Dynamic simulation of human motion

In order to study the mechanisms behind the human motion and its disorders, and to comprehend how do neuromuscular impairments affect the human movements, it is crucial to know the human musculoskeletal system. This knowledge is also essential to effectively model and analyse the musculoskeletal system, and to classify the human movement. With this work we conducted preliminary studies over a musculoskeletal model in motion, and performed a comparative analysis in gait between two models: one with a non-pathological gait and another with a disordered gait

Wireless sensor network simulation for fault detection in industrial processes

Sensor data is extremely important to monitor machines at the shop-floor level and its environmental surrounding conditions for condition-based monitoring, machine diagnosis and process adaptation to new requirements. Based on the described scope, self-diagnostics and self-organizing capabilities are core functionalities of any Industrial Wireless Sensor Network (IWSN). In the present work, a simulated case study was developed with the main intent of validating techniques implemented for sensor data diagnosis of error detection and equipment failure. The scenarios explored try to mimic some common situations of a manufacturing environment when dealing with WSNs, where a piece of sensor equipment suddenly stops working or an unpredictable change in the environment leads to faulty data readings. This paper introduces Castalia and describes how it was used to simulate a direct application of an Optical Metrology System on an industrial Resistance Spot Welding process, which is composed of a camera and several luminosity sensors. More specifically, a sensor data validation module was proposed, implemented and used to extend Castalia functionalities

“Want to come play with me?” Outlier subgroup discovery on spatio-temporal interactions

Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatiotemporal data from the real-time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio-temporal interaction data, focusing on two areas: exceptional subgroups and spatiotemporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease