Abstract
In order to improve the accuracy of the evaluation results of multiperception intelligent wearable devices, the mathematical statistical characteristics based on speech, behavior, environment, and physical signs are proposed; first, the PCA feature compression algorithm was used to reduce the dimension of these features, and the differences among different training samples were compared and analyzed; then, three weak classifiers are designed using the logistic regression algorithm, and finally, a strong classifier with higher prediction accuracy is designed according to the boosting decision fusion method and ensemble learning idea. The results showed that the accuracy of the logistic regression model trained with the feature data of voice PCA was 0.964, but the recall rate and crossover results were significantly reduced to 0.844 and 0.846, respectively. The accuracy, accuracy and recall of the decision fusion model based on the boosting method and integrated learning are 0.969, and the prediction accuracy of K-folds cross-validation is also as high as 0.956; the superposition fusion results of three weak classifiers achieve a better classification effect.
Highlights
A person’s emotions can be objectively reflected through information such as language, sound, behavior, and physical signs, while a person’s mental health is often related to his or her long-term emotions; in particular, speech signals containing various speech features can be used as an important objective evaluation standard for personal emotional expression [1]
A mental health device based on a wearable device can objectively monitor a person’s mental activity; when the mental activities of the subjects fluctuate for a long time, the subjects should be timely reminded to conduct professional mental health diagnosis and rehabilitation treatment [4]. e pressure of study, life, and employment of contemporary college students is becoming increasingly significant, and it is easy to produce a variety of negative emotions; this can lead to various mental health problems and mental diseases, such as depression, anxiety, and autism [1]
Many college students with mental illness often do not take the initiative to seek help and consult professional psychological tutors or doctors; this makes the incidence of psychological disorders and diseases among college students high at about 30%, in order to reduce the Contrast Media & Molecular Imaging incidence of psychological disorders or diseases among college students; it is of great significance to objectively monitor the psychological activities of college students and to seek a method to objectively monitor their mental health for a long time; in addition, this method can timely remind the patient for further treatment when it is found that the tested person has weak signs of psychological disorder [5]
Summary
A person’s emotions can be objectively reflected through information such as language, sound, behavior, and physical signs, while a person’s mental health is often related to his or her long-term emotions; in particular, speech signals containing various speech features can be used as an important objective evaluation standard for personal emotional expression [1]. Many college students with mental illness often do not take the initiative to seek help and consult professional psychological tutors or doctors; this makes the incidence of psychological disorders and diseases among college students high at about 30%, in order to reduce the Contrast Media & Molecular Imaging incidence of psychological disorders or diseases among college students; it is of great significance to objectively monitor the psychological activities of college students and to seek a method to objectively monitor their mental health for a long time; in addition, this method can timely remind the patient for further treatment when it is found that the tested person has weak signs of psychological disorder [5]. A strong classifier with higher prediction accuracy is designed according to the boosting decision fusion method and ensemble learning idea. e results showed that the accuracy of the logistic regression model trained with the feature data of voice PCA was 0.964, but the recall rate and crossover results were significantly reduced to 0.844 and 0.846, respectively. e accuracy, accuracy and recall of the decision fusion model based on the boosting method and ensemble learning were 0.969, and the prediction accuracy of K-folds cross-validation was as high as 0.956, which enabled the superposition fusion results of the three weak classifiers to achieve a better classification effect
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