Omnibearing remote monitoring system

Abstract

The invention relates to the technical field of medical health, in particular to an omnibearing remote monitoring system which comprises a monitoring equipment module, a data transmission module, a psychological behavior analysis module and a remote intervention and treatment module. The monitoring equipment module is used for monitoring the physiological health state and the psychological state of the psychological disease patient. Through mutual cooperation of the monitoring equipment module, the data transmission module, the psychological behavior analysis module and the remote intervention and treatment module, the purposes of remotely seeing a doctor and monitoring the physiological health state of a patient in real time are achieved, and the overall health state of the patient is systematically and comprehensively evaluated by monitoring the condition of the patient in real time; comprehensive and accurate patient information is provided for doctors, the doctors know the change of the state of illness of the patients, resources are effectively utilized by medical institutions through remote intervention and treatment, the overall medical cost is reduced, popularization and development of mental health are promoted, and data safety and privacy protection are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of medical health technology, and specifically relates to an all-round remote monitoring system. Background Art

[0002] Mental health refers to people's efficient and happy adaptation to the environment, or the ability to happily accept the norms of life. In recent years, mental health problems have shown a trend of universality and younger age worldwide. Depression refers to significant and persistent low mood that lasts for more than 2 weeks, manifested as a low mood that is inconsistent with the surrounding environment, ranging from melancholy to grief, and even some stupor, hallucinations, delusions and psychotic symptoms. Traditional mental illness monitoring methods rely on patients' active medical treatment and face-to-face consultation and treatment. This model has many limitations. Patients may delay medical treatment for various reasons, and lack continuous and effective monitoring and support during the treatment process. In this regard, we propose a comprehensive remote monitoring system. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides an all-round remote monitoring system to solve the above technical problems.

[0004] To achieve the above object, the present invention provides the following technical solutions: an all-round remote monitoring system, the remote monitoring system comprising: a monitoring device module, a data transmission module, a psychological behavior analysis module and a remote intervention and treatment module;

[0005] The monitoring device module is used to monitor the physiological health status and psychological status of patients with mental illness;

[0006] The data transmission module is used to encrypt and transmit the data obtained by the monitoring device module at high speed;

[0007] The psychological behavior analysis module is used to analyze the data transmitted by the data transmission module and evaluate the health of patients with mental illness;

[0008] The remote intervention and treatment module is used to provide online psychological counseling and treatment services, and to provide free learning and educational resources and an online patient communication community for patients with mental illness.

[0009] Preferably, the monitoring device module includes a wearable monitoring unit, a behavior recognition unit, and a psychological condition assessment unit;

[0010] The wearable monitoring unit monitors and obtains the patient's vital signs in real time, including heart rate, blood pressure, respiratory rate, stress and sleep quality;

[0011] The behavior recognition unit obtains the patient's daily behavior data through image information, and analyzes the patient's behavior in combination with the psychological behavior analysis module to determine whether there is any abnormality;

[0012] The psychological status assessment unit regularly assesses the patient's psychological status using a psychological measurement form.

[0013] Preferably, the behavior recognition unit implements the steps of analyzing and distinguishing abnormalities in behavior as follows:

[0014] A1. The behavior recognition unit captures the patient's behavior patterns, facial expression changes and voice information in real time;

[0015] A2. Clean, denoise and remove redundancy from video data, perform face detection on video data, extract facial expression areas, and perform speech separation and denoising on audio data;

[0016] A3. Use the psychological behavior analysis module to extract behavioral features, compare the features with the information in the behavior pattern library, identify the patient's behavior pattern, and determine whether the behavior is abnormal based on the abnormal behavior criteria;

[0017] Based on the correspondence between facial expressions and emotions, psychological behavioral analysis is used to extract key expression features, and algorithms are used to classify the extracted features to identify the patient's emotional state. Combined with the facial expression recognition results, algorithms are used to analyze the patient's voice emotions. Based on the recognition results, combined with the patient's psychological state and background information, it is determined whether there is any abnormality in the emotions.

[0018] Preferably, the detailed steps of video and audio data processing in A2 are:

[0019] (1) Video data processing

[0020] B1. Convert the video data format to AVI format and decompose it into individual frames. Use the image similarity algorithm to delete duplicate and highly similar frames.

[0021] B2. Use Gaussian filtering technology to remove random noise in the video, and use the temporal correlation between video frames to remove noise through inter-frame differences;

[0022] B3. Use Haar feature + AdaBoost algorithm to detect human faces in video frames, locate facial features in the faces, and extract key areas containing facial expressions based on the locations of facial features;

[0023] (2) Audio data processing

[0024] C1. Separate human voice and background sound through noise reduction and termination channel extraction tools;

[0025] C2, use spectral subtraction to remove noise from audio;

[0026] The Gaussian filtering technology, Haar feature + AdaBoost algorithm and spectral subtraction mentioned above are all existing technologies and will not be elaborated here.

