# Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices

Authors: Xichao Dai; Yumei Ding
Journal: Contrast Media & Molecular Imaging
 (2021)

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## 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.


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## Body

## 1. Introduction

A person’s emotions can easily 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]. Wearables can monitor mental health through changes in voice, behavior, environment, and physical signs, and some researchers have proved an effective way to monitor an individual’s mental health by objectively projecting small changes in mental activity over time. At present, the mainstream method of mental health assessment is still in the form of questionnaire or direct consultation with authoritative psychological counseling doctors [2]. In these mainstream methods, the biggest problem is that patients with mental illness have a great subjectivity in the process of participation [3]. It is very difficult for medical staff to do preventive treatment for patients with mental illness in advance, that is, when mental illness has just occurred or is about to occur, remind patients to go to professional psychological treatment institutions timely diagnosis and treatment. But 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]. The 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 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]. In view of this research problem, Huang and Wang proposed that pervasive computing is a kind of computing that emphasizes the integration with the environment, so that people do not feel the existence of computing devices. On this premise, people can acquire and process information no matter what environment they are in [6]. Mario et al. proposed that situation awareness is actually a comprehensive analysis of multidimensional sensor information collected by sensor technology, and the user’s environment is carefully “guessed” so as to help the user to complete daily work more conveniently [7]. Jiang et al. found that noninvasive behavioral tests, such as speech features and motor states, were associated with depression, and these features can be used to classify emotional states and track the effects of depression treatment over time [8]. On the basis of current research, this study proposes mathematical statistical features based on speech, behavior, environment, and physical signs. First, the PCA feature compression algorithm is used to reduce the dimension of these features, and the differences between different training samples are compared and analyzed. Then, three weak classifiers are designed using the logistic regression algorithm. 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 ensemble learning were 0.969, and the prediction accuracy of K-folds cross-validation was also as high as 0.956, which enabled the superposition fusion results of the three weak classifiers to achieve a better classification effect.


## 2. Methods

### 2.1. Application of Wearable Devices for Mental Health Monitoring

Individual’s mental health state can be naturally reflected through language expression, behavior, living environment, physiological indicators, and so on. With the progress of science and the further exploration of natural science by scientists, a large number of researchers have applied wearable devices to behavioral research, speech detection and recognition, health supervision, environmental measurement, and other fields. Based on the application research of existing intelligent wearable devices, this study tries to apply intelligent wearable devices to the field of mental health monitoring. Sensor data such as speech, environment, behavior, physical signs, and electrochemistry can objectively reflect the use of language, comfort of the environment, physical activity, and physical health of the wearer during the day. If these long-term macro and small variables are explored further, they may provide a true and objective reflection of the wearer’s mental health. If these sensors are embedded in a wearable device, they can monitor the wearer’s mental health in real time over a long period of time.


### 2.2. Low-Frequency Data Preprocessing

According to relevant theories of behavioral recognition, before feature extraction of behavioral data, sliding window must be used to reduce the accelerometer noise signal and external interference of the gyroscope when the wrist moves naturally [9]. The principle of a sliding window is similar to that of a low-pass filter, which is mainly related to the window size 1 and the sliding step size s. If the sampling frequency of behavior, environment, physical signs, and other low-frequency sensors is fHz, then the size of a window is usually set to 2f, and the move step s is usually set to f. However, in practical application, the window size should be an exponential power of 2 to ensure the smooth calculation of the FAST Fourier transform, as shown in the following formula:(1)l=2ceillog22∗f.This preconditioning method is specially designed for long-term experiments because in long-term mental health experiments, it is found that the subjects do not keep moving or active all the time; instead, they spend most of their time in a relatively static state, such as sitting, learning, and self-study, which increases the difficulty of feature extraction, and no matter what method is used to extract the eigenvalue, it will reflect the static state [10]. In order to offset the influence of the stationary state in the long time experiment, the pretreatment method of removing bases was proposed, that is, a set of sensor data in a completely static and quiet environment is first collected, and the reference values of various features are calculated; after subtracting this reference value from the long-term experimental data, the subsequent feature extraction is performed, and it has been verified by experiments that the pretreatment method of debasedness can reduce the influence of static state and quiet environment [11].


