Intelligent old people health monitoring system

Abstract

The invention relates to the technical field of medical monitoring, in particular to an intelligent old people health monitoring system, which comprises a heart rate module for collecting heart rate signals, identifying local peak values in the signals to detect heartbeat, measuring time intervals between continuous peak values to determine heart beat intervals, and calculating differences between adjacent intervals to evaluate variability. According to the method, through synchronous analysis of multi-dimensional data of the comprehensive heart rate, the skin conductance and the facial expression, the comprehensive understanding and evaluation ability of the health state of the old people is enhanced. The complex relationship between the emotion change and the physiological response is accurately captured and analyzed by integrating multi-source data without singly depending on any physiological index. Through the method, the health warning signal can be identified in the subtle change, and early warning can be carried out in time. Besides, environmental factors such as illumination, music and fragrance can be dynamically adjusted according to the mood and health state monitored in real time, so that more personalized and optimized environmental response is provided.

Description

Technical Field

[0001] The present invention relates to the field of medical monitoring technology, and in particular to an intelligent health monitoring system for the elderly. Background Art

[0002] The field of medical monitoring technology focuses on the development and application of various technologies to monitor the health status of patients. This field uses information and communication technology (ICT) to achieve real-time tracking and data analysis of patient signs, thereby improving the accuracy of diagnosis and the effectiveness of treatment. Specifically, it includes advanced imaging technology, biomarker detection technology and medical simulation for medical diagnosis. In addition, this field also covers the use of big data and machine learning technology for medical data mining, and the development of systems for detecting, monitoring or modeling epidemics or infectious diseases. The advancement of these technologies has played a key role in public health response measures.

[0003] Existing technologies in medical monitoring are often limited to a single data source and have difficulty providing comprehensive health assessments, especially in complex situations where emotions and physical health interact. For example, existing technologies have difficulty accurately identifying changes in heart rate caused by emotional fluctuations, resulting in inaccurate or in-time early warnings of health problems. When users experience health or emotional problems, they often cannot provide the necessary support, affecting the effectiveness of health management. These technologies limit the functions of health prevention and intervention, reducing the potential for improving user experience and quality of life. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent health monitoring system for the elderly.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an intelligent health monitoring system for the elderly comprises: A heart rate module collects heart rate signals, identifies local peaks in the signals to detect heartbeats, measures the time intervals between consecutive peaks to determine the heartbeat intervals, calculates the differences between adjacent intervals to assess variability, calculates the mean and standard deviation statistics of the variability, stores the values ​​in a data set, and obtains a heart rate variability data index; Skin response module, collects skin conductance signals, records conductance values ​​at fixed time intervals, identifies changes in conductance levels by comparing consecutive readings, calculates the rate of change of conductance over time, analyzes patterns of conductance increase and decrease, calculates statistics of mean and standard deviation, stores values ​​in a data set, and obtains skin conductance data index; The expression module captures facial images, locates facial points at the corners of the eyes, eyebrows, and mouth, tracks the positions of facial points in consecutive images, calculates position differences to measure displacement and movement, quantifies the degree of activation of facial muscles, stores the measurements in a data set, and obtains facial expression data; An emotion module, based on the heart rate variability data index, skin conductance data index and facial expression data, synchronizes the data by aligning the timestamps to correspond to the same moment, calculates the correlation coefficient between the physiological data and the facial movement to analyze the correlation, identifies the synchronization pattern of physiological and facial changes, determines the similarity of each emotional state by comparing the observed pattern with the stored template to match the preset emotional characteristics, selects the emotional state with the highest similarity, assigns a calm, happy, sad or anxious label, records the confidence level of the recognition, stores the information in the data set, and obtains the emotional state information; an adjustment module, based on the emotional state information, accessing an environment setting database associated with the emotional state, retrieving recommended parameters for the brightness level and color temperature of the lighting, selecting a matching music track by matching the emotional tone, determining the type and intensity of fragrance known to affect the mood, assigning values ​​and selections to each parameter, and generating an environment adjustment instruction; The feedback module, based on the environmental adjustment instruction, sends commands to the lighting to adjust the brightness and color temperature, plays the selected music track on the audio player, and activates the fragrance device to release the fragrance.

