Abstract
A multifeature fusion-based enterprise employee psychological stress prediction algorithm is suggested to address the concerns of low prediction accuracy, long duration, and poor results in current psychological stress prediction approaches. Examine ECG signal generation and properties, as well as the notion and causes of heart rate variability. The ECG signal is gathered according to the psychological stress reaction mechanism, and the digital filter is utilized to filter and preprocess the noise interference of the ECG signal. The linear discriminant analysis algorithm extracts the time domain linear features, frequency domain linear features, and nonlinear features of the ECG signal and then selects the ECG signal characteristics. D-S evidence theory is used to fuse the time domain linear characteristics, frequency domain linear characteristics, and nonlinear characteristics of the ECG signal, construct the psychological stress prediction model, obtain the final result of psychological stress state prediction, and realize the psychological stress prediction of enterprise employees, all based on multifeature fusion technology. The results of the experiments reveal that the suggested algorithm has a greater predictive effect on employee psychological stress, allowing it to enhance forecast accuracy and reduce prediction time.
Highlights
When people perceive or understand that they are confronted with a significant and demanding environmental necessity, they experience psychological stress, which manifests itself in a variety of psychological and bodily responses [1]
Because of the fast-paced nature of modern life, business employees are subjected to a variety of life and work pressures; there will be a high number of patients suffering from mental and psychological disorders. e precise forecast of psychological stress is critical for the continued development of businesses and the health of personnel
Analyze the observed physiological indicators and develop new quantified input features for physical activity and acute psychological stress using wristbands that are suitable for free-living mobile personnel
Summary
When people perceive or understand that they are confronted with a significant and demanding environmental necessity, they experience psychological stress, which manifests itself in a variety of psychological and bodily responses [1]. E precise forecast of psychological stress is critical for the continued development of businesses and the health of personnel. Analyze the observed physiological indicators and develop new quantified input features for physical activity and acute psychological stress using wristbands that are suitable for free-living mobile personnel. Reference [5] proposed an acute psychological stress prediction system based on mobile context perception, which can predict the stress state of users according to their. An adaptable and personalized prediction model is produced using a machine learning algorithm and cloud computing service based on the user context collected from their smartphone. In order to address the aforementioned issues, a multifeature fusion-based method for forecasting employee psychological stress is developed. E method uses multifeature fusion technology and D-S evidence theory, fuses numerous characteristics of ECG signals, builds a psychological stress prediction model, obtains psychological stress state prediction results, and implements psychological stress prediction for enterprise personnel. is method has a good prediction effect, and it can significantly enhance forecast accuracy and reduce prediction time
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