Human body physiological signal acquisition and analysis system in Internet of Things environment

Abstract

The invention discloses a human body physiological signal acquisition and analysis system in an Internet of Things environment. An operation method of the system comprises the following steps: step 1, acquiring a multi-modal physiological signal; 2, intelligent prediction and early warning of the body health state; 3, the adaptability of the system is enhanced through an environment sensing function; 4, mental health analysis is conducted on the user, and the data collection module is used for comprehensively collecting physiological signals and mental health indexes of the user; the intelligent analysis module is used for establishing a prediction model by using artificial intelligence and a machine learning algorithm, analyzing and predicting multi-modal data, and early warning possible health risks in advance; the user service module is used for providing personalized health suggestions and intervention measures according to the physical and mental health conditions of the user and helping the user to improve the health state; the system has the characteristics of comprehensive human body data acquisition and accurate physiological and psychological data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a human physiological signal collection and analysis system in an Internet of Things environment. Background Art

[0002] With the continuous development of information technology and the Internet of Things, human physiological signal acquisition and health monitoring technology are experiencing revolutionary changes. Traditional health monitoring equipment can usually only collect a single physiological signal, such as heart rate, blood pressure, etc., which makes it difficult to fully assess the user's physical condition. At the same time, the impact of mental health on physical health is also receiving increasing attention, but existing health monitoring systems often ignore the comprehensive analysis of psychological factors.

[0003] To solve the above problems, some research institutions and enterprises have tried to integrate multiple sensors to achieve multimodal physiological signal acquisition. By combining multiple sensors such as electrocardiogram, electroencephalogram, and blood oxygen saturation, the user's physiological state can be fully understood from different angles. In addition, the development of artificial intelligence and machine learning algorithms has made it possible to analyze data intelligently, allowing the system to predict and warn the collected physiological signals, thereby better helping users deal with health risks.

[0004] However, traditional physiological signal acquisition systems still face challenges in dealing with data changes in different environments and psychological states. The accuracy and reliability of physiological signals are affected by environmental factors and are not sufficient for assessing the user's physical and mental health. Therefore, it is necessary to design a human physiological signal acquisition and analysis system in the IoT environment with comprehensive data acquisition and accurate analysis. Summary of the invention

[0005] The purpose of the present invention is to provide a human physiological signal collection and analysis system in an Internet of Things environment to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a human physiological signal acquisition and analysis system in an Internet of Things environment, wherein the operation method of the system comprises the following steps: Step 1: Multimodal physiological signal acquisition; Step 2: Intelligent prediction and early warning of physical health status; Step 3: Enhance the system’s adaptability through environmental perception functions; Step 4: Conduct psychological health analysis on users.

[0007] According to the above technical solution, the step of collecting multimodal physiological signals includes: Install various sensors; Comprehensively collect users' physiological signals from different angles; Data fusion and synchronization.

[0008] According to the above technical solution, the step of comprehensively collecting the user's physiological signals from different angles includes: The system uses a variety of different types of sensors, covering the cardiovascular, nervous system and respiratory systems. For example, ECG sensors are used to record cardiac activity, EEG sensors are used to monitor brain wave activity, and blood oxygen saturation sensors are used to measure blood oxygen saturation. The selection of these sensors can be flexibly adjusted according to application requirements and user characteristics to meet different health monitoring goals. At the same time, it ensures that various physiological signals can be accurately collected. For example, the ECG sensor should be placed on the chest, the EEG sensor should be placed on the scalp surface, and the blood oxygen saturation sensor can be placed on the finger or earlobe.

[0009] According to the above technical solution, the steps of intelligent prediction and early warning of physical health status include: Use artificial intelligence and machine learning algorithms to build predictive models; Analyze multimodal physiological signal data to predict possible health risks.