### 2.3. Analysis of Eigenvalues of Low-Frequency Data

Based on the low-frequency sensor data composed of behavior, environment, and physical signs, a total of 17 eigenvalues in time domain and frequency domain were extracted, including 8 and 9 eigenvalues in time domain and frequency domain, respectively. Time-domain features refer to some time-related features in the process of data series changing with time, and the time-domain characteristics of low-frequency sensor data mainly include mean value, standard deviation, minimum value, maximum value, mode, correlation coefficient, range, and signal amplitude area. Frequency-domain features are usually used to find periodic signals in signals, and frequency-domain analysis is mainly calculated using fast Fourier transforms; since low-frequency data are one-dimensional,n values will be obtained by FFT for signals of length n, and the frequency-domain features are mainly calculated for these n values. The frequency-domain characteristics of low-frequency sensor data mainly include dc component, shape statistical characteristics, and amplitude statistical characteristics in frequency domain. The DC component is the remainder of the low-frequency signal FFT on dc, which is mathematically the first component, much larger than the rest of the FFT. Power spectral density is used to describe the energy distribution of sensor data in the spectrum, which can be divided into amplitude, shape, and mathematical statistical characteristics, the amplitude eigenvalue is the absolute value of the FFT transformation results, and the shape eigenvalue is the two-dimensional area formed by these FFT results. For the amplitude and shape after the fast Fourier transform, five mathematical statistical characteristic values, namely, mean value, skewness, kurtosis, standard deviation, and kurtosis, are calculated, respectively [12].It is assumed that a window of the same low-frequency sensor data hasn discrete data, and the corresponding sample value of each discrete data is ai.The mean value of a set of discrete sequences of low-frequency sensing is calculated, as shown in following formula:(2)Mean=1n∑i=1nai.The standard deviation of the discrete sequence of low-frequency sensing in the standard deviation group is shown in the following formula:(3)Std=1n∑i=1nai−mean2.The minimum value of a set of discrete sequence of low-frequency sensing is calculated as shown in the following formula:(4)min=minai,i∈1,2,…,n.The maximum value of a set of discrete sequences of low-frequency sensing is calculated as shown in the following formula:(5)max=maxai,i∈1,2,…,n.Mode is the most frequent occurrence of data in a discrete sequence of low-frequency sensors; but if all the data are equally frequent, in this case, there is no modal value for this set of low-frequency sequences, and the calculation formula is shown in the following formula:(6)m=modeai,i∈1,2,…,n.Correlation coefficient represents the degree of similarity between two vectors with the same dimension, and its essence is the covariance of the two vectors. The calculation formula is shown in the following formula:(7)ρx,y=covx,yσxσy.The range is the absolute value of the maximum and minimum values of a frame of discrete low-frequency data, as shown in the following equation:(8)Range=max−min.The area of signal amplitude refers to the sum of the area enclosed by the discrete data and the abscissa time axis. This feature is obvious in the static state and the motion state, and the specific calculation formula is shown in the following formula:(9)SMA=1t∫0txtdt.Statistical characteristics of amplitude: letCi be the frequency amplitude of the ith window after FFT conversion, and N represents the number of windows; then, the calculation methods of several statistics of amplitude statistical characteristics are shown in (10)–(13).Mean amplitude:(10)μamp=1N∑i=1NCi.Standard deviation:(11)σamp=1N∑i=1NCi−μamp2.Partial degrees:(12)γamp=1N∑i=1NCi−μampσamp3.Kurtosis:(13)λamp=1N∑i=1NCi−μampσamp4−3.When calculating the eigenvalues of behavior sensor data, in order to ensure the accuracy of the features and reduce the computational complexity, the synthetic acceleration and frequency-domain eigenvalues are adopted, and the specific calculation method is(14)ai=aix2+aiy2+aiz,ωi=ωix2+ωiy2+ωiz2,i∈1,2,…,n.


### 2.4. Voice Data Processing

Voice data are a specific analog signal waveform carrying the voice information, environmental noise, and wind resistance noise of the wearer [13]. In the wearable device, two MEMS crystal microphones are used for collection, and the codec chip is used for digital processing, and finally, the stereo audio data of the left and right channels are formed. Research shows that the digital model of speech signal can be roughly divided into the excitation model, acoustic tube model, and radiation model, and from the perspective of mathematics, speech signal is a series of unsteady and time-varying process. Considering the voice privacy of long-term experiments and the limited computing power of embedded chips, the final solution of voice feature extraction is to embed short-term energy, spectral entropy, for resonance and brightness into intelligent wearable devices for online real-time feature processing [14].


