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Geomagnetic indices quantify the disturbance caused by the solar activity in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at mid-latitude with a 1-minute resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNN). In this work, we present two DNNs developed to forecast the SYM-H and ASY-H indices. Both networks have been trained using solar wind data from the last two solar cycles and they are able to accurately forecast the indices two hours in advance, considering the solar wind and indices values for the previous 16 hours. The evaluation of both networks reveals a great precision for the forecasting, including good predictions for large storms that occurred during the solar cycle 23.

Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at midlatitude with a 1-min resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM-H and ASY-H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013-2020., Junta de Comunidades de Castilla-La Mancha, Ministerio de Ciencia e Innovación, Ministerio de Economía y Competitividad

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices., Junta de Comunidades de Castilla-La Mancha, Comunidad de Madrid, Agencia Estatal de Investigación

Falls are the predominant cause of injury for older people. How to detect them is being in the last decade the focus of attention of many projects and researchers. This paper presents a simulator for recreating triaxial accelerometer measures of people falling in two circumstances: as a consequence of a loss of conscience or due to bumping into an obstacle. The objective of the simulator is to generate falling instances to train Machine Learning algorithms that can be incorporated into wearable devices. The developed simulator can generate triaxial accelerometer measures that exhibit similar patterns compared to falls recorded with real people and mannequins using a commercial device.

One of the most notable consequences of aging is the loss of motor function abilities, making elderly people specially susceptible to falls, which is of the most remarkable concerns in elder care. Thus, several solutions have been proposed to detect falls, however, none of them achieved a great success mainly because of the need of wearing a recording device. In this paper, we study the use of sound to detect fall events. The advantage of this approach over the traditional ones is that the subject does not require to wear additional devices to monitor his or her activities. Here, we apply machine learning techniques to process sound simulated the most common type of fall for the elderly, i.e., when the foot collides with an obstacle and the trunk hits the ground before using his/her hands to absorb the fall. The results show that high levels of accuracy can be achieved using only a few signal processing techniques.

The loss of motor function in the elderly makes this population group prone to accidental falls. Actually, falls are one of the most notable concerns in elder care. Not surprisingly, there are several technical solutions to detect falls, however, none of them has achieved great acceptance. The popularization of smartwatches provides a promising tool to address this problem. In this work, we present a solution that applies machine learning techniques to process the output of a smartwatch accelerometer, being able to detect a fall event with high accuracy. To this end, we simulated the two most common types of falls in elders, gathering acceleration data from the wrist, then applied that data to train two classifiers. The results show high accuracy and robust classifiers able to detect falls.