiplicative extended Kalman filter implementation, both with and without outlier rejection. In addition, a human experiment strengthened the simulation results under realistic sensor errors., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Medical Instruments & Bio-Inspired Technology, Team Manon Kok

Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic actuators. A major challenge is the presence of magnetic disturbances that results in unreliable magnetometer readings. Recent research revealed that this problem can be overcome by exploitation of kinematic constraints. While typically each segment of the kinematic chain is equipped with an IMU, a novel approach called sparse inertial motion tracking aims at inferring the complete motion states from measurements of a reduced set of sensors. In the present contribution, we combine the magnetometer-free and the sparse approach for real-time motion tracking of double-hinge joint systems with non-parallel joint axes. Analyzing the observability of the system, we find a condition which assures that the relative orientations between all segments are uniquely determined by a kinematic constraint, which contains only the gyroscope readings. Furthermore, we propose a moving-horizon estimator and validate it in a simulation study of four movements, which differ by their degrees of excitation. The results of this study confirm the theoretical conjectures and demonstrate that magnetometer-free sparse inertial real-time motion tracking is feasible under precise and simple excitation conditions., Team Manon Kok

Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment., Team Manon Kok

In juli 2021 zijn grote delen van Limburg getroffen door hevige regenval en overstromingen. Ook delen van België en Duitsland overstroomden met zeer veel schade en verlies aan mensenlevens tot gevolg. Dit betrof een extreme en ongeëvenaarde gebeurtenis met enorme impact. Daarom is naar aanleiding van de overstromingen deze verkenning uitgevoerd om een eerste stap te maken om beschikbare informatie over deze gebeurtenis te verzamelen en analyseren. Het onderzoek is uitgevoerd door een breed consortium (TU Delft, Deltares, HKV Lijn in Water, VU Amsterdam, Universiteit Utrecht, KNMI, WUR, Erasmus MC en Universiteit Twente) in opdracht van het Expertise Netwerk Waterveiligheid (ENW). Een overstroming heeft effect op de hele maatschappij. Daarom zijn niet alleen hydrologische en civieltechnische onderwerpen beschouwd, maar ook de maatschappelijke gevolgen van overstromingen, de crisisrespons en de gezondheidseffecten. Contributors (in alphabetical order): Nathalie Asselman (Deltares), Hermjan Barneveld (HKV / Wageningen UR), Jules Beersma (KNMI), Eline Boelee (Deltares), Wouter Botzen (VU Amsterdam), Eefke Copper (TU Delft), Dim Coumou (KNMI), Karin de Bruijn (Deltares), Anniek de Jong (Deltares), Jurjen de Jong (Deltares), Hans de Moel (VU Amsterdam), Ferdinand Diermanse (Deltares), Astrid Fischer (Evides) , Gert-Jan Geerling (Deltares), Marie-Louise Geurts (WML), Rob Groenland (KNMI), Mark Hegnauer (Deltares), Bas Jonkman (TU Delft), Nicole Jungermann (KNMI), Frans Klijn (Deltares), Andre Koelewijn (Deltares), Matthijs Kok (HKV / TU Delft), Elco Koks (VU Amsterdam), Bas Kolen (HKV / TU Delft), Marion Koopmans (Erasmus MC), Laurens Leunge (Deltares), Hans Middelkoop (Utrecht University), Roelof Moll (TU Delft), Jaap Mos (Dunea), Sjoukje Philip (KNMI), Gerbert Pleijter (HKV), Joost Pol (HKV / TU Delft), Stephan Rikkert (TU Delft), Guus Rongen (TU Delft), Rinus Scheele (KNMI), Julius Schlumberger (TU Delft), Peter Siegmund (KNMI), Kymo Slager (Deltare, Hydraulic Structures and Flood Risk, Safety and Security Science, Coastal Engineering

In this paper the safety of a double-dike system (or twin dikes) is assessed. Such a system consist of two parallel lines of flood defences. During storms the combined strength of the parallel flood defences must prevent flooding of the hinterland. A culvert can be implemented for the tidal exchange of sea water to enable new land-uses in the area between the dikes such as aquaculture, saline agriculture, salt marsh restoration and clay extraction. We develop a general framework for assessing the safety of such double dike systems and apply a simplified version to the Double Dike between Eemshaven and Delfzijl (The Netherlands) to test this method. In doing so, we aim to quantify the flood protection benefits of parallel flood defenses and enable their use in multifunctional flood protection strategies. Within the framework the transmission of hydraulic loads by the seaward dike to the landward dike in the case-study was described by overtopping, overflow and erosion of the outer slope, alongside discharge through the culvert in the event of a non-closure. For the subset of coastal double dike systems with a tall seaward dike (as in the case-study), the results show only a negligible improvement in flood protection compared to a single dike system. With the addition of a culvert in the first dike, flood risk will only be reduced by the second landward dike if its height is sufficient to retain water in the event of a non-closure during common storm events. These double dike systems are implemented for potential uses of the inter-dike zone, e.g. for nature restoration, rather than as a measure to primarily improve flood protection., Hydraulic Structures and Flood Risk, Coastal Engineering