[0006] Preferably, the steps of obtaining the heart rate variability data index are: Collecting heart rate signals, identifying local peaks in the signals to detect heartbeats, measuring time intervals between consecutive peaks to determine heartbeat intervals, and obtaining heartbeat interval data; Based on the heart beat interval data, the difference between adjacent heart beat intervals is calculated using the formula:

[0007] Calculate the heart beat interval variability and generate heart beat interval variability data, where: represents the heart beat interval measured for the i-th time, represents the heart beat interval measured at the i-1th time, represents the average value of the heart beat interval, represents the variance of the heart beat interval; Based on the heart rate interval variability data, the mean and standard deviation of the variability are calculated, and the values ​​are stored in a data set to obtain a heart rate variability data index.

[0008] Preferably, the steps of obtaining the skin conductance data index are: Collect skin conductance signals, record conductance values ​​at fixed time intervals, and obtain time series conductance data; Based on the time series conductance data, consecutive readings are compared to identify changes in conductance levels using the formula:

[0009] Calculate the rate of change of conductivity over time and generate conductivity change rate data, where: represents the conductivity value measured for the ith time, represents the conductivity value measured at the i-1th time, represents the average value of the conductivity, represents the standard deviation of the conductivity value; Based on the conductivity change rate data, the mean and standard deviation of the change rate are calculated, and the values ​​are stored in the data set to obtain the skin conductance data index.

[0010] Preferably, the steps of acquiring the facial expression data are: Capture facial images, locate facial points at the corners of eyes, eyebrows and mouth, track the position changes of facial points in continuous images, and obtain facial point position data; Based on the facial point position data, position differences are calculated to measure displacement and motion, using the formula:

[0011] Calculate the activation degree of facial muscles and generate facial muscle activation data, where: and Represent the x and y coordinates of a specific facial point at the i-th measurement, and Respectively represent the x and y coordinates of the point at the i-1th measurement, represents the average of all facial point position differences; Based on the facial muscle activation data, the statistics of the activation degree are calculated, the values ​​are stored in the data set, and the facial expression data is obtained.

[0012] Preferably, the steps of acquiring the emotional state information are: Based on the heart rate variability data index, the skin conductance data index and the facial expression data, aligning the timestamp synchronization data to correspond to the same moment, thereby obtaining synchronized physiological and facial data; Based on the synchronized physiological and facial data, a correlation coefficient between the physiological data and the facial movement is calculated using the formula:

[0013] Analyze the correlation and generate correlation analysis data, where: and Respectively represent the values ​​of physiological data and facial data measured at the same time point, and Respectively represent the average values ​​of these data; Based on the correlation analysis data, the synchronous pattern of physiological and facial changes is identified, and the similarity of each emotional state is determined by comparing the monitored pattern with the stored template to match the preset emotional characteristics, thereby obtaining the emotional state information.

[0014] Preferably, the steps of obtaining the environment adjustment instruction are: Based on the emotional state information, access an environment setting database associated with the emotional state, retrieve recommended parameters of the brightness level and color temperature of the lighting, and obtain recommended lighting parameters; According to the recommended lighting parameters, a matching music track is selected by matching the mood tone, and the type and intensity of fragrance known to affect the mood are determined to obtain a comprehensive environment adjustment setting; Based on the comprehensive environmental adjustment settings, values ​​and selections are assigned to each parameter, and environmental adjustment instructions are generated.