[0027] Preferably, the analysis method in step A3 is divided into two categories:

[0028] (1) Behavioral feature extraction and pattern recognition

[0029] D1. Based on the principles of psychology and behavior, select key indicators that reflect individual behavior characteristics from the processed data, including movement frequency, movement amplitude, activity range, and social interaction patterns;

[0030] D2. Extract behavioral features using computer vision technology;

[0031] D3. Build a behavior pattern library based on a large amount of behavior data, use pattern matching methods to compare the extracted behavior features with the information in the behavior pattern library, and identify the most similar behavior patterns;

[0032] D4. Based on the comparison results and the abnormal behavior criteria, determine whether the behavior is abnormal;

[0033] (2) Facial expression and emotion recognition

[0034] E1. Extract static and dynamic features of the face from the processed data using a convolutional neural network;

[0035] E2. Establish the correspondence between facial expressions and emotions based on psychology and neuroscience, use classification algorithms to classify the extracted expression features, and identify the corresponding emotional states;

[0036] E3, extract acoustic and linguistic features from speech, combine with facial expression recognition results, combine visual and auditory information, improve the accuracy of emotion recognition, and analyze speech features to identify the patient's speech emotions;

[0037] E4. Comprehensively analyze the emotion recognition results based on the patient's psychological state and background information to determine whether the emotions are abnormal;

[0038] The computer vision technology, convolutional neural network and classification algorithm mentioned above are all existing technologies and will not be elaborated here.

[0039] Preferably, the psychological behavior analysis module evaluates the patient's physical and mental health with the following factors and weights:

[0040] F1, physiological indicators account for 40% of the total weight, consisting of heart rate 10%, blood pressure 10%, respiratory rate 5%, sleep quality 10%, and stress response 5%;

[0041] F2, behavioral characteristics account for 30% of the total weight, composed of daily activity 5%, social interaction pattern 5%, abnormal behavior frequency 15%, and life regularity 5%;

[0042] F3. Psychological state accounts for 30% of the total weight, which is composed of anxiety level 10%, depression level 10%, coping mechanism 5%, and psychological resilience 5%.

[0043] Preferably, the analysis module divides the patient's physical and mental health assessment criteria into:

[0044] G1, Very healthy, 90-100 points, the patient's physical and mental health is excellent, physiological indicators are stable within the normal range, behavioral characteristics are normal, there is no abnormal behavior or emotional expression, the psychological state is positive and healthy, and there is no obvious anxiety or depression;

[0045] G2, healthy, 75-89 points, the patient is in good physical and mental health, physiological indicators are within the normal range most of the time, with occasional slight fluctuations, behavioral characteristics are basically normal, with occasional slight abnormalities but not affecting overall health, the psychological state is relatively stable, with occasional mild anxiety or depression but able to self-regulate;

[0046] G3, sub-health, 60-74 points, the patient's physical and mental health is in a sub-healthy state, physiological indicators have obvious fluctuations or mild abnormalities, and behavioral characteristics have many abnormalities, including reduced social interaction, reduced range of activities, unstable mental state, anxiety, depression and psychological problems. The psychological problems are more obvious and require regular psychological support and intervention;

[0047] G4, unhealthy, 0-59 points, the patient's physical and mental health is poor, there are serious abnormalities in physiological indicators, accompanied by multiple physiological diseases, and significantly abnormal behavioral characteristics or even dangerous behaviors, including frequent self-harm and attacks on others. The mental state is extremely unstable, and anxiety and depression are serious psychological problems, requiring urgent psychotherapy and medical intervention.

[0048] Preferably, the remote intervention and treatment module includes an online diagnosis and treatment unit and a mobile device unit;

[0049] The online diagnosis and treatment unit provides targeted remote treatment to patients based on the scores of the psychological behavior analysis module, and determines the time and frequency of online diagnosis and treatment based on the scores;

[0050] The mobile device unit has the ability to access the online diagnosis and treatment unit. It also provides a large number of free learning and educational resources related to mental illness. The patient communication community can be accessed online through the mobile device unit. The mobile device unit also has a guardian mode that can remotely view the patient's real-time status.

[0051] Preferably, when it is monitored that the patient's physiological indicators are below the life safety red line or dangerous behavior occurs, the emergency response mechanism will be triggered, and an alarm message will be automatically sent to the emergency contact, and the nearest medical institution will be notified for rescue.