Backward erosion piping is an internal erosion process, which compromises the stability of water retaining structures such as dams and levees. In this paper, we propose a numerical solution that combines a 2D Darcy groundwater solution with Exner's 1D sediment transport mass conservation equation. As an estimate of sediment transport, we tested four different empirical transport equations for laminar flow. The model performance was evaluated based on the results of the real-scale IJkdijk experiment. Through this, we were able to demonstrate the applicability of existing sediment transport equations to the description of particle motion during piping erosion. The proposed transient piping model not only predicts the pipe progression in time, it also allows for an identification of pore pressure transitions due to the erosion process. A major conclusion of the study is that from the four different modeling approaches for laminar flow, it is recommended to follow the approach of Yalin et al. regarding the simulation of backward erosion piping for dike configurations similar to those of the IJkdijk experiment., Hydraulic Structures and Flood Risk, Water Resources

During construction of a dike, slope stability often reaches critical levels, due to the excess pore water pressures in the foundation. The loading condition during construction has similarities with the design conditions during flood loading. Not only in terms of the pore water pressures as the main driving force, but also in terms of criticality of the stability. This paper examines how the information of survival of the construction stage can be used to improve the reliability estimate for a dike in flood conditions, using Bayesian updating. The approach is exemplified for a range of typical dikes and for a case study of a full-scale test embankment. The main result is that the reliability can increase significantly by including the information of construction survival and the uncertainty reduction involved, especially for dikes on soft soil blankets. For the investigated cases, the posterior failure probability was up to several orders of magnitude lower than the prior failure probability. The main factors influencing the degree of reliability update, were the ground conditions and the degree of criticality of the slope stability during construction. In conclusion, using the information of the survived construction leads to improved reliability-based safety assessments of dikes, and consequently to more targeted and cost-effective flood protection., Hydraulic Structures and Flood Risk

A major shortcoming in kinematic estimation using skin-attached inertial sensors is the alignment of sensor-embedded and segment-embedded coordinate systems. Only a correct alignment results in clinically relevant kinematics. Model-based inertial-sensor-to-bone alignment methods relate inertial sensor measurements with a model of the joint. Therefore, they do not rely on properly executed calibration movements or a correct sensor placement. However, it is unknown how accurate such model-based methods align the sensor axes and the underlying segment-embedded axes, as defined by clinical definitions. Also, validation of the alignment models is challenging, since an optical motion capture ground truth can be prone to disturbances from soft tissue movement, orientation estimation and manual palpation errors. We present an anatomical tibiofemoral ground truth on an unloaded cadaveric measurement set-up that intrinsically overcomes these disturbances. Additionally, we validate existing model-based alignment strategies. Modeling the degrees of freedom leads to the identification of rotation axes. However, there is no reason why these axes would align with the segment-embedded axes. Relative inertial-sensor orientation information and rich arbitrary movements showed to aid in identifying the underlying joint axes. The first dominant sagittal rotation axis aligned sufficiently well with the underlying segment-embedded reference. The estimated axes that relate to secondary kinematics tend to deviate from the underlying segment-embedded axes as much as their expected range of motion around the axes. In order to interpret the secondary kinematics, the alignment model should more closely match the biomechanics of the joint., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

We present a novel approach to estimate the relative sensor orientation from inertial sensors placed on connected body segments. Drift in the relative orientation estimates obtained by integrating the gyroscope measurements is corrected solely by incorporating common information in the inertial sensor measurements due to the connection of the body segments. We solve the estimation problem using a complementary filtering implementation to reduce the computational complexity. We study its robustness under common real-world model violations, e.g., soft tissue artifacts and spikes in the acceleration signals due to impacts. The efficacy of the method is illustrated with numerical simulations and is compared to a multiplicative extended Kalman filter implementation, both with and without outlier rejection. In addition, a human experiment strengthened the simulation results under realistic sensor errors., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Medical Instruments & Bio-Inspired Technology, Team Manon Kok

We propose an application of magnetic field norm simultaneous localisation and mapping to measurements from a foot-mounted sensor for pedestrian navigation. The algorithm is, to the best of the authors’ knowledge, the first three dimensional drift-compensating indoor navigation method using only accelerometer, gyroscope and magnetometer measurements that does not rely on assumptions about the spatial structure of the indoor environment. We use a Rao-Blackwellized particle filter to simultaneously and recursively estimate the magnetic field norm map using reduced rank Gaussian process regression, and the position and orientation of the sensor. Our experiments demonstrate that our algorithm results in a drift-free position estimate using measurements collected from a foot-mounted sensor while walking around inside a hallway., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Manon Kok

Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic actuators. A major challenge is the presence of magnetic disturbances that results in unreliable magnetometer readings. Recent research revealed that this problem can be overcome by exploitation of kinematic constraints. While typically each segment of the kinematic chain is equipped with an IMU, a novel approach called sparse inertial motion tracking aims at inferring the complete motion states from measurements of a reduced set of sensors. In the present contribution, we combine the magnetometer-free and the sparse approach for real-time motion tracking of double-hinge joint systems with non-parallel joint axes. Analyzing the observability of the system, we find a condition which assures that the relative orientations between all segments are uniquely determined by a kinematic constraint, which contains only the gyroscope readings. Furthermore, we propose a moving-horizon estimator and validate it in a simulation study of four movements, which differ by their degrees of excitation. The results of this study confirm the theoretical conjectures and demonstrate that magnetometer-free sparse inertial real-time motion tracking is feasible under precise and simple excitation conditions., Team Manon Kok

Prioritisation of flood defence maintenance is typically based on visual inspection. However, literature shows that the Probability of Detection (PoD) of visual inspection can vary significantly. Here we investigate the PoD for visual inspections of flood defence structures, the consistency of damage classification, and the influence of different variables on the PoD, such as past experience. Four flood defence sections were inspected by 22 different inspectors for a variety of damage types, such as animal burrowing and damage to block revetments. It is found that the PoD varies significantly both per damage type and inspector. Additionally, the estimated severity of damages varies significantly in comparison to the reference situation: over half of the registered damages is assigned a different severity compared to the reference, which potentially leads to incorrect maintenance measures. A likely explanation for the variation in results is the complexity of inspection guidelines and task definitions. Therefore it is advised to simplify inspection guidelines and use more focussed inspections for the most important types of damage. This likely leads to both a reduction of the number of false negatives associated with an increase in flood risk, and better risk-based asset management and maintenance prioritisation in general., Hydraulic Structures and Flood Risk, Integral Design & Management

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7° root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situation, Biomechanical Engineering, Team Manon Kok

In juli 2021 zijn grote delen van Limburg getroffen door hevige regenval en overstromingen. Ook delen van België en Duitsland overstroomden met zeer veel schade en verlies aan mensenlevens tot gevolg. Dit betrof een extreme en ongeëvenaarde gebeurtenis met enorme impact. Daarom is naar aanleiding van de overstromingen deze verkenning uitgevoerd om een eerste stap te maken om beschikbare informatie over deze gebeurtenis te verzamelen en analyseren. Het onderzoek is uitgevoerd door een breed consortium (TU Delft, Deltares, HKV Lijn in Water, VU Amsterdam, Universiteit Utrecht, KNMI, WUR, Erasmus MC en Universiteit Twente) in opdracht van het Expertise Netwerk Waterveiligheid (ENW). Een overstroming heeft effect op de hele maatschappij. Daarom zijn niet alleen hydrologische en civieltechnische onderwerpen beschouwd, maar ook de maatschappelijke gevolgen van overstromingen, de crisisrespons en de gezondheidseffecten. Contributors (in alphabetical order): Nathalie Asselman (Deltares), Hermjan Barneveld (HKV / Wageningen UR), Jules Beersma (KNMI), Eline Boelee (Deltares), Wouter Botzen (VU Amsterdam), Eefke Copper (TU Delft), Dim Coumou (KNMI), Karin de Bruijn (Deltares), Anniek de Jong (Deltares), Jurjen de Jong (Deltares), Hans de Moel (VU Amsterdam), Ferdinand Diermanse (Deltares), Astrid Fischer (Evides) , Gert-Jan Geerling (Deltares), Marie-Louise Geurts (WML), Rob Groenland (KNMI), Mark Hegnauer (Deltares), Bas Jonkman (TU Delft), Nicole Jungermann (KNMI), Frans Klijn (Deltares), Andre Koelewijn (Deltares), Matthijs Kok (HKV / TU Delft), Elco Koks (VU Amsterdam), Bas Kolen (HKV / TU Delft), Marion Koopmans (Erasmus MC), Laurens Leunge (Deltares), Hans Middelkoop (Utrecht University), Roelof Moll (TU Delft), Jaap Mos (Dunea), Sjoukje Philip (KNMI), Gerbert Pleijter (HKV), Joost Pol (HKV / TU Delft), Stephan Rikkert (TU Delft), Guus Rongen (TU Delft), Rinus Scheele (KNMI), Julius Schlumberger (TU Delft), Peter Siegmund (KNMI), Kymo Slager (Deltare, Hydraulic Structures and Flood Risk, Safety and Security Science, Coastal Engineering