[0015] Preferably, the execution steps of the feedback module are: Based on the environment adjustment instruction, a command is sent to the lighting lamp to adjust the brightness and color temperature, and lighting adjustment feedback is obtained; According to the environment adjustment instruction, the selected music track is played on the audio player to obtain music playing feedback; According to the environment adjustment instruction, the fragrance device is activated to release the fragrance and obtain the fragrance release feedback Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the comprehensive understanding and assessment of the health status of the elderly is enhanced through the synchronous analysis of multi-dimensional data of heart rate, skin conductance and facial expressions. Rather than relying solely on any one physiological indicator, it integrates multi-source data to accurately capture and analyze the complex relationship between emotional changes and physiological responses. In this way, health warning signals can be identified in subtle changes and early warnings can be issued in a timely manner. In addition, environmental factors such as lighting, music and fragrance can be dynamically adjusted according to the real-time monitored emotions and health status to provide a more personalized and optimized environmental response, thereby improving the user's overall happiness and health. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] See also Figure 1The present invention provides a technical solution: an intelligent health monitoring system for the elderly, comprising: A heart rate module collects heart rate signals, identifies local peaks in the signals to detect heartbeats, measures the time intervals between consecutive peaks to determine the heartbeat intervals, calculates the differences between adjacent intervals to assess variability, calculates the mean and standard deviation statistics of the variability, stores the values ​​in a data set, and obtains a heart rate variability data index; Skin response module, collects skin conductance signals, records conductance values ​​at fixed time intervals, identifies changes in conductance levels by comparing consecutive readings, calculates the rate of change of conductance over time, analyzes patterns of conductance increase and decrease, calculates statistics of mean and standard deviation, stores values ​​in a data set, and obtains skin conductance data index; The expression module captures facial images, locates facial points at the corners of the eyes, eyebrows, and mouth, tracks the positions of facial points in consecutive images, calculates position differences to measure displacement and movement, quantifies the degree of activation of facial muscles, stores the measurements in a data set, and obtains facial expression data; The emotion module, based on the heart rate variability data index, skin conductance data index and facial expression data, synchronizes the data by aligning the timestamps to correspond to the same moment, calculates the correlation coefficient between the physiological data and the facial movements to analyze the correlation, identifies the synchronization pattern of physiological and facial changes, determines the similarity of each emotional state by comparing the observed pattern with the stored template to match the preset emotional features, selects the emotional state with the highest similarity, assigns calm, happy, sad or anxious labels, records the confidence level of the recognition, stores the information in the data set, and obtains the emotional state information; an adjustment module, based on the emotional state information, accesses an environment setting database associated with the emotional state, retrieves recommended parameters for the brightness level and color temperature of the lighting, selects a matching music track by matching the emotional tone, determines the type and intensity of fragrance known to affect the mood, specifies a value and selection for each parameter, and generates an environment adjustment instruction; The feedback module, based on the environmental adjustment instructions, sends commands to the lighting to adjust the brightness and color temperature, plays the selected music track on the audio player, and activates the fragrance device to release the fragrance.

[0019] The steps to obtain the heart rate variability data index are: Collecting heart rate signals, identifying local peaks in the signals to detect heartbeats, measuring time intervals between consecutive peaks to determine heartbeat intervals, and obtaining heartbeat interval data; Based on the heart beat interval data, the difference between adjacent heart beat intervals is calculated using the formula:

[0020] Calculate the heart beat interval variability and generate heart beat interval variability data, where: represents the heart beat interval measured for the i-th time, represents the heart beat interval measured at the i-1th time, represents the average value of the heart beat interval, represents the variance of the heart beat interval; Based on the heart rate variability data, the mean and standard deviation of the variability are calculated, the values ​​are stored in the data set, and the heart rate variability data index is obtained.

[0021] Specifically, the heart rate signal is collected, and the electrical signal of each heartbeat is recorded in real time through the sensor. The peak points in these signals are identified and marked as heartbeat events. The time interval between the peak of each heartbeat and the next peak is the heartbeat interval, which can reflect the rhythm and health status of the heart. The sensor used is accurate to milliseconds to ensure the accuracy of the data. Through this method, the heart rate data of an individual can be continuously monitored and recorded, providing basic data for subsequent analysis and obtaining heartbeat interval data; formula The calculation process: In the formula, and They represent the heart rate interval between two consecutive heartbeats, which are measured in real time by the acquisition equipment. In one measurement, Second, Seconds, the average of the heart beat intervals It is obtained by calculating the average value of all heart beat intervals within a certain time window, for example, Seconds, variance It is obtained by calculating the variance of the heart beat interval in the same time window, for example , then the specific calculation process of the formula is:

[0022] The results show that the variability of the heart beat interval is 0.00404, which reflects the stability of the heart beat interval. The smaller the variability, the more stable the heart rhythm. Based on the heart rate interval variability data, the heart rate interval variability values ​​of each measurement are statistically analyzed. By calculating the mean and standard deviation of these data, a comprehensive data set describing the heart health status can be obtained. The mean of the heart rate interval reflects the overall heart rate level, while the standard deviation indicates the fluctuation of the heart rate. The data is stored in a high-security database system to ensure the integrity and confidentiality of the data, and the heart rate variability data index is obtained.