[0052] Preferably, the remote intervention and treatment module will recommend personalized treatment plans and rehabilitation plans to the patient based on the evaluation results of the psychological behavioral analysis module and the patient's historical treatment records, and implement them in the remote intervention and treatment module.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] This application achieves the purpose of remote medical treatment and real-time monitoring of the patient's physiological health status through the mutual cooperation of the monitoring equipment module, the data transmission module, the psychological behavior analysis module and the remote intervention and treatment module. By real-time monitoring of the patient's condition, the system comprehensively evaluates the patient's overall health status and provides doctors with comprehensive and accurate patient information. By real-time transmission of monitoring data, doctors can immediately understand changes in the patient's condition and quickly adjust the treatment plan according to actual conditions. The instant feedback mechanism greatly shortens the treatment response time and improves the treatment efficiency and effectiveness. The system provides convenience for doctors and also enhances the patient's self-management ability. Patients understand their physical and mental conditions through the system, and self-regulate and manage according to the doctor's guidance, which improves the initiative and compliance of treatment and reduces the burden on medical institutions. Through remote intervention and treatment, medical institutions effectively utilize resources, reduce overall medical costs, promote the popularization and development of mental health, and ensure data security and privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is the framework diagram of this all-round remote monitoring system;

[0056] Figure 2 This is the module structure diagram of the monitoring equipment of the remote monitoring system;

[0057] Figure 3 This is the structure diagram of the remote intervention and treatment module of the remote monitoring system;

[0058] Figure 4 This is the abnormality identification flow chart of the behavior analysis of the remote monitoring system;

[0059] Figure 5 This is a flow chart of Example 1;

[0060] Figure 6 This is a flow chart of Example 2. DETAILED DESCRIPTION

[0061] 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.

[0062] The present invention provides a technical solution: an all-round remote monitoring system, the remote monitoring system comprising: a monitoring device module, a data transmission module, a psychological behavior analysis module and a remote intervention and treatment module;

[0063] As the front-end perception layer of the entire system, the monitoring device module integrates advanced biosensors, wearable devices and emotion recognition technology to monitor the physiological health and psychological state of patients with mental illness, including continuous tracking of heart rate, blood pressure, sleep quality, and skin conductance physiological indicators. Through facial expression analysis and voice and tone recognition technology, it accurately captures the patient's emotional changes and psychological state, such as anxiety, depression, and stress levels, providing a rich and accurate data basis for subsequent in-depth analysis;

[0064] The data transmission module is used to encrypt and transmit the data acquired by the monitoring device module at high speed, ensuring the security and real-time performance of the sensitive data collected by the monitoring device module during transmission;

[0065] The psychological behavior analysis module is the core processing unit of the system, which is used to analyze the data transmitted by the data transmission module. This module can not only predict the trend of the patient's physiological indicators and assess the health risks, but also combine the patient's emotional changes, daily behavior patterns and multi-dimensional information to assess the health of patients with mental illnesses. By comparing the standard database and the expert knowledge base, the system can automatically identify the patient's psychological problems and provide a scientific basis for subsequent intervention and treatment.

[0066] The remote intervention and treatment module is the system's direct service window for patients, used to provide online psychological counseling and treatment services. Patients can use this module to make appointments with professional psychological counselors for video consultations, and receive one-on-one professional advice and emotional support. It also provides free learning and education resources for patients with mental illness, covering basic psychology knowledge, self-regulation skills, and emotional management content, as well as an active online patient communication community to promote experience sharing and emotional support among patients, creating a positive recovery atmosphere.

[0067] Further, the monitoring device module includes a wearable monitoring unit, a behavior recognition unit, and a psychological condition assessment unit;

[0068] The wearable monitoring unit monitors and obtains the patient's vital signs in real time. It integrates high-precision sensor technology and can continuously and non-interferingly monitor and record the patient's vital signs, including heart rate, blood pressure, respiratory rate, and can sense the patient's physical tension, providing a physiological basis for assessing their psychological stress level and sleep quality. By analyzing the patient's sleep cycle, deep sleep time, and REM period ratio, the key indicators, the sleep quality of the patient is comprehensively assessed, which is then related to their mental health status.

[0069] The behavior recognition unit obtains the patient's daily behavior data through image information. The data includes the patient's body movement trajectory, posture changes, facial expressions, and subtle gestures. Combined with the psychological behavior analysis module, the patient's behavior is analyzed, such as whether there are frequent anxious behaviors and whether there are abnormalities in social avoidance behaviors under depression symptoms.

[0070] The psychological condition assessment unit regularly assesses the patient's mental state through psychological measurement scales, including the self-rating depression scale, the self-rating anxiety scale, and the life satisfaction scale. They cover multiple dimensions of emotion, cognition, behavior, and sociality, and can comprehensively reflect the patient's mental health status.

[0071] Furthermore, the behavior recognition unit implements the steps of analyzing and distinguishing abnormal behaviors:

[0072] A1. The behavior recognition unit captures the patient's behavior patterns, facial expression changes and voice information in real time, and records the patient's daily activity scenes in real time;

[0073] A2. After obtaining the original video and audio data, we first perform preprocessing work. We use image enhancement technology to clean, denoise and remove redundancy on the video data, use face detection technology to automatically locate and track the patient's face area, extract the facial expression area, and use voice separation technology to separate and denoise the patient's voice on the audio data to reduce interference factors, laying the foundation for subsequent emotion analysis.

[0074] A3. Use the psychological behavior analysis module to extract behavioral features and compare the features with the information in the behavior pattern library to identify the patient's behavior pattern. The behavior pattern library contains sample data of various normal and abnormal behavior patterns. The classification model obtained through training can identify whether the patient's current behavior pattern belongs to the known abnormal behavior category and judge whether the behavior is abnormal according to the abnormal behavior standard.

[0075] According to the correspondence between facial expressions and emotions, psychological behavioral analysis is used to extract key expression features, and emotional computing technology is used to identify the patient's emotions, classify the extracted features, and identify the patient's emotional state. Combined with the facial expression recognition results, the patient's voice emotions are analyzed using algorithms, and the patient's emotional changes are judged through the tone, rhythm, and speed of the voice. Based on the recognition results, combined with the patient's psychological state and background information, it is judged whether the emotions are abnormal.

[0076] Furthermore, the detailed steps of video and audio data processing in A2 are as follows:

[0077] (1) Video data processing

[0078] B1. Convert the video data format to AVI format, which is a widely used video packaging format with good compatibility and compression efficiency, and decompose it into separate frames. In order to reduce the amount of calculation and storage requirements, the system uses an image similarity algorithm to compare the frames and delete those repeated or highly similar frames. This is achieved by calculating the pixel differences or feature differences between adjacent frames, effectively removing redundant information in the video;

[0079] B2. Video data is often interfered by various noises during the acquisition and transmission process, such as electronic noise and ambient light changes. In order to improve the video quality, Gaussian filtering technology is used to remove random noise in the video. Gaussian filtering is a linear smoothing filter that is suitable for removing noise that obeys normal distribution. The influence of noise is reduced by weighted averaging each pixel in the video frame and the pixels in its neighborhood. The temporal correlation between video frames is used to remove noise through inter-frame difference. The inter-frame difference method identifies the motion area by calculating the pixel difference between adjacent frames, and considers that the pixel changes in the non-motion area are mainly caused by noise, so these areas are smoothed.

[0080] B3. Use Haar feature + AdaBoost algorithm to detect faces in video frames. Haar feature is a simple rectangular feature that can reflect the grayscale changes of an image. AdaBoost algorithm is an ensemble learning method that improves detection performance by iteratively training multiple weak classifiers and combining them into a strong classifier. After detecting a face, it locates the facial features in the face and extracts the key area containing facial expressions based on the location of the facial features.

[0081] (2) Audio data processing

[0082] C1. Audio data often contains multiple sound components such as human voice and background sound during collection. Human voice and background sound are separated by noise reduction and end channel extraction tools. Noise reduction technology improves the signal-to-noise ratio by suppressing the noise components in the audio signal, while the end channel extraction tool uses the dual-channel characteristics of the audio signal to separate the human voice signal by calculating the difference between the channels, which helps to reduce the interference of background noise on the emotion analysis results.

[0083] C2. Use spectral subtraction to remove noise from audio. Spectral subtraction is a noise suppression technology based on frequency domain processing. It first estimates the spectral characteristics of noise, then subtracts the estimated noise spectrum from the spectrum of the speech signal to obtain a pure speech spectrum. By reducing the spectral energy of noise, the clarity and intelligibility of the speech signal are improved, providing higher quality audio data for subsequent emotion analysis.

[0084] The Gaussian filtering technology, Haar feature + AdaBoost algorithm and spectral subtraction mentioned above are all existing technologies and will not be elaborated here.

[0085] Furthermore, the analysis methods in step A3 are divided into two categories:

[0086] (1) Behavioral feature extraction and pattern recognition

[0087] D1. According to the principles of psychology and behavior, select key indicators that reflect individual behavioral characteristics from the processed data, including movement frequency, movement amplitude, range of activity, and social interaction patterns. The selection of these indicators should be based on in-depth behavioral research to ensure that they can fully and accurately reflect the patient's behavioral characteristics;

[0088] D2. Use computer vision technology to extract behavioral features. Computer vision technology includes image processing, video analysis, feature detection and tracking. Use image processing and video analysis technology to identify and process patient behaviors in video frames. By applying edge detection, corner detection, and optical flow methods, accurately extract the patient's movement trajectory and posture changes;

[0089] D3. In order to identify the patient's behavior pattern, it is necessary to build a behavior pattern library containing a large number of normal and abnormal behavior pattern samples. The pattern library is built by collecting and analyzing a large number of patients' behavior data. The extracted behavior features are compared with the information in the behavior pattern library using the pattern matching method, and the most similar behavior pattern is identified using similarity calculation and cluster analysis methods.

[0090] D4. Based on the comparison results and combined with abnormal behavior standards, determine whether the behavior is abnormal. Abnormal behavior standards are usually formulated based on clinical experience and psychological research, including excessively high or low movement frequency, abnormal movement amplitude, limited or excessive expansion of activity range, and lack or abnormal social interaction patterns;

[0091] (2) Facial expression and emotion recognition

[0092] E1. Use convolutional neural networks to extract static and dynamic features of the face from the processed data. Static features mainly include facial texture, shape, and color information, while dynamic features focus on the changing process of facial expressions. These features can fully and carefully describe the patient's facial expressions.