## 2.1. Application of Wearable Devices for Mental Health Monitoring

Individual’s mental health state can be naturally reflected through language expression, behavior, living environment, physiological indicators, and so on. With the progress of science and the further exploration of natural science by scientists, a large number of researchers have applied wearable devices to behavioral research, speech detection and recognition, health supervision, environmental measurement, and other fields. Based on the application research of existing intelligent wearable devices, this study tries to apply intelligent wearable devices to the field of mental health monitoring. Sensor data such as speech, environment, behavior, physical signs, and electrochemistry can objectively reflect the use of language, comfort of the environment, physical activity, and physical health of the wearer during the day. If these long-term macro and small variables are explored further, they may provide a true and objective reflection of the wearer’s mental health. If these sensors are embedded in a wearable device, they can monitor the wearer’s mental health in real time over a long period of time.


## 2.2. Low-Frequency Data Preprocessing

According to relevant theories of behavioral recognition, before feature extraction of behavioral data, sliding window must be used to reduce the accelerometer noise signal and external interference of the gyroscope when the wrist moves naturally [9]. The principle of a sliding window is similar to that of a low-pass filter, which is mainly related to the window size 1 and the sliding step size s. If the sampling frequency of behavior, environment, physical signs, and other low-frequency sensors is fHz, then the size of a window is usually set to 2f, and the move step s is usually set to f. However, in practical application, the window size should be an exponential power of 2 to ensure the smooth calculation of the FAST Fourier transform, as shown in the following formula:(1)l=2ceillog22∗f.This preconditioning method is specially designed for long-term experiments because in long-term mental health experiments, it is found that the subjects do not keep moving or active all the time; instead, they spend most of their time in a relatively static state, such as sitting, learning, and self-study, which increases the difficulty of feature extraction, and no matter what method is used to extract the eigenvalue, it will reflect the static state [10]. In order to offset the influence of the stationary state in the long time experiment, the pretreatment method of removing bases was proposed, that is, a set of sensor data in a completely static and quiet environment is first collected, and the reference values of various features are calculated; after subtracting this reference value from the long-term experimental data, the subsequent feature extraction is performed, and it has been verified by experiments that the pretreatment method of debasedness can reduce the influence of static state and quiet environment [11].