[0023] The steps to obtain the skin conductance data index are: Collect skin conductance signals, record conductance values ​​at fixed time intervals, and obtain time series conductance data; Based on time series conductivity data, consecutive readings are compared to identify changes in conductivity levels using the formula:

[0024] Calculate the rate of change of conductivity over time and generate conductivity change rate data, where: represents the conductivity value measured for the ith time, represents the conductivity value measured at the i-1th time, represents the average value of the conductivity, represents the standard deviation of the conductivity value; Based on the conductivity change rate data, the mean and standard deviation of the change rate are calculated, and the values ​​are stored in the data set to obtain the skin conductance data index.

[0025] Specifically, the formula The calculation process of: Set a time point The conductivity value Siemens, previous time point The conductivity value Siemens, average value of conductance Siemens, standard deviation of conductance values Siemens.

[0026] The rate of change of conductivity is calculated as follows:

[0027] The result shows that the rate of change of the conductivity value is 0.19048, indicating that the conductivity value at this time point increased by about 19.048% compared with the previous time point.

[0028] The heart rate signal is collected to identify the local peaks in the signal to detect the heartbeats, and the time intervals between consecutive peaks are measured to determine the heartbeat intervals. The interval data is captured by using a high-precision time measurement device, where each peak is automatically marked by an electrocardiogram (ECG) device, and the obtained heartbeat interval data can be used for subsequent heart rate variability analysis. The heartbeat interval data is obtained for each heartbeat signal captured by the electrocardiogram technology.

[0029] Based on the heart rate interval variability data, the mean and standard deviation of the variability are calculated. These calculations are implemented through mathematical statistical software, where the statistical software uses the stored heart rate interval data and applies statistical functions to directly calculate the mean and standard deviation of the heart rate variability to obtain the heart rate variability data index.

[0030] The steps to obtain facial expression data are: Capture facial images, locate facial points at the corners of eyes, eyebrows and mouth, track the position changes of facial points in continuous images, and obtain facial point position data; Based on the facial point position data, the position difference is calculated to measure displacement and movement, using the formula:

[0031] Calculate the activation degree of facial muscles and generate facial muscle activation data, where: and Represent the x and y coordinates of a specific facial point at the i-th measurement, and Respectively represent the x and y coordinates of the point at the i-1th measurement, represents the average of all facial point position differences; Based on the facial muscle activation data, the statistics of the activation degree are calculated, the values ​​are stored in the data set, and the facial expression data is obtained.

[0032] Specifically, when capturing facial images, high-precision cameras are used to collect images in a controlled environment, and facial key points, such as the corners of the eyes, eyebrows, and corners of the mouth, are detected and marked in real time. The data of these key points provide a detailed record of facial movements under different expressions and emotional states, and the position changes of these facial points in continuous images are analyzed. The processing time for each frame of the image does not exceed 10 milliseconds, ensuring efficient data transmission and processing between continuous images, and obtaining facial point position data. This data can accurately reflect the dynamic process of changes in individual facial expressions, thereby providing basic data for subsequent expression analysis.

[0033] formula The calculation process: Facial expression changes through facial point position differences To determine, and For the The x and y coordinates of a point at the time of the first image capture, and For the The coordinates of the second.

[0034] In a measurement , , , , the average of the position differences is the average value of the position difference in multiple measurements, for example, 0.5, then the specific value in the formula is calculated as follows:

[0035] This calculation shows that the activity of facial muscles in this measurement resulted in a position difference of approximately 5.83, which is a method that can quantify the actual movement distance of facial points after each expression change.

[0036] Based on facial muscle activation data, through continuous analysis of facial expression images at different time points, the degree of facial muscle activation at each time point is calculated, and statistical analysis tools such as standard deviation and mean value are used to provide a quantitative expression level for each expression. These data are stored in a database for long-term tracking and analysis. This method can be used to observe the regularity of expression changes and abnormal reactions to sudden events, and obtain facial expression data. These data help researchers understand the relationship between emotional state and expression, and provide important experimental data for psychology and human-computer interaction research.