[0093] E2. Establish the correspondence between facial expressions and emotions based on psychology and neuroscience, use classification algorithms to classify the extracted expression features, and identify the corresponding emotional states, which include happiness, sadness, anger, surprise, fear, and more complex emotional combinations;

[0094] E3. In addition to facial expressions, speech is also an important carrier for expressing emotions. We extract acoustic and linguistic features from speech, combine them with the results of facial expression recognition, and combine visual and auditory information to further improve the accuracy and robustness of emotion recognition. We can analyze speech features to identify the patient's speech emotions.

[0095] E4. Comprehensively analyze the emotion recognition results based on the patient's psychological state and background information. The psychological state includes the patient's personality traits and emotional stability, and the background information includes the patient's medical history, living environment, and social relationships, to determine whether the emotions are abnormal;

[0096] The computer vision technology, convolutional neural network and classification algorithm mentioned above are all existing technologies and will not be elaborated here.

[0097] Furthermore, the psychological behavior analysis module evaluates the patient's physical and mental health and the weights are as follows:

[0098] F1. Physiological indicators account for 40% of the total weight. Physiological indicators are the basis for measuring the patient's physical health and are crucial for assessing the patient's overall physical and mental health. They are composed of heart rate 10%. The normal heart rate range can explain the stability and efficiency of heart function. Too fast or too slow a heart rate may indicate potential health problems. Blood pressure 10%. Hypertension is a risk factor for many cardiovascular diseases, and low blood pressure may also cause dizziness and fatigue symptoms. Respiratory rate 5%. Normal respiratory rate can ensure that the body gets enough oxygen and discharges carbon dioxide. Abnormal respiratory rate may indicate problems with the respiratory system. Sleep quality 10%. Sleep quality is directly related to the patient's ability to rest and recover. Good sleep quality helps maintain the immune system, memory and emotional stability, while sleep problems, such as insomnia and sleep apnea, will seriously affect the patient's physical and mental health. Stress response 5%. Stress response refers to the physiological changes that occur when the body faces stress, such as increased cortisol levels. Moderate stress response helps individuals cope with challenges, but long-term excessive stress may lead to a variety of health problems, including cardiovascular disease and immune system disorders.

[0099] F2. Behavioral characteristics account for 30% of the total weight. Behavioral characteristics are important aspects of evaluating patients' daily living habits and behavior patterns. They can reflect patients' activity levels, social interaction abilities, and life regularity. They are composed of 5% daily activity, which refers to the level of physical activity in patients' daily lives. Appropriate physical activity helps maintain physical health, enhance cardiopulmonary function, and improve psychological state, while long-term lack of activity may lead to obesity, cardiovascular disease, and muscle atrophy. Social interaction patterns account for 5% of the total weight. Social interaction patterns refer to the way and frequency of patients' interactions with others. Good social interactions can enhance individuals' sense of belonging, happiness, and mental health. Social isolation or poor social interaction patterns may lead to loneliness, depression and anxiety. Abnormal behavior frequency is 15%. Abnormal behavior frequency refers to the number of times the patient exhibits unconventional or harmful behaviors. These behaviors may include self-harm, aggressive behavior, excessive dependence on drugs or alcohol. The frequent occurrence of abnormal behaviors often indicates that the patient has serious mental health problems or needs emergency intervention. Regularity of life is 5%. Regularity of life refers to the orderliness and regularity of the patient's daily life. Maintaining a regular lifestyle is helpful to maintain physical health, improve work efficiency and improve mental state, while irregular life may lead to biological clock disorders, fatigue and mood swings.

[0100] F3. Psychological state accounts for 30% of the total weight. Psychological state is the core aspect of assessing the patient's mental health. It covers the patient's emotional state, coping ability and psychological resilience. It is composed of anxiety level 10%. Anxiety is a common emotional response. Moderate anxiety helps individuals cope with challenges and pressure, but excessive anxiety may seriously affect the patient's daily life and mental health. The level of anxiety is measured through scale assessment, clinical observation or patient self-report. Depression level 10%. Depression is a serious mental health problem, manifested as low mood, loss of interest, and decreased energy. The level of depression is directly related to the patient's mental health and quality of life. It is measured through scale assessment, clinical observation or professional diagnosis. Accurately assess the patient's degree of depression, coping mechanism 5%, coping mechanism refers to the coping strategies and methods adopted by individuals when facing stress or challenges. Effective coping mechanisms help reduce stress, relieve anxiety and depression, and promote the individual's mental health recovery. Assess the patient's coping mechanism to understand his attitude and behavior when facing difficulties, and provide corresponding support and guidance. Psychological resilience 5%, psychological resilience refers to the ability of an individual to recover and maintain a good mental state when facing adversity, setbacks or stress. Individuals with high psychological resilience are usually able to better cope with challenges and changes in life, maintain a positive attitude and a healthy mental state. Assess the patient's psychological resilience and develop a personalized intervention plan for him to improve his coping ability and quality of life.