[0010] According to the above technical solution, the step of analyzing multimodal physiological signal data and predicting possible health risks includes: Users use this system to monitor their cardiovascular health. The system obtains the user's electrocardiogram, electroencephalogram and blood oxygen saturation data through the multimodal physiological signal acquisition module. The prediction model will establish a cardiovascular health prediction model based on these data. When the system analyzes these data, it is found that the user's electrocardiogram shows an abnormal heart rate pattern and the blood oxygen saturation drops to a low level. In this case, the system will immediately send an early warning message to the user's mobile phone, prompting the user that there may be cardiovascular health risks. After receiving the early warning, the user can consult a doctor in time for further diagnosis and treatment to avoid potential health problems. Through intelligent prediction and early warning, the system can help users better understand their health status and take positive measures to maintain their health.

[0011] According to the above technical solution, the step of enhancing the adaptability of the system through the environment perception function includes: The system is equipped with environmental sensors; The system automatically adjusts the parameters for physiological signal acquisition.

[0012] According to the above technical solution, the step of automatically adjusting the parameters of physiological signal acquisition by the system includes: The frequency of heart rate collection is adjusted according to the ambient temperature to adapt to the user's physiological response at different temperatures; in low light conditions, the gain of the EEG signal is increased to enhance the stability of the signal. In addition to adjusting the acquisition parameters, the system will also adjust the analysis algorithm according to environmental factors. For example, in a high temperature environment, the heart rate anomaly detection algorithm may need to be adjusted accordingly to avoid misjudgment of the heart rate signal by the ambient temperature. In this way, the system can better adapt to and optimize the analysis algorithm in different environments to ensure the accuracy of data processing. Suppose the user is exercising outdoors in a high temperature summer environment, and the system's environmental sensor senses an increase in ambient temperature. In this case, the system will automatically adjust the frequency of heart rate collection and increase the time interval for collection to cope with the user's large fluctuations in heart rate in a high temperature environment. At the same time, the system will also adjust the heart rate anomaly detection algorithm accordingly to ensure that the identification of heart rate anomalies under high temperature conditions is more accurate. In this way, the system can flexibly adjust the acquisition parameters and algorithms in different usage scenarios to provide more reliable health monitoring services and help users stay healthy in different environments.

[0013] According to the above technical solution, the step of performing psychological health analysis on the user includes: The system collects the user's mental health indicators through the user terminal or specific sensor modules; The collected mental health indicators are correlated with the physiological signal data collected at the same time.

[0014] According to the above technical solution, the step of performing correlation analysis on the collected mental health indicators and the physiological signal data collected at the same time includes: During the user's use, the system collects the user's mental health indicators, such as the user's emotional state obtained through expression recognition technology, and the stress level assessed through HRV physiological indicators. After comprehensively analyzing the collected data, the system finds that the user has experienced frequent anxiety in recent times, accompanied by an increase in heart rate changes. Based on these data analyses, the system will send relevant health suggestions to the user, such as reminding the user to perform deep breathing exercises, physical relaxation and meditation when feeling anxious to reduce stress and anxiety. In addition, the system can also suggest that the user maintain regular exercise and eating habits to promote an overall balance of mental and physical health. Through the implementation of mental health analysis, the system can help users better understand their physical and mental health, provide personalized health advice and intervention measures, and promote users' health management and improved quality of life.

[0015] According to the above technical solution, the system includes: Data collection module, used to comprehensively collect users' physiological signals and mental health indicators; Intelligent analysis module, which uses artificial intelligence and machine learning algorithms to establish predictive models, analyze and predict multimodal data, and provide early warning of possible health risks; The user service module is used to provide personalized health advice and intervention measures based on the user's physical and mental health conditions to help users improve their health status.