[0037] The steps to obtain emotional state information are: Based on the heart rate variability data index, skin conductance data index and facial expression data, synchronize the data by aligning the timestamps to correspond to the same moment, and obtain synchronized physiological and facial data; Based on the synchronized physiological and facial data, the correlation coefficient between the physiological data and the facial movement is calculated using the formula:

[0038] Analyze the correlation and generate correlation analysis data, where: and Respectively represent the values ​​of physiological data and facial data measured at the same time point, and Respectively represent the average values ​​of these data; Based on the correlation analysis data, the synchronous patterns of physiological and facial changes are identified, and the similarity of each emotional state is determined by comparing the monitored patterns with the stored templates to match the preset emotional features, thus obtaining the emotional state information.

[0039] Specifically, synchronization of physiological and facial data is achieved by aligning the timestamps of the heart rate variability data index, skin conductance data index and facial expression data. This process ensures the precise synchronization of data corresponding to the same moment. Heart rate data is continuously collected at fixed intervals by an electrocardiogram device, and the heart rate peak and interval duration are automatically calculated by an algorithm. The skin conductance data index is obtained by detecting changes in skin resistance through sensors. Facial expression data is obtained by capturing facial images with a high-resolution camera and analyzing key facial points through facial recognition software. After being recorded, these data are marked with a unified timestamp, ensuring that during integrated analysis, the data can be compared and associated in the same time frame to obtain precisely aligned physiological and facial data.

[0040] formula The calculation process: in and Represent the specific values ​​of physiological data and facial data at the same time point, and are the average values ​​of these data respectively. For example, at a certain time point, the physiological data and facial data are: X=[85,90,88,87,85], Y=[0.5,0.6,0.55,0.58,0.57], then the average value and Then substitute it into the formula for calculation. After specific calculation, the correlation coefficient C is obtained. The closer the value is to 1 or -1, the stronger the linear correlation between the two data is, reflecting the synchronization pattern of physiological and facial changes.

[0041] The acquisition of emotional state information involves comparing the heart rate variability data index, skin conductance data index and facial expression data with preset emotional templates. The heart rate variability data index evaluates the irregularity of the heart rate by calculating the difference in the interval time between each heartbeat. The skin conductance data index reflects the level of excitement monitored by changes in skin resistance. The facial expression data captures facial movements related to emotional changes. The three types of data are synchronized through an algorithm and their correlation is calculated. The obtained pattern is matched with the emotional expression template stored in the database to determine the similarity of the emotional state. The emotional labels are classified according to the highest similarity to obtain the final emotional state information, such as happy, sad and other labels.

[0042] The steps to obtain the environmental adjustment instructions are: Based on the emotional state information, access an environment setting database associated with the emotional state, retrieve recommended parameters of the brightness level and color temperature of the lighting, and obtain recommended lighting parameters; Based on the recommended lighting parameters, matching music tracks are selected by matching the mood tone, and the type and intensity of fragrances known to affect mood are determined to obtain a comprehensive environment adjustment setting; Based on the comprehensive environment adjustment settings, the environment adjustment instructions are generated by specifying values ​​and selections for each parameter.

[0043] Specifically, the recommended lighting parameters are obtained by accessing the environment setting database associated with the emotional state. By refining the data retrieval process, the environment setting database is first connected, which stores the environment adjustment parameters preset according to different emotional states, including lighting brightness and color temperature. Each emotional state such as calm, happy, sad or anxious has a corresponding lighting setting. During the retrieval process, according to the input emotional state information, such as "happy", the database query returns the lighting brightness and color temperature that matches the "happy" emotion, for example, the brightness is 500lx and the color temperature is 5500K. These parameters are the average optimized values ​​obtained by long-term collection of user feedback and experimental data, and are adjusted by comparing the comfort feedback of users in different emotional states to obtain the recommended lighting parameters.

[0044] According to the obtained lighting parameters, matching music tracks and fragrance types are selected. The selection of music tracks and fragrances is also achieved through similar database queries. This database not only includes lighting parameters, but also integrates music tracks and fragrance types. The recommended music and fragrance types for each emotional state are also obtained through historical data analysis. For example, for the "happy" state, lively music and citrus fragrances may be recommended. The music database is classified according to the emotional impact of the tracks, and the fragrance database records the research data on the psychological and physiological effects of different fragrances on people. After integrating this information, a set of environmental adjustment settings will be output, including the selected music tracks and fragrance intensity. For example, "Ode to Joy" and a medium-intensity citrus fragrance are selected to obtain a comprehensive environmental adjustment setting.