## 2.3. Analysis of Eigenvalues of Low-Frequency Data

Based on the low-frequency sensor data composed of behavior, environment, and physical signs, a total of 17 eigenvalues in time domain and frequency domain were extracted, including 8 and 9 eigenvalues in time domain and frequency domain, respectively. Time-domain features refer to some time-related features in the process of data series changing with time, and the time-domain characteristics of low-frequency sensor data mainly include mean value, standard deviation, minimum value, maximum value, mode, correlation coefficient, range, and signal amplitude area. Frequency-domain features are usually used to find periodic signals in signals, and frequency-domain analysis is mainly calculated using fast Fourier transforms; since low-frequency data are one-dimensional,n values will be obtained by FFT for signals of length n, and the frequency-domain features are mainly calculated for these n values. The frequency-domain characteristics of low-frequency sensor data mainly include dc component, shape statistical characteristics, and amplitude statistical characteristics in frequency domain. The DC component is the remainder of the low-frequency signal FFT on dc, which is mathematically the first component, much larger than the rest of the FFT. Power spectral density is used to describe the energy distribution of sensor data in the spectrum, which can be divided into amplitude, shape, and mathematical statistical characteristics, the amplitude eigenvalue is the absolute value of the FFT transformation results, and the shape eigenvalue is the two-dimensional area formed by these FFT results. For the amplitude and shape after the fast Fourier transform, five mathematical statistical characteristic values, namely, mean value, skewness, kurtosis, standard deviation, and kurtosis, are calculated, respectively [12].It is assumed that a window of the same low-frequency sensor data hasn discrete data, and the corresponding sample value of each discrete data is ai.The mean value of a set of discrete sequences of low-frequency sensing is calculated, as shown in following formula:(2)Mean=1n∑i=1nai.The standard deviation of the discrete sequence of low-frequency sensing in the standard deviation group is shown in the following formula:(3)Std=1n∑i=1nai−mean2.The minimum value of a set of discrete sequence of low-frequency sensing is calculated as shown in the following formula:(4)min=minai,i∈1,2,…,n.The maximum value of a set of discrete sequences of low-frequency sensing is calculated as shown in the following formula:(5)max=maxai,i∈1,2,…,n.Mode is the most frequent occurrence of data in a discrete sequence of low-frequency sensors; but if all the data are equally frequent, in this case, there is no modal value for this set of low-frequency sequences, and the calculation formula is shown in the following formula:(6)m=modeai,i∈1,2,…,n.Correlation coefficient represents the degree of similarity between two vectors with the same dimension, and its essence is the covariance of the two vectors. The calculation formula is shown in the following formula:(7)ρx,y=covx,yσxσy.The range is the absolute value of the maximum and minimum values of a frame of discrete low-frequency data, as shown in the following equation:(8)Range=max−min.The area of signal amplitude refers to the sum of the area enclosed by the discrete data and the abscissa time axis. This feature is obvious in the static state and the motion state, and the specific calculation formula is shown in the following formula:(9)SMA=1t∫0txtdt.Statistical characteristics of amplitude: letCi be the frequency amplitude of the ith window after FFT conversion, and N represents the number of windows; then, the calculation methods of several statistics of amplitude statistical characteristics are shown in (10)–(13).Mean amplitude:(10)μamp=1N∑i=1NCi.Standard deviation:(11)σamp=1N∑i=1NCi−μamp2.Partial degrees:(12)γamp=1N∑i=1NCi−μampσamp3.Kurtosis:(13)λamp=1N∑i=1NCi−μampσamp4−3.When calculating the eigenvalues of behavior sensor data, in order to ensure the accuracy of the features and reduce the computational complexity, the synthetic acceleration and frequency-domain eigenvalues are adopted, and the specific calculation method is(14)ai=aix2+aiy2+aiz,ωi=ωix2+ωiy2+ωiz2,i∈1,2,…,n.


## 2.4. Voice Data Processing

Voice data are a specific analog signal waveform carrying the voice information, environmental noise, and wind resistance noise of the wearer [13]. In the wearable device, two MEMS crystal microphones are used for collection, and the codec chip is used for digital processing, and finally, the stereo audio data of the left and right channels are formed. Research shows that the digital model of speech signal can be roughly divided into the excitation model, acoustic tube model, and radiation model, and from the perspective of mathematics, speech signal is a series of unsteady and time-varying process. Considering the voice privacy of long-term experiments and the limited computing power of embedded chips, the final solution of voice feature extraction is to embed short-term energy, spectral entropy, for resonance and brightness into intelligent wearable devices for online real-time feature processing [14].


