RESUMEN
Several parameters affect our brain's neuronal system and can be identified by analyzing electroencephalogram (EEG) signals. One of the parameters is alcoholism, which affects the pattern of our EEG signals. By analyzing these EEG signals, one can derive information regarding the alcoholic or normal stage of an individual. Many road accident cases around the world, including drinking and driving scenarios, which result in loss of life, have been reported. Another reason for such incidents is that riders avoid wearing helmets while driving two-wheelers. Many road accident cases involving two-wheelers, including drinking, driving, overspeeding, and nonwearing helmets, have been reported. Therefore, to solve such issues, the present work highlights the features of an intelligent model that can predict the alcoholism level of the subject, wearing of a helmet, vehicle speed, location, etc. The system is designed with the latest technologies and is smart enough to make decisions. The system is based on multilayer perceptron, histogram of oriented gradients (HoG) feature extraction, and random forest to make decisions in real time. The accuracy of the proposed method is approximately 95%, which will reduce the fatality rate due to road accidents. The system is tested under different working environments, i.e., indoor and outdoor, and satisfactory outcomes are observed.
Asunto(s)
Accidentes de Tránsito , Conducción de Automóvil , Electroencefalografía , Internet de las Cosas , Humanos , Accidentes de Tránsito/prevención & control , Electroencefalografía/métodos , Masculino , Adulto , Dispositivos de Protección de la Cabeza , Alcoholismo , FemeninoRESUMEN
Energy is associated with anything and everything around us and can be transferred, transformed but cannot be destroyed. Many existing theories in physics like quantum physics, metaphysics, and electromagnetism give rise to the thought for the existence of an invisible field of bio-energy, in living things. Every living being, at its atomic level, absorbs and releases a good amount of energy, which is not visible through normal eyes but observable to measure through other means. The mentioned energy layer is known as Human Bio-field. Additionally, various studies also clear that measures of such energies can give deeper insights of our wellbeing and health. It also reflect thoughts, emotions, and inter-physiologic, which may affect the functioning of the human body. This article shows the results of the proposed algorithm for the visualisation of human bio-field. Further, the performance of the proposed work is evaluated in terms of accuracy by using existing methods.