[0045] Based on the comprehensive environmental adjustment settings, specific environmental adjustment instructions are generated. This step involves converting the environmental adjustment settings into executable control instructions. First, the system adjusts the lighting system according to the acquired lighting parameters, sets the brightness and color temperature, for example, adjusts the brightness to 500lx and the color temperature to 5500K. The music player plays according to the selected track settings, and the fragrance device releases fragrance according to the selected fragrance type and intensity. Finally, the generated environmental adjustment instructions ensure that the adjusted environment can match the user's emotional state, providing a comfortable and beneficial living or working environment, and generating environmental adjustment instructions.

[0046] The execution steps of the feedback module are: Based on the environmental adjustment instructions, send commands to the lighting to adjust the brightness and color temperature, and get lighting adjustment feedback; According to the environment adjustment instructions, the selected music track is played on the audio player to obtain music playback feedback; According to the environmental adjustment instructions, the fragrance device is activated to release the fragrance and obtain the fragrance release feedback.

[0047] Specifically, based on the transmission of the environment adjustment instruction, the lighting adjustment instruction received by the environment adjustment system includes specific brightness and color temperature settings, which are optimization parameters associated with the emotional state information retrieved from the database. The lighting system adjusts the light according to these parameters, controls the dimmer through the PWM signal, adjusts the output of voltage and current, thereby changing the light brightness and color temperature, detects the actual brightness and color temperature through sensor feedback to ensure consistency with the instruction, and confirms the accuracy of the lighting adjustment through system feedback. The lighting adjustment feedback obtained ensures that the ambient light matches the preset emotional state; The received music playback instruction specifically includes the path and playback settings of the music file. The audio playback system searches the music library based on this information, finds the corresponding music file, parses the music file format through the decoder, converts the digital signal into an analog signal, amplifies the audio signal through the loudspeaker, and drives the speaker to play the music. The volume and playback order are optimized and adjusted by the control system according to the emotion adjustment instruction. At the same time, the music playback status is monitored in real time to ensure that the playback is correct, and feedback is given on the music playback effect. The music playback feedback obtained shows that the music is coordinated with the environmental settings, which enhances the emotional atmosphere of the space. The release instructions received by the fragrance device specify the fragrance type and release intensity in detail. The control system inside the device adjusts the pumping rate and atomization intensity of the fragrance liquid according to the instructions, converts the fragrance liquid into gas through the atomizer, and diffuses it into the environment. The indoor fragrance concentration is monitored by the environmental sensor to ensure that the release amount meets the preset parameters. The release intensity is adjusted in real time to adapt to different spaces and ventilation conditions. The fragrance release feedback system ensures that the odor is evenly distributed and maintains a continuous fragrance experience. The fragrance release feedback obtained shows that the environmental fragrance is synchronized with the emotion regulation needs.

Claims

1. An intelligent health monitoring system for the elderly, characterized in that: The system comprises: A heart rate module collects heart rate signals, identifies local peaks in the signals to detect heartbeats, measures the time intervals between consecutive peaks to determine the heartbeat intervals, calculates the differences between adjacent intervals to assess variability, calculates the mean and standard deviation statistics of the variability, stores the values ​​in a data set, and obtains a heart rate variability data index; The skin response module collects skin conductance signals, records conductance values ​​at fixed time intervals, stores the values ​​in a data set, and obtains a skin conductance data index; The expression module captures facial images, locates facial points at the corners of the eyes, eyebrows, and mouth, tracks the positions of facial points in consecutive images, calculates position differences to measure displacement and movement, quantifies the degree of activation of facial muscles, stores the measurements in a data set, and obtains facial expression data; The emotion module, based on the heart rate variability data index, skin conductance data index and facial expression data, aligns the timestamp synchronization data to correspond to the same moment, calculates the correlation coefficient between the physiological data and the facial movement to analyze the correlation, identifies the synchronization pattern of physiological and facial changes, and obtains the emotional state information; an adjustment module, based on the emotional state information, accessing an environment setting database associated with the emotional state, retrieving recommended parameters for brightness level and color temperature of the lighting, assigning values ​​and selections to each parameter, and generating an environment adjustment instruction; The feedback module sends commands to the lighting to adjust the brightness and color temperature and control the audio player and fragrance equipment based on the environmental adjustment instructions.

