Огляд Сучасних Підходів До Визначення Фізичної Втоми За Допомогою Моделей Штучного Інтелекту На Основі Аналізу Серцевих Сигналів

Abstract

794variability, spectral characteristics of the signal, and other parameters can serve as key indicators of physical condition.Further progress can be achieved using recurrent neural networks (RNNs). These networks are specialized for processing sequential data, such as time series, which cardiac signals are. Thus, RNNs consider temporal dependencies between sequential ECG measurements, providing a more accurate analysis.Such an approach makes real-time detection and prediction of fatigue levels possible. This paves the way for new practical applications, such as automated shift scheduling, notifying employees of the need for rest, or alerting about a potentially dangerous fatigue state.In conclusion, employing AI for the analysis and determination of physical fatigue unveils new paths towards enhancing workplace safety and efficiency. Machine and deep learning technologies have the potential to fundamentally change the way we understand and respond to the physical state of individuals.