## 3. Results and Analysis

### 3.1. Multisensor Feature Compression Based on PCA

A total of 105 speech-related features, 273 behavioral activity-related features, and 289 environmental and physical signs related features were extracted; although high-dimensional features may imply more information, they are not conducive to visualization and intuitive understanding, and the eigenvalue dimension is too high; it is also possible to introduce unnecessary noise interference and make the convergence rate of the model slower; therefore, in order to get a better expression of the high-dimensional features of three different sensors, the PCA feature compression method was adopted to reduce the dimension of 105 speech features to 4-dimensional space, 273 behavioral activity features were reduced to a three-dimensional space, and 289 environmental and physical signs related features were reduced to a two-dimensional plane [15].As shown in Figures1–6, the scatter diagram of social characteristics is processed by the PCA algorithm because the subjects of the mental health monitoring experiment are only 16; if you take everyone as a sample object, you cannot support a reasonable machine learning model.Figure 1
Scatter diagram of social features of speech samples processed by PCA in one month.Figure 2
Scatter diagram of social features of speech samples around the PCA processing.Figure 3
Scatter plot of PCA processing of behavior and social characteristics in one month.Figure 4
Scatter diagram of PCA processing of behavior and social characteristics around.Figure 5
Scatter plot of one-month PCA treatment of environmental signs and social characteristics.Figure 6
Scatter plot of the surrounding environmental signs and social characteristics processed by PCA.Figures1, 3, and 5 show the 2D feature scatter plot of speech, behavior, environment, and physical signs of 16 samples treated by PCA in one month; due to too few samples, the training model is easy to overfit, resulting in low accuracy of cross-validation [16]. In order to increase more training samples, sample sampling was carried out at a time interval of one week, that is, the average data of each person in one week was taken as a training sample; this ensures that there are enough samples for the machine learning model to train and that the accuracy of the model will not be reduced by the abnormal data of one day. As shown in Figure 1, people with high ASQ scores and those with low ASQ scores have certain overlap in the daily speech features, which is the reason for the presence of noise in the sampled data on a daily basis. So, sampling weekly is a good way to avoid the instability of microdata; in addition, it can supplement the scarcity of macrodata; there are 64 samples from 16 people for 4 weeks, which is enough to support training a simple machine learning model [17].Figures2, 4, and 6 are 2D feature scatter plots of speech, behavior, environment, and physical signs of 64 samples sampled weekly after PCA processing; compared with Figures 1, 3, and 5, it is more deterministic, and the distribution of sample points is more concentrated. More specifically, in the scatter plot of two-dimensional voice PCA features in Figure 2, people with high autism tendency and people with low autism score are evenly distributed on the left and right sides; however, in Figure 1, they are very scattered, and it is difficult to determine their distribution. In the scatter diagram of two-dimensional behavioral PCA features in Figure 4, those with high ASQ scores are mainly scattered in four corners, while those with low ASQ scores are mainly concentrated in the middle and lower part of the graph; in Figure 3, the two are intertwined horizontally and longitudinally, and it is not known from which angle they can be completely separated [18]. In the scatter diagram of 2D environment and physical signs in Figure 6, ASQ high groups are mainly distributed in the lower left corner of the figure, ASQ low groups are mainly clustered in the upper right corner of the graph, and the classification problems of both of them seem to be solved by a linear classifier; as shown in Figure 5, although high ASQ scores and low ASQ scores seem to be easily divided, the actual experiment found that training with a small number of samples would lead to a particularly low generalization ability of the model.In order to further verify the effectiveness of PCA feature compression algorithm and the correctness of training sample selection, in addition, a 2D PCA scatter diagram is presented for the probability features before the boosting method, which promotes weak learner to strong learner, as shown in Figure7. It can be clearly seen from the 2D PCA scatter diagram before boosting voting for the model that the high grouping of ASQ is mainly distributed on the left side of the graph, low ASQ groups are mainly distributed on the right side of the graph, while high ASQ scores and low ASQ scores only have a few sample points overlap; theoretically, the classification accuracy and recall rate should be higher than Figures 2, 4, and 6. Figure 7 shows that the compression algorithm of the PCA physical signs algorithm has achieved significant results when applied to 2D feature transformation; if it is used for 3D or 4D feature transformation, the classification effect of the sample should be better. In addition, 64 training samples are sufficient to verify the rationality of the model for the dichotomy problem in this study.Figure 7
Two-dimensional PCA scatter diagram before boosting voting for the model.


### 3.2. Decision Fusion Based on the Boosting Method

The boosting method-based decision fusion model, the boosting idea of minority voting with majority probability, and the ensemble learning idea of integrating multiple weak learners into a better strong learner through a certain algorithm are learned from boosting. Based on wearable devices to collect sensor data, voice characteristics, and PCA feature compression algorithm, the classification of the logistic regression algorithm, boosting method, and integrated learning thought finally achieved a high precision and good generalization ability; the psychological health monitoring method and integrated learning classification model based on boosting the strong classifier can increase the classification accuracy of 6.3% the left and right sides, and it makes good use of different high-dimensional information of different sensor features to achieve the effect of multisensor feature data fusion. However, the PCA algorithm can reduce the number of features while maintaining most of the high-dimensional information, so that the design of the strong classifier can achieve high prediction accuracy only by using logistic regression as a linear model. In addition, it is worth mentioning that the design of the boosting algorithm should consider the balance between the overfitting and generalization capabilities of weak classifiers; for training data with a small number of samples for a long time, the simpler (linear) the model is, the less likely it is to be overfitted. It is not that the more complex the model is, the better [19].Finally, in order to further evaluate the quality of the mental health monitoring model, this study calculated the accuracy, accuracy, recall rate, and K-folds cross-validation prediction accuracy based on the above speech PCA logistic regression, behavioral PCA logistic regression, environmental and physical signs logistic regression, and boosting decision fusion model. Among them, the accuracy rate, recall rate, and cross-validation accuracy of the weak classification logistic regression model for behavior, environment, and physical signs PCA feature data training were relatively low. Although the accuracy of the logistic regression model for voice PCA feature data training was 0.964, the recall rate and cross-validation results were significantly reduced to 0.844 and 0.846, respectively. The accuracy, accuracy and recall of the decision fusion model based on te boosting method and ensemble learning were 0.969, and the prediction accuracy of K-folds cross-validation was also as high as 0.956, which enabled the superposition fusion results of the three weak classifiers to achieve a better classification effect.