[0016] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention realizes human body data analysis by providing a data acquisition module, an intelligent analysis module, and a user service module. The system first combines a variety of sensors to comprehensively collect the user's electrocardiogram, electroencephalogram, blood oxygen saturation and other physiological signals to provide comprehensive health data. Then, artificial intelligence and machine learning algorithms are used to establish a prediction model, and predict possible health risks based on multimodal physiological signal data, and send early warning information to users in a timely manner to help users better deal with health problems. Secondly, the system can automatically adjust the acquisition parameters and analysis algorithms according to the user's environment and activities, improve the accuracy and effectiveness of data acquisition, so as to better adapt to different usage scenarios and provide reliable health monitoring services. Finally, the system not only collects the user's physiological signals, but also obtains the user's emotional and stress level and other mental health indicators. By analyzing the relationship between these mental health indicators and physiological signals, the system comprehensively evaluates the user's physical and mental health status, and provides personalized health advice and intervention measures. The present invention has the characteristics of comprehensive data acquisition and accurate data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A flow chart of a method for collecting and analyzing human physiological signals in an Internet of Things environment provided by Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the module composition of the human physiological signal acquisition and analysis system in the Internet of Things environment provided by Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1: Figure 1This is a flow chart of a method for collecting and analyzing human physiological signals in an Internet of Things environment provided by Embodiment 1 of the present invention. This embodiment can be applied to scenarios of human health monitoring. This method can be performed by a human physiological signal collection and analysis system in an Internet of Things environment provided by this embodiment, such as Figure 1 As shown, the method specifically comprises the following steps: Step 1: Multimodal physiological signal acquisition; In the embodiment of the present invention, by combining multiple sensors, the user's physiological signals are comprehensively collected from different angles, including but not limited to electrocardiogram, electroencephalogram, blood oxygen saturation, etc., to provide more comprehensive and diverse health data, helping the system to better understand the user's physical condition; Exemplarily, the system uses a variety of different types of sensors, covering aspects such as the cardiovascular system, the nervous system, and the respiratory system. For example, an electrocardiogram sensor is used to record cardiac activity, an electroencephalogram sensor is used to monitor brain wave activity, and a blood oxygen saturation sensor is used to measure blood oxygen saturation. The selection of these sensors can be flexibly adjusted according to application requirements and user characteristics to meet different health monitoring goals. At the same time, it is ensured that various physiological signals can be accurately collected. For example, an electrocardiogram sensor should be placed on the chest, an electroencephalogram sensor should be placed on the scalp surface, and a blood oxygen saturation sensor can be placed on the finger or earlobe. Through a reasonable sensor layout, the system can simultaneously obtain multiple physiological signals to form comprehensive physiological data. Exemplarily, during the acquisition process, the physiological data generated by different sensors need to be fused and synchronized, which includes timestamp processing and data calibration to ensure that the collected physiological signals remain consistent in time, making subsequent data analysis and processing more accurate and reliable. In addition, during the data acquisition process, the system monitors the status of the sensor and the data quality in real time. If a sensor failure or data abnormality is found, the system should promptly remind the user to process or replace the sensor to ensure the reliability and stability of physiological signal acquisition. Through the above implementation methods, multimodal physiological signal acquisition can obtain the user's physiological data from multiple aspects to form a comprehensive and diverse health database.

[0020] Step 2: Intelligent prediction and early warning of physical health status; In the embodiment of the present invention, artificial intelligence and machine learning algorithms are combined to establish a prediction model using multimodal physiological signal data. By analyzing these data, the system can predict possible health risks, such as heart disease, cerebrovascular disease, etc., and send early warning information to users in advance to help users better deal with health problems; Exemplarily, the system collects a large amount of multimodal physiological signal data and annotates each data sample according to its actual health status. For example, for the prediction of heart disease, it is necessary to collect the physiological signal data of heart disease patients and healthy people, and annotate whether they have heart disease. Then, from the collected data, a feature extraction algorithm is used to extract features related to health risks. These features include heart rate variability, blood oxygen saturation curve features, brain wave spectrum, etc. Then, a feature selection algorithm is used to screen out features that are important for health risk prediction, so as to reduce the complexity of the model and improve the prediction accuracy. Finally, an intelligent prediction model is established through artificial intelligence and machine learning algorithms. By learning and optimizing on the training data, the model can learn patterns and laws related to health risks from multimodal physiological signal data. Exemplarily, when a user collects physiological signals, the system inputs the physiological signals into the prediction model to predict health risks. For example, based on the user's electrocardiogram, blood oxygen saturation and other data, the model can predict whether the user is at risk of heart disease. If a possible health problem is found, the system will immediately send a warning message to the user to remind the user to pay attention and take corresponding health management measures in time. For example, when a user uses this system to monitor cardiovascular health, the system obtains the user's electrocardiogram, electroencephalogram and blood oxygen saturation data through a multimodal physiological signal acquisition module. The prediction model will establish a cardiovascular health prediction model based on these data. When the system analyzes these data, it is found that the user's electrocardiogram shows an abnormal heart rate pattern and the blood oxygen saturation drops to a low level. In this case, the system will immediately send a warning message to the user's mobile phone, prompting the user that there may be cardiovascular health risks. After receiving the warning, the user can consult a doctor in time for further diagnosis and treatment to avoid potential health problems. Through intelligent prediction and warning, the system can help users better understand their health status and take positive measures to maintain their health.