[0101] Furthermore, the psychological behavior analysis module divides the patient's physical and mental health assessment criteria into:

[0102] G1, Very healthy, 90-100 points, the patient's physical and mental health is excellent, physiological indicators are stable within the normal range, behavioral characteristics are normal, there is no abnormal behavior or emotional expression, the psychological state is positive and healthy, and there is no obvious anxiety or depression;

[0103] G2, healthy, 75-89 points, the patient is in good physical and mental health, physiological indicators are within the normal range most of the time, with occasional slight fluctuations, behavioral characteristics are basically normal, with occasional slight abnormalities but not affecting overall health, the psychological state is relatively stable, with occasional mild anxiety or depression but able to self-regulate;

[0104] G3, sub-health, 60-74 points, the patient's physical and mental health is in a sub-healthy state, physiological indicators have obvious fluctuations or mild abnormalities, and behavioral characteristics have many abnormalities, such as reduced social interaction, reduced range of activities, unstable mental state, anxiety, depression, and psychological problems. The psychological problems are more obvious and require regular psychological support and intervention;

[0105] G4, unhealthy, 0-59 points, the patient's physical and mental health is poor, there are serious abnormalities in physiological indicators, accompanied by multiple physiological diseases, and significantly abnormal behavioral characteristics or even dangerous behaviors, including frequent self-harm and attacks on others. The mental state is extremely unstable, and anxiety and depression are serious psychological problems, requiring urgent psychotherapy and medical intervention.

[0106] Furthermore, the remote intervention and treatment module includes an online diagnosis and treatment unit and a mobile device unit;

[0107] As the core of the remote intervention and treatment module, the online diagnosis and treatment unit provides patients with convenient and efficient psychological treatment services through highly integrated technical means. The online diagnosis and treatment unit provides targeted remote treatment for patients based on the scores of the psychological behavior analysis module, and sets the time and frequency of online diagnosis and treatment based on the scores. Taking into account the individual differences and living habits of patients, the online diagnosis and treatment unit provides flexible treatment time and frequency arrangements. Through intelligent algorithms, the system can automatically adjust the treatment plan according to the patient's score changes and treatment progress to ensure the maximum treatment effect.

[0108] As an extension of the remote intervention and treatment module, the mobile device unit brings treatment services into the daily lives of patients, greatly improving the convenience and accessibility of treatment. The mobile device unit has the ability to access the online diagnosis and treatment unit, and also provides a large number of free learning and educational resources related to mental illness, including popular science articles, video tutorials, and psychological tests. The mobile device unit can also access the patient communication community online to share experiences and exchange ideas with other patients. The mobile device unit also has a guardian mode that can remotely view the patient's real-time status. This function not only helps family members understand the patient's treatment progress and changes in their condition, but also provides timely support and help in emergency situations.

[0109] Furthermore, when it is monitored that the patient's physiological indicators are below the life safety red line or dangerous behaviors occur, the emergency response mechanism will be triggered, and an alarm message will be automatically sent to the emergency contact and the nearest medical institution will be notified for rescue.

[0110] Furthermore, the remote intervention and treatment module will recommend personalized treatment plans and rehabilitation plans for patients based on the evaluation results of the psychological and behavioral analysis module and the patient's historical treatment records, and implement them in the remote intervention and treatment module.

[0111] Example 1

[0112] Sleep quality directly affects the physical health of patients with mental illness. Lack of sleep or poor sleep quality can lead to decreased physical function and weakened immune system, which increases the risk of other diseases and further aggravates physical discomfort and fatigue. Long-term lack of sleep can lead to increased negative emotions such as depression, anxiety, and irritability, which in turn aggravates the symptoms of mental illness and makes it more difficult for patients to cope with the stress and challenges of daily life, forming a vicious cycle.

[0113] This embodiment uses a wearable monitoring unit to monitor and obtain the patient's vital signs in real time, and uses a behavior recognition unit to obtain the patient's daily behavior data through image information to directly monitor the patient's symptoms caused by sleep quality problems, and directly presents them in the form of clear physical and mental health assessment scores through a psychological behavior analysis module. Using a remote intervention and treatment module, online doctors can promptly diagnose and treat poor sleep quality problems to improve the patient's sleep quality.

[0114] The specific implementation steps are:

[0115] S1. The patient wears the wearable monitoring unit, which monitors the patient's vital signs around the clock, especially the indicators related to sleep quality, including heart rate variability, respiratory rate and depth, and body movement frequency;

[0116] S2, the behavior recognition unit captures the patient's nighttime sleep behavior, including the number of times he turns over, when he gets up at night, and when he falls asleep. It processes the video data in combination with the psychological behavior analysis module, analyzes the patient's behavior pattern, and determines whether there is abnormal behavior caused by poor sleep quality.

[0117] S3, the data transmission module transmits the data acquired by the wearable monitoring unit and the behavior recognition unit to the psychological behavior analysis module;

[0118] S4, the psychological behavior analysis module evaluates physiological indicators, behavioral characteristics, and psychological state. The physiological indicator evaluation combines real-time monitoring data to analyze the patient's physiological state, focusing on abnormal physiological indicators caused by decreased sleep quality. The behavioral characteristic evaluation determines whether there is staying up late or insomnia during the night sleep period based on image information. The psychological state evaluation combines facial expression and voice emotion analysis to identify the patient's anxiety, depression and other negative emotions caused by poor sleep quality.