## 3.1. Multisensor Feature Compression Based on PCA

A total of 105 speech-related features, 273 behavioral activity-related features, and 289 environmental and physical signs related features were extracted; although high-dimensional features may imply more information, they are not conducive to visualization and intuitive understanding, and the eigenvalue dimension is too high; it is also possible to introduce unnecessary noise interference and make the convergence rate of the model slower; therefore, in order to get a better expression of the high-dimensional features of three different sensors, the PCA feature compression method was adopted to reduce the dimension of 105 speech features to 4-dimensional space, 273 behavioral activity features were reduced to a three-dimensional space, and 289 environmental and physical signs related features were reduced to a two-dimensional plane [15].As shown in Figures1–6, the scatter diagram of social characteristics is processed by the PCA algorithm because the subjects of the mental health monitoring experiment are only 16; if you take everyone as a sample object, you cannot support a reasonable machine learning model.Figure 1
Scatter diagram of social features of speech samples processed by PCA in one month.Figure 2
Scatter diagram of social features of speech samples around the PCA processing.Figure 3
Scatter plot of PCA processing of behavior and social characteristics in one month.Figure 4
Scatter diagram of PCA processing of behavior and social characteristics around.Figure 5
Scatter plot of one-month PCA treatment of environmental signs and social characteristics.Figure 6
Scatter plot of the surrounding environmental signs and social characteristics processed by PCA.Figures1, 3, and 5 show the 2D feature scatter plot of speech, behavior, environment, and physical signs of 16 samples treated by PCA in one month; due to too few samples, the training model is easy to overfit, resulting in low accuracy of cross-validation [16]. In order to increase more training samples, sample sampling was carried out at a time interval of one week, that is, the average data of each person in one week was taken as a training sample; this ensures that there are enough samples for the machine learning model to train and that the accuracy of the model will not be reduced by the abnormal data of one day. As shown in Figure 1, people with high ASQ scores and those with low ASQ scores have certain overlap in the daily speech features, which is the reason for the presence of noise in the sampled data on a daily basis. So, sampling weekly is a good way to avoid the instability of microdata; in addition, it can supplement the scarcity of macrodata; there are 64 samples from 16 people for 4 weeks, which is enough to support training a simple machine learning model [17].Figures2, 4, and 6 are 2D feature scatter plots of speech, behavior, environment, and physical signs of 64 samples sampled weekly after PCA processing; compared with Figures 1, 3, and 5, it is more deterministic, and the distribution of sample points is more concentrated. More specifically, in the scatter plot of two-dimensional voice PCA features in Figure 2, people with high autism tendency and people with low autism score are evenly distributed on the left and right sides; however, in Figure 1, they are very scattered, and it is difficult to determine their distribution. In the scatter diagram of two-dimensional behavioral PCA features in Figure 4, those with high ASQ scores are mainly scattered in four corners, while those with low ASQ scores are mainly concentrated in the middle and lower part of the graph; in Figure 3, the two are intertwined horizontally and longitudinally, and it is not known from which angle they can be completely separated [18]. In the scatter diagram of 2D environment and physical signs in Figure 6, ASQ high groups are mainly distributed in the lower left corner of the figure, ASQ low groups are mainly clustered in the upper right corner of the graph, and the classification problems of both of them seem to be solved by a linear classifier; as shown in Figure 5, although high ASQ scores and low ASQ scores seem to be easily divided, the actual experiment found that training with a small number of samples would lead to a particularly low generalization ability of the model.In order to further verify the effectiveness of PCA feature compression algorithm and the correctness of training sample selection, in addition, a 2D PCA scatter diagram is presented for the probability features before the boosting method, which promotes weak learner to strong learner, as shown in Figure7. It can be clearly seen from the 2D PCA scatter diagram before boosting voting for the model that the high grouping of ASQ is mainly distributed on the left side of the graph, low ASQ groups are mainly distributed on the right side of the graph, while high ASQ scores and low ASQ scores only have a few sample points overlap; theoretically, the classification accuracy and recall rate should be higher than Figures 2, 4, and 6. Figure 7 shows that the compression algorithm of the PCA physical signs algorithm has achieved significant results when applied to 2D feature transformation; if it is used for 3D or 4D feature transformation, the classification effect of the sample should be better. In addition, 64 training samples are sufficient to verify the rationality of the model for the dichotomy problem in this study.Figure 7
Two-dimensional PCA scatter diagram before boosting voting for the model.