[0021] Step 3: Enhance the system’s adaptability through environmental perception functions; In the embodiment of the present invention, the system can automatically adjust the collection parameters and analysis algorithms according to the environment and activities of the user to improve the accuracy and effectiveness of data collection. By making corresponding adjustments according to environmental factors, the system can better adapt to different usage scenarios, thereby providing more reliable health monitoring services; Exemplarily, the system is equipped with environmental sensors for sensing the user's environment, such as temperature, humidity, light intensity, etc. These environmental sensors can be hardware sensors built into the device or external sensors connected to the system. The environmental sensors will continuously monitor environmental parameters and provide the system with timely environmental perception data. Based on the data obtained from the environmental sensors, the system analyzes environmental factors. For example, in a high temperature environment, the user's heart rate and body temperature may increase; in low light conditions, the electroencephalogram signal may be disturbed. By analyzing these environmental factors, the system can understand the degree of influence of the environment on physiological signals. Exemplarily, based on the analysis results of environmental factors, the system automatically adjusts the parameters of physiological signal acquisition and adjusts the frequency of heart rate acquisition according to the ambient temperature to adapt to the user's physiological response at different temperatures; under low light conditions, the gain of the EEG signal is increased to enhance the stability of the signal. In addition to adjusting the acquisition parameters, the system also adjusts the analysis algorithm according to environmental factors. For example, in a high temperature environment, the heart rate anomaly detection algorithm may need to be adjusted accordingly to avoid misjudgment of the heart rate signal by the ambient temperature. In this way, the system can better adapt to and optimize the analysis algorithm in different environments to ensure the accuracy of data processing. Suppose the user is exercising outdoors in a high temperature summer environment, and the system's environmental sensor senses an increase in ambient temperature. In this case, the system will automatically adjust the frequency of heart rate acquisition and increase the time interval for acquisition to cope with the user's large fluctuations in heart rate in a high temperature environment. At the same time, the system will also adjust the heart rate anomaly detection algorithm accordingly to ensure that the recognition of heart rate anomalies under high temperature conditions is more accurate. In this way, the system can flexibly adjust the acquisition parameters and algorithms in different usage scenarios to provide more reliable health monitoring services and help users stay healthy in different environments.

[0022] Step 4: Conduct psychological health analysis on users.

