Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.
Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.
Personal data is increasingly used by cities to track the behavior of their inhabitants. While the data is often used to mainly provide information to the authorities, it can also be harnessed for providing information to the citizens in real-time. In an on-going research project on increasing the awareness of motorists w.r.t. the environmental consequences of their driving behavior, we make use of sensors, artificial intelligence, and real-time feedback to design an intervention. A key component for successful deployment of the system is data related to the personal driving behavior of individual motorists. Through this outset, we identify challenges and research questions that relate to the use of personal data in systems, which are designed to increase the quality of life of the inhabitants of the built environment.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
‘Dieren in de dijk’ aims to address the issue of animal burrows in earthen levees, which compromise the integrity of flood protection systems in low-lying areas. Earthen levees attract animals that dig tunnels and cause damages, yet there is limited scientific knowledge on the extent of the problem and effective approaches to mitigate the risk. Recent experimental research has demonstrated the severe impact of animal burrows on levee safety, raising concerns among levee management authorities. The consortium's ambition is to provide levee managers with validated action perspectives for managing animal burrows, transitioning from a reactive to a proactive risk-based management approach. The objectives of the project include improving failure probability estimation in levee sections with animal burrows and enhancing risk mitigation capacity. This involves understanding animal behavior and failure processes, reviewing existing and testing new deterrence, detection, and monitoring approaches, and offering action perspectives for levee managers. Results will be integrated into an open-access wiki-platform for guidance of professionals and in education of the next generation. The project's methodology involves focus groups to review the state-of-the-art and set the scene for subsequent steps, fact-finding fieldwork to develop and evaluate risk reduction measures, modeling failure processes, and processing diverse quantitative and qualitative data. Progress workshops and collaboration with stakeholders will ensure relevant and supported solutions. By addressing the knowledge gaps and providing practical guidance, the project aims to enable levee managers to effectively manage animal burrows in levees, both during routine maintenance and high-water emergencies. With the increasing frequency of high river discharges and storm surges due to climate change, early detection and repair of animal burrows become even more crucial. The project's outcomes will contribute to a long-term vision of proactive risk-based management for levees, safeguarding the Netherlands and Belgium against flood risks.
Road freight transport contributes to 75% of the global logistics CO2 emissions. Various European initiatives are calling for a drastic cut-down of CO2 emissions in this sector [1]. This requires advanced and very expensive technological innovations; i.e. re-design of vehicle units, hybridization of powertrains and autonomous vehicle technology. One particular innovation that aims to solve this problem is multi-articulated vehicles (road-trains). They have a smaller footprint and better efficiency of transport than traditional transport vehicles like trucks. In line with the missions for Energy Transition and Sustainability [2], road-trains can have zero-emission powertrains leading to clean and sustainable urban mobility of people and goods. However, multiple articulations in a vehicle pose a problem of reversing the vehicle. Since it is extremely difficult to predict the sideways movement of the vehicle combination while reversing, no driver can master this process. This is also the problem faced by the drivers of TRENS Solar Train’s vehicle, which is a multi-articulated modular electric road vehicle. It can be used for transporting cargo as well as passengers in tight environments, making it suitable for operation in urban areas. This project aims to develop a reverse assist system to help drivers reverse multi-articulated vehicles like the TRENS Solar Train, enabling them to maneuver backward when the need arises in its operations, safely and predictably. This will subsequently provide multi-articulated vehicle users with a sustainable and economically viable option for the transport of cargo and passengers with unrestricted maneuverability resulting in better application and adding to the innovation in sustainable road transport.
