Worker’s physical fatigue classification using neural networks

Physical fatigue is not only an indication of the user’s physical condition and/or need for sleep or rest, but can also be a significant symptom of various diseases. This fatigue affects the performance of workers in jobs that involve some continuous physical activity, and is the cause of a large proportion of accidents at work. The physical fatigue is commonly measured by the perceived exertion (RPE). Many previous studies have attempted to continuously monitor workers in order to detect the level of fatigue and prevent these accidents, but most have used invasive sensors that are difficult to place and prevent the worker from performing their tasks correctly. Other works use activity measurement sensors such as accelerometers, but the large amount of information obtained is difficult to analyse in order to extract the characteristics of each fatigue state. In this work, we use a dataset that contains data from inertial sensors of several workers performing various activities during their working day, labelled every 10 min based on their level of fatigue using questionnaires and the Borg fatigue scale. Applying Machine Learning techniques, we design, develop and test a system based on a neural network capable of classifying the variation of fatigue caused by the physical activity collected every 10 min; for this purpose, a feature extraction is performed after the time decomposition done with the Discrete Wavelet Transform (DWT). The results show that the proposed system has an accuracy higher than 92% for all the cases, being viable for its application in the proposed scenario.European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Consejería de Economía, Conocimiento, Empresas y Universidad (Junta de Andalucía) US-126371

Dynamics of Neospora caninum-Associated Abortions in a Dairy Sheep Flock and Results of a Test-and-Cull Control Programme

[EN] Neospora caninum is an apicomplexan parasite that can cause abortions and perinatal mortality in sheep. Although ovine neosporosis has been described worldwide, there is a lack of information about the relationship between N. caninum serostatus and the reproductive performance. In this study, we described the infection dynamics in a dairy sheep flock with an abortion rate up to 25% and a N. caninum seroprevalence of 32%. Abortions were recorded in 36% and 9% of seropositive and seronegative sheep, respectively. Seropositive sheep were more likely to abort twice (OR = 4.44) or three or more times (OR = 10.13) than seronegative sheep. Endogenous transplacental transmission was the main route of transmission since 86% of seropositive sheep had seropositive offspring. Within dams that had any abortion, seropositive sheep were more likely than seronegative ones to have female descendants that aborted (OR = 8.12). The slight increase in seropositivity with the age, the low percentage of animals with postnatal seroconversion or with low avidity antibodies, and the seropositivity of one flock dog, indicated that horizontal transmission might have some relevance in this flock. A control programme based on selective culling of seropositive sheep and replacement with seronegative animals was effective in reducing the abortion rate to 7.2%SIThis study was funded by the Spanish Ministry of Science and Innovation (PID2019- 104713RB-C21). R.S.-S. was funded by the Community of Madrid, Spain (PLATESA2, S2018/BAA- 4370) and M.F.-E. by UCM-Santander/2017 predoctoral grants. L.M. Ortega-Mora and G. Alvarez- Garcia are part of the TOXOSOURCES consortium, supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme

Generación de Mallas y Simulación Numérica en Medioambiente

[ES]En esta conferencia se introducen ideas básicas sobre la simulación numérica, mediante el método de los elementos finitos (MEF), de problemas medioambientales que han sido abordados por nuestro grupo en diversos proyectos de investigación sobre simulación de campos de viento, radiación solar, contaminación atmosférica y la modelización de yacimientos de petróleo