[0023] In the embodiment of the present invention, in order to realize the psychological health analysis function, the system collects psychological health indicators such as the user's emotions and stress levels while comprehensively collecting the user's physiological signals. By analyzing the relationship between these psychological health indicators and physiological signals, the system can provide comprehensive physical and mental health assessments and suggestions; Exemplarily, the system collects the user's mental health indicators through the user terminal or a specific sensor module. These indicators include emotional state, stress level, anxiety level, sleep quality, etc. The emotional state can be obtained through the user's text input, voice recognition or expression recognition, and the stress level can be evaluated through the user's heart rate variability and other physiological indicators. The system performs correlation analysis on the collected mental health indicators and the physiological signal data collected at the same time. By cross-analyzing these data, the system can explore the relationship and mutual influence between mental health status and physiological signals. Based on the results of the correlation analysis, the system can comprehensively evaluate the user's physical and mental health status; Exemplarily, during the user's use, the system collects the user's mental health indicators, such as the user's emotional state obtained through expression recognition technology, and the stress level assessed through physiological indicators such as HRV. After comprehensively analyzing the collected data, the system finds that the user has experienced frequent anxiety in recent times, accompanied by an increase in heart rate changes. Based on these data analyses, the system will send relevant health advice to the user, such as reminding the user to perform deep breathing exercises, physical relaxation and meditation when feeling anxious to reduce stress and anxiety. In addition, the system can also suggest that the user maintain regular exercise and eating habits to promote an overall balance of mental and physical health. Through the implementation of mental health analysis, the system can help users better understand their physical and mental health, provide personalized health advice and intervention measures, and promote users' health management and improved quality of life.

[0024] Embodiment 2: Embodiment 2 of the present invention provides a human physiological signal collection and analysis system in an Internet of Things environment. Figure 2 The schematic diagram of the module composition of the human physiological signal acquisition and analysis system in the Internet of Things environment provided by the second embodiment of the present invention is as follows: Figure 2 As shown, the system includes: Data collection module, used to comprehensively collect users' physiological signals and mental health indicators; Intelligent analysis module, which uses artificial intelligence and machine learning algorithms to establish predictive models, analyze and predict multimodal data, and provide early warning of possible health risks; User service module, which is used to provide personalized health advice and intervention measures based on the user's physical and mental health conditions to help users improve their health status; In some embodiments of the present invention, the data acquisition module includes: A multimodal physiological signal acquisition module is used to comprehensively collect the user's physiological signals from different angles by combining multiple sensors; The mental health index collection module is used to collect the user's mood, stress level, sleep quality and other mental health indicators for comprehensive analysis of the user's physical and mental health status; The environment perception module is used to obtain the user's environment information through the environment sensor; In some embodiments of the present invention, the intelligent analysis module includes: A multimodal data fusion module, used to fuse the collected multimodal physiological signal data; Prediction model building and optimization module, which is used to build prediction models using artificial intelligence and machine learning algorithms, analyze and predict multimodal data, and provide early warning of possible health risks; Correlation analysis module, used to analyze the relationship between mental health indicators and physiological signals; In some embodiments of the present invention, the user service module includes: The early warning information sending module is used to send early warning information to users when the intelligent analysis module detects potential health risks, so that users can take corresponding measures to deal with health problems in a timely manner; Personalized health advice and intervention module, which is used to provide personalized health advice and intervention measures based on the user's physical and mental health conditions; The long-term health monitoring and feedback module is used to conduct long-term monitoring of the user's physical and mental health, provide historical health data and health trend analysis, and help users make self-adjustments and management.

[0025] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0026] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Human physiological signal collection and analysis methods in the Internet of Things environment, The method comprises the following steps: Step 1: Multimodal physiological signal acquisition; Step 2: Intelligent prediction and early warning of physical health status; Step 3: Enhance the system’s adaptability through environmental perception functions; Step 4: Conduct psychological health analysis on users.

2. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 1, characterized in that: The step of collecting multimodal physiological signals includes: Install various sensors; Comprehensively collect users' physiological signals from different angles; Data fusion and synchronization.

3. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 2 is characterized in that: The step of comprehensively collecting the user's physiological signals from different angles includes: The system uses a variety of different types of sensors, covering the cardiovascular, nervous system and respiratory systems. For example, ECG sensors are used to record cardiac activity, EEG sensors are used to monitor brain wave activity, and blood oxygen saturation sensors are used to measure blood oxygen saturation. The selection of these sensors can be flexibly adjusted according to application requirements and user characteristics to meet different health monitoring goals. At the same time, it ensures that various physiological signals can be accurately collected. For example, the ECG sensor should be placed on the chest, the EEG sensor should be placed on the scalp surface, and the blood oxygen saturation sensor can be placed on the finger or earlobe.

4. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 1, characterized in that: The steps of intelligent prediction and early warning of physical health status include: Use artificial intelligence and machine learning algorithms to build predictive models; Analyze multimodal physiological signal data to predict possible health risks.

5. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 4 is characterized in that: The step of analyzing multimodal physiological signal data to predict possible health risks includes: Users use this system to monitor their cardiovascular health. The system obtains the user's electrocardiogram, electroencephalogram and blood oxygen saturation data through the multimodal physiological signal acquisition module. The prediction model will establish a cardiovascular health prediction model based on these data. When the system analyzes these data, it is found that the user's electrocardiogram shows an abnormal heart rate pattern and the blood oxygen saturation drops to a low level. In this case, the system will immediately send an early warning message to the user's mobile phone, prompting the user that there may be cardiovascular health risks. After receiving the early warning, the user can consult a doctor in time for further diagnosis and treatment to avoid potential health problems. Through intelligent prediction and early warning, the system can help users better understand their health status and take positive measures to maintain their health.

6. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 1, characterized in that: The step of enhancing the adaptability of the system through the environment perception function includes: The system is equipped with environmental sensors; The system automatically adjusts the parameters for physiological signal acquisition.

7. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 6, characterized in that: The step of automatically adjusting the parameters of physiological signal acquisition by the system includes: The frequency of heart rate collection is adjusted according to the ambient temperature to adapt to the user's physiological response at different temperatures; in low light conditions, the gain of the EEG signal is increased to enhance the stability of the signal. In addition to adjusting the acquisition parameters, the system will also adjust the analysis algorithm according to environmental factors. For example, in a high temperature environment, the heart rate anomaly detection algorithm may need to be adjusted accordingly to avoid misjudgment of the heart rate signal by the ambient temperature. In this way, the system can better adapt to and optimize the analysis algorithm in different environments to ensure the accuracy of data processing. Suppose the user is exercising outdoors in a high temperature summer environment, and the system's environmental sensor senses an increase in ambient temperature. In this case, the system will automatically adjust the frequency of heart rate collection and increase the time interval for collection to cope with the user's large fluctuations in heart rate in a high temperature environment. At the same time, the system will also adjust the heart rate anomaly detection algorithm accordingly to ensure that the identification of heart rate anomalies under high temperature conditions is more accurate. In this way, the system can flexibly adjust the acquisition parameters and algorithms in different usage scenarios to provide more reliable health monitoring services and help users stay healthy in different environments.

8. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 1, characterized in that: The step of performing psychological health analysis on the user comprises: The system collects the user's mental health indicators through the user terminal or specific sensor modules; The collected mental health indicators are correlated with the physiological signal data collected at the same time.

9. The method for collecting and analyzing human physiological signals in an Internet of Things environment according to claim 8, characterized in that: The step of correlating and analyzing the collected mental health indicators with the physiological signal data collected simultaneously includes: During the user's use, the system collects the user's mental health indicators, such as the user's emotional state obtained through expression recognition technology, and the stress level assessed through HRV physiological indicators. After comprehensively analyzing the collected data, the system finds that the user has experienced frequent anxiety in recent times, accompanied by an increase in heart rate changes. Based on these data analyses, the system will send relevant health suggestions to the user, such as reminding the user to perform deep breathing exercises, physical relaxation and meditation when feeling anxious to reduce stress and anxiety. In addition, the system can also suggest that the user maintain regular exercise and eating habits to promote an overall balance of mental and physical health. Through the implementation of mental health analysis, the system can help users better understand their physical and mental health, provide personalized health advice and intervention measures, and promote users' health management and improved quality of life.

10. The human physiological signal acquisition and analysis system in the Internet of Things environment is characterized by: The system comprises: Data collection module, used to comprehensively collect users' physiological signals and mental health indicators; Intelligent analysis module, which uses artificial intelligence and machine learning algorithms to establish predictive models, analyze and predict multimodal data, and provide early warning of possible health risks; The user service module is used to provide personalized health advice and intervention measures based on the user's physical and mental health conditions to help users improve their health status.

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