A Smart wearable motion sensor and acoustic signal processing based on vocabulary for monitoring children's wellbeing using Big Data

Abstract

The brain is the most complex organ in the human body, and it is also the most complex organ in the whole biological system, making it the most complex organ on the planet. According to the findings of current studies, modern study that properly characterises the EEG data signal provides a clear classification accuracy of human activities that is distinct from previous research. Various brain wave patterns related with common activities such as sleeping, reading, and watching a movie may be found in the Electroencephalography (EEG) data that has been collected. In response to these activities, we accumulate various sorts of emotion signals in our brain, such as the Delta, Theta, and Alpha bands, which will provide different types of emotion signals in our brain as a consequence of our actions. When dealing with EEG recordings that are non-stationary in nature, time-frequency domain techniques, on the other hand, are more likely to provide good results. The ability to detect diverse neural rhythm scales using time-frequency representation has also been shown to be a legitimate EEG marker; this ability has also been demonstrated to be a powerful tool for investigating small-scale neural brain oscillations. On the basis of several parameters such as filtering response, precision, recall, and F-measure, as well as accuracy and precision, the Matlab simulation software was used to evaluate the performance of the proposed system.