2. The intelligent elderly health monitoring system according to claim 1 is characterized in that: The steps for obtaining the heart rate variability data index are: Collecting heart rate signals, identifying local peaks in the signals to detect heartbeats, measuring time intervals between consecutive peaks to determine heartbeat intervals, and obtaining heartbeat interval data; Based on the heart beat interval data, the difference between adjacent heart beat intervals is calculated using the formula: ； Calculate the heart beat interval variability and generate heart beat interval variability data, where: represents the heart beat interval measured at the i-th time, represents the heart beat interval measured at the i-1th time, represents the average value of the heart beat interval, represents the variance of the heart beat interval; Based on the heart rate interval variability data, the mean and standard deviation of the variability are calculated, and the values ​​are stored in a data set to obtain a heart rate variability data index.

3. The intelligent elderly health monitoring system according to claim 1 is characterized in that: The steps for obtaining the skin conductance data index are: Collect skin conductance signals, record conductance values ​​at fixed time intervals, and obtain time series conductance data; Based on the time series conductance data, consecutive readings are compared to identify changes in conductance levels using the formula: ； Calculate the rate of change of conductivity over time and generate conductivity change rate data, where: represents the conductivity value measured for the ith time, represents the conductivity value measured at the i-1th time, represents the average value of the conductivity, represents the standard deviation of the conductivity value; Based on the conductivity change rate data, the mean and standard deviation of the change rate are calculated, and the values ​​are stored in the data set to obtain the skin conductance data index.

4. The intelligent elderly health monitoring system according to claim 1 is characterized in that: The steps for acquiring the facial expression data are: Capture facial images, locate facial points at the corners of eyes, eyebrows and mouth, track the position changes of facial points in continuous images, and obtain facial point position data; Based on the facial point position data, position differences are calculated to measure displacement and motion, using the formula: ； Calculate the activation degree of facial muscles and generate facial muscle activation data, where: and Represent the x and y coordinates of a specific facial point at the i-th measurement, and Respectively represent the x and y coordinates of the point at the i-1th measurement, represents the average of all facial point position differences; Based on the facial muscle activation data, the statistics of the activation degree are calculated, the values ​​are stored in the data set, and the facial expression data is obtained.

5. The intelligent elderly health monitoring system according to claim 1 is characterized in that: The steps for acquiring the emotional state information are: Based on the heart rate variability data index, the skin conductance data index and the facial expression data, aligning the timestamp synchronization data to correspond to the same moment, thereby obtaining synchronized physiological and facial data; Based on the synchronized physiological and facial data, a correlation coefficient between the physiological data and the facial movement is calculated using the formula: ； Analyze the correlation and generate correlation analysis data, where: and Respectively represent the values ​​of physiological data and facial data measured at the same time point, and Respectively represent the average values ​​of these data; Based on the correlation analysis data, the synchronous pattern of physiological and facial changes is identified, and the similarity of each emotional state is determined by comparing the monitored pattern with the stored template to match the preset emotional characteristics, thereby obtaining the emotional state information.

6. The intelligent elderly health monitoring system according to claim 1 is characterized in that: The steps of obtaining the environment adjustment instruction are as follows: Based on the emotional state information, access an environment setting database associated with the emotional state, retrieve recommended parameters of the brightness level and color temperature of the lighting, and obtain recommended lighting parameters; According to the recommended lighting parameters, a matching music track is selected by matching the mood tone, and the type and intensity of fragrance known to affect the mood are determined to obtain a comprehensive environment adjustment setting; Based on the comprehensive environmental adjustment settings, values ​​and selections are assigned to each parameter, and environmental adjustment instructions are generated.

7. The intelligent elderly health monitoring system according to claim 1 is characterized in that: The execution steps of the feedback module are: Based on the environment adjustment instruction, a command is sent to the lighting lamp to adjust the brightness and color temperature, and lighting adjustment feedback is obtained; According to the environment adjustment instruction, the selected music track is played on the audio player to obtain music playing feedback; According to the environment adjustment instruction, the fragrance device is activated to release the fragrance, and the fragrance release feedback is obtained.

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