[0119] S5. The psychological behavior analysis module gives a clear physical and mental health assessment score based on the assessment. This assessment score will be automatically uploaded to the system and notified to the online physician. The remote intervention and treatment module will arrange the online diagnosis and treatment time and notify the patient and physician.

[0120] S6. Based on the evaluation results, the doctor determines the problem caused by poor sleep quality and provides a treatment plan. The remote intervention and treatment module assists the patient and the doctor in completing the entire treatment process according to the treatment plan, including setting the time and frequency of treatment, drug guidance, and conducting health assessments on the patient at stages during the treatment to determine whether the treatment is effective.

[0121] Example 2

[0122] Communication is one of the important ways to express emotions. Patients feel lonely and isolated. Lack of communication will make them feel that their emotions are suppressed and they cannot find a suitable outlet to release them. This will accumulate inner pain and anxiety, making it difficult to relieve symptoms. Long-term lack of communication will affect patients' cognitive functions, causing them to feel slow thinking, lack of concentration, and even memory loss. These cognitive disorders may further affect their daily life and work ability.

[0123] This embodiment uses the behavior recognition unit to obtain the patient's daily behavior data through image information to directly detect the patient's avoidance of communication, and conducts online diagnosis and treatment through the remote intervention and treatment module to prevent the patient from not receiving offline treatment due to avoidance of communication, and notifies the guardian of the situation to help the patient out of the predicament.

[0124] The specific implementation steps are:

[0125] S1. The patient wears the wearable monitoring unit to continuously monitor the patient's physiological indicators;

[0126] S2, the behavior recognition unit captures the patient's behavior patterns, facial expression changes and voice information in real time, paying special attention to whether the patient is alone for a long time, avoids eye contact with people, avoids social activities and other behaviors that indicate avoidance of communication. It processes image and audio data, extracts key behavior and expression features, and regularly evaluates the patient's psychological state through a psychological measurement table, focusing on indicators such as the patient's social willingness, loneliness and depression level;

[0127] S3, the data transmission module transmits the data acquired by the wearable monitoring unit and the behavior recognition unit to the psychological behavior analysis module;

[0128] S4, Psychological behavior analysis module compares the patient's daily activity, social interaction pattern, abnormal behavior frequency with the information in the behavior pattern library to identify whether there is a behavior pattern of avoiding communication, analyze the patient's facial expression and language emotions, determine whether he or she shows negative emotions such as loneliness, anxiety or depression, and combine the results of the psychological measurement table to evaluate the patient's anxiety level, depression degree, coping mechanism and psychological resilience;

[0129] S5. The psychological behavior analysis module gives a clear physical and mental health assessment score based on the assessment. This assessment score will be automatically uploaded to the system and notified to the online physician. The remote intervention and treatment module will arrange the online diagnosis and treatment time and notify the patient and physician.

[0130] S6. Based on the assessment results, the physician will design a special communication training course to help patients learn how to establish and maintain social relationships and improve communication skills in response to the avoidance of communication. The physician will promptly notify the guardian of the patient's avoidance of communication so that the guardian can understand the patient's psychological condition and assist the patient in treatment. If the patient's physiological indicators are monitored to be below the life safety red line or dangerous behavior is present, the emergency response mechanism will be immediately triggered, an alarm message will be sent to the emergency contact, and the nearest medical institution will be notified for rescue. The remote intervention and treatment module will conduct health assessments on the patient at stages during the treatment to determine whether the treatment is effective.

[0131] 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.

[0132] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive remote monitoring system, characterized in that: The remote monitoring system includes: monitoring equipment module, data transmission module, psychological behavior analysis module and remote intervention and treatment module; The monitoring device module is used to monitor the physiological health status and psychological status of patients with mental illness; The data transmission module is used to encrypt and transmit the data obtained by the monitoring device module at high speed; The psychological behavior analysis module is used to analyze the data transmitted by the data transmission module and evaluate the health of patients with mental illness; The remote intervention and treatment module is used to provide online psychological counseling and treatment services, and to provide free learning and educational resources and an online patient communication community for patients with mental illness.

2. The all-round remote monitoring system according to claim 1, characterized in that: The monitoring device module includes a wearable monitoring unit, a behavior recognition unit, and a psychological condition assessment unit; The wearable monitoring unit monitors and obtains the patient's vital signs in real time, including heart rate, blood pressure, respiratory rate, stress and sleep quality; The behavior recognition unit obtains the patient's daily behavior data through image information, and analyzes the patient's behavior in combination with the psychological behavior analysis module to determine whether there is any abnormality; The psychological status assessment unit regularly assesses the patient's psychological status using a psychological measurement form.

