Psychological Stress Detection and Early Warning System Based on Wireless Network Transmission

Li, Yaling

doi:10.1155/2021/3739045

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…At present, accurate measurement of psychological stress is the basis of evaluating mental health, among which there are three representative measurement methods: the self-evaluation method, behavioral measurement method, and physiological measurement method [ 4 ]. The self-evaluation method mainly uses the patient to fill in the self-evaluation scale to measure the source of psychological pressure and the state of mental stress.…”

Section: Introductionmentioning

confidence: 99%

Model Construction of Using Physiological Signals to Detect Mental Health Status

Liu

2021

Journal of Healthcare Engineering

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Background. Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health. Objectives. To design a mental health state detection model based on physiological signals to detect human mental health. Methods. For the detection of mental health, the sliding window method is used to divide the physiological signal dataset and the corresponding time into several segments and then calculate the physiological signal data in the sliding window for each physiological signal to form a sequence of characteristic values; according to the heart rate variability of the physiological signal, the heart rate variability (HRV) is extracted from the interval spectrum waveform: through the discrete trend analysis in statistics, the change characteristics of the ECG signal are analyzed, and the sequence statistical indicators of the physiological signal are calculated. With the help of a support vector machine used for the significant accuracy with less computation power, the physiological signals of the mental state are classified, and the discriminant function of the mental health state signals is normalized. A mental health state detection model is constructed according to the index system, the optimal solution of the model is obtained through the optimization function, and the mental health state detection is completed. Result. The detection error of the proposed model is less which improves the detection accuracy and is less time consuming. Conclusion. The detection model using physiological signals is proposed to evaluate the mental health status. As compared to the other detection models, its detection time is short and method error is always less than 2% which shows its accuracy and effectiveness.

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Section: Introductionmentioning

confidence: 99%

Model Construction of Using Physiological Signals to Detect Mental Health Status

Liu

2021

Journal of Healthcare Engineering

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“…The main purpose of Liu R's analysis of the fusion of network media and secondary English was to better play the function of secondary English. He discovered that using resource integration technology to manage distributed teaching resources helped colleges and universities improve their level of informatization [ 5 ]. Bai and Li designed a platform for statistical analysis of educational data and studied the storage of educational multidimensional data models in order to meet the rapid growth of educational data and improve operational efficiency and scientific decision-making.…”

Section: Related Workmentioning

confidence: 99%

Sharing of Teaching Resources for English Majors Based on Ubiquitous Learning Resource Sharing Platform and Neural Network

Zhang

2022

Computational Intelligence and Neuroscience

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Teachers in traditional English education devote a great deal of time and effort to developing excellent English teaching courseware and other teaching materials. However, because the network’s powerful sharing capability was not fully utilized, poor resource sharing hampered the school’s overall teaching efficiency. The goal of this paper is to investigate how to use a ubiquitous learning resource sharing platform and a neural network to analyze the method of English major education resource sharing. Because the BP neural network algorithm has better classification and prediction functions, it was proposed because it can classify and predict information in the sharing of teaching resources for English majors. This allows for seamless sharing of teaching materials. According to the findings of this study, there were 562.13 million people learning English in China in 2015, accounting for 37% of the population. By 2018, 945.61 million people were learning English, accounting for 85 percent of the population. Almost every student is learning English, demonstrating the language’s importance. As a result, the importance of English instruction is clear, and the sharing of teaching resources should be prioritized.

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“…erefore, the interval between two impulses in the detection cepstrum is the pitch period. e reciprocal of the pitch period is the pitch frequency [18].…”

Section: Fundamental Frequency and Jittermentioning

confidence: 99%

Application of Virtual Reality Technology in the Recognition System for Overcoming Anxiety and Psychological Pressure of Family Elderly

Gao

2022

Mobile Information Systems

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When the elderly enter the old age, their physical functions gradually decline; their physical strength, vision, and hearing are weakened; and their self-care ability gradually decreases with the increase of age. At the same time, it is accompanied by different degrees of anxiety and psychological problems. The rapid development of virtual reality technology in recent years provides a way to solve this problem, and high-performance computer and processing technology are the keys to directly affect the performance of the system. The existing research is more about the material and spiritual life of the elderly, but the research on their psychological status is relatively insufficient. From the perspective of mental health, this study makes a comprehensive and objective empirical study on the psychological status of family elderly. Based on virtual reality (VR), by extracting heart rate variability (HRV) features from the elderly, this paper establishes a model to identify the psychological pressure of family elderly, which solves the problem that the elderly have adverse effects on their body, mind, and life due to long-term psychological pressure. The paper analyzes the accuracy of KNN, CNN, and RFC models in identifying three psychological stress states: relaxation and low-pressure state, relaxation and medium-pressure state, and relaxation and high-pressure state. When the pressure is higher, the accuracy of the three models will be better than that of the lower pressure state. Under high pressure, the accuracy of CNN and RFC is close, but RFC is slightly better than CNN, and the accuracy of RFC model is as high as 95.02%.

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Psychological Stress Detection and Early Warning System Based on Wireless Network Transmission

Cited by 11 publications

References 14 publications

Model Construction of Using Physiological Signals to Detect Mental Health Status

Model Construction of Using Physiological Signals to Detect Mental Health Status

Sharing of Teaching Resources for English Majors Based on Ubiquitous Learning Resource Sharing Platform and Neural Network

Application of Virtual Reality Technology in the Recognition System for Overcoming Anxiety and Psychological Pressure of Family Elderly

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