## 3.2. Decision Fusion Based on the Boosting Method

The boosting method-based decision fusion model, the boosting idea of minority voting with majority probability, and the ensemble learning idea of integrating multiple weak learners into a better strong learner through a certain algorithm are learned from boosting. Based on wearable devices to collect sensor data, voice characteristics, and PCA feature compression algorithm, the classification of the logistic regression algorithm, boosting method, and integrated learning thought finally achieved a high precision and good generalization ability; the psychological health monitoring method and integrated learning classification model based on boosting the strong classifier can increase the classification accuracy of 6.3% the left and right sides, and it makes good use of different high-dimensional information of different sensor features to achieve the effect of multisensor feature data fusion. However, the PCA algorithm can reduce the number of features while maintaining most of the high-dimensional information, so that the design of the strong classifier can achieve high prediction accuracy only by using logistic regression as a linear model. In addition, it is worth mentioning that the design of the boosting algorithm should consider the balance between the overfitting and generalization capabilities of weak classifiers; for training data with a small number of samples for a long time, the simpler (linear) the model is, the less likely it is to be overfitted. It is not that the more complex the model is, the better [19].Finally, in order to further evaluate the quality of the mental health monitoring model, this study calculated the accuracy, accuracy, recall rate, and K-folds cross-validation prediction accuracy based on the above speech PCA logistic regression, behavioral PCA logistic regression, environmental and physical signs logistic regression, and boosting decision fusion model. Among them, the accuracy rate, recall rate, and cross-validation accuracy of the weak classification logistic regression model for behavior, environment, and physical signs PCA feature data training were relatively low. Although the accuracy of the logistic regression model for voice PCA feature data training was 0.964, the recall rate and cross-validation results were significantly reduced to 0.844 and 0.846, respectively. The accuracy, accuracy and recall of the decision fusion model based on te boosting method and ensemble learning were 0.969, and the prediction accuracy of K-folds cross-validation was also as high as 0.956, which enabled the superposition fusion results of the three weak classifiers to achieve a better classification effect.


## 4. Conclusions

The mathematical statistical features based on speech, behavior, environment, and physical signs were proposed, the PCA feature compression algorithm was used to reduce the dimension of these features, the differences between different training samples were compared and analyzed, and then, three weak classifiers were designed using the logistic regression algorithm; 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 ensemble learning were 0.969, and the prediction accuracy of K-folds cross-validation was also as high as 0.956, which enabled the superposition fusion results of the three weak classifiers to achieve the better classification effect. With the progress and development of science and technology, various biosensors are rapidly iterated. In the future, a heart rate and blood pressure expansion module with high measurement accuracy, small power loss, small integrated area, and simple application circuit will be found, which will further strengthen the monitoring of biological weak current signals of intelligent wearable devices in this study. With the rapid development of 5G, the wide application of the IoT module and the rapid upgrade of bluetooth protocol and Wi-Fi protocol, a wireless module with ultralow power consumption, ultrafast transmission speed, and ultralong transmission distance will be found in the future, which will make up for the shortcomings of wearable devices in wireless data transmission in this study. In terms of the experimental data, while this article has carried on the psychological health monitoring experiment for a long time, it has collected a large number of original data, but from a macroperspective, the sample quantity is not enough, the sample distribution is not comprehensive, in the future work, should collect more different age and different classes of the experimental data for a long time, promoting mental health monitoring generalization and robustness of the model.



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*Source: 8307576-2021-11-16.xml*