3. The all-round remote monitoring system according to claim 2, characterized in that: The steps for the behavior recognition unit to implement behavior analysis and identify abnormalities are: A1. The behavior recognition unit captures the patient's behavior patterns, facial expression changes and voice information in real time; A2. Clean, denoise and remove redundancy from video data, perform face detection on video data, extract facial expression areas, and perform speech separation and denoising on audio data; A3. Use the psychological behavior analysis module to extract behavioral features, compare the features with the information in the behavior pattern library, identify the patient's behavior pattern, and determine whether the behavior is abnormal based on the abnormal behavior criteria; Based on the correspondence between facial expressions and emotions, psychological behavioral analysis is used to extract key expression features, and algorithms are used to classify the extracted features to identify the patient's emotional state. Combined with the facial expression recognition results, algorithms are used to analyze the patient's voice emotions. Based on the recognition results, combined with the patient's psychological state and background information, it is determined whether there is any abnormality in the emotions.

4. The all-round remote monitoring system according to claim 3, characterized in that: The detailed steps of video and audio data processing in A2 are: (1) Video data processing B1. Convert the video data format to AVI format and decompose it into individual frames. Use the image similarity algorithm to delete duplicate and highly similar frames. B2. Use Gaussian filtering technology to remove random noise in the video, and use the temporal correlation between video frames to remove noise through inter-frame differences; B3. Use Haar feature + AdaBoost algorithm to detect human faces in video frames, locate facial features in the faces, and extract key areas containing facial expressions based on the locations of facial features; (2) Audio data processing C1. Separate human voice and background sound through noise reduction and termination channel extraction tools; C2, use spectral subtraction to remove noise from audio; The Gaussian filtering technology, Haar feature + AdaBoost algorithm and spectral subtraction mentioned above are all existing technologies and will not be elaborated here.

5. The all-round remote monitoring system according to claim 3, characterized in that: The analysis methods in step A3 are divided into two categories: (1) Behavioral feature extraction and pattern recognition D1. Based on the principles of psychology and behavior, select key indicators that reflect individual behavior characteristics from the processed data, including movement frequency, movement amplitude, activity range, and social interaction patterns; D2. Extract behavioral features using computer vision technology; D3. Build a behavior pattern library based on a large amount of behavior data, use pattern matching methods to compare the extracted behavior features with the information in the behavior pattern library, and identify the most similar behavior patterns; D4. Based on the comparison results and the abnormal behavior standards, determine whether the behavior is abnormal; (2) Facial expression and emotion recognition E1. Use convolutional neural network to extract static and dynamic features of the face from the processed data; E2. Establish the correspondence between facial expressions and emotions based on psychology and neuroscience, use classification algorithms to classify the extracted expression features, and identify the corresponding emotional states; E3, extract acoustic and linguistic features from speech, combine with facial expression recognition results, combine visual and auditory information, improve the accuracy of emotion recognition, and analyze speech features to identify the patient's speech emotions; E4. Comprehensively analyze the emotion recognition results based on the patient's psychological state and background information to determine whether the emotions are abnormal; The computer vision technology, convolutional neural network and classification algorithm mentioned above are all existing technologies and will not be elaborated here.

6. The all-round remote monitoring system according to claim 1, characterized in that: The psychological behavior analysis module evaluates the patient's physical and mental health with the following factors and weights: F1. Physiological indicators account for 40% of the total weight, consisting of heart rate 10%, blood pressure 10%, respiratory rate 5%, Sleep quality 10%, stress response 5%; F2, behavioral characteristics account for 30% of the total weight, composed of 5% daily activities, 5% social interaction patterns, The frequency of abnormal behavior was 15%, and the regularity of life was 5%; F3. Psychological state accounts for 30% of the total weight, which is composed of anxiety level 10%, depression level 10%, coping mechanism 5%, and psychological resilience 5%.

7. The all-round remote monitoring system according to claim 1, characterized in that: The analysis module's assessment criteria for patients' physical and mental health are divided into: G1, Very healthy, 90-100 points, the patient's physical and mental health is excellent, physiological indicators are stable within the normal range, behavioral characteristics are normal, there is no abnormal behavior or emotional expression, the psychological state is positive and healthy, and there is no obvious anxiety or depression; G2, healthy, 75-89 points, the patient is in good physical and mental health, physiological indicators are within the normal range most of the time, with occasional slight fluctuations, behavioral characteristics are basically normal, with occasional slight abnormalities but not affecting overall health, the psychological state is relatively stable, with occasional mild anxiety or depression but able to self-regulate; G3, sub-health, 60-74 points, the patient's physical and mental health is in a sub-healthy state, physiological indicators have obvious fluctuations or mild abnormalities, and behavioral characteristics have many abnormalities, including reduced social interaction, reduced range of activities, unstable mental state, anxiety, depression and psychological problems. The psychological problems are more obvious and require regular psychological support and intervention; G4, unhealthy, 0-59 points, the patient's physical and mental health is poor, there are serious abnormalities in physiological indicators, accompanied by multiple physiological diseases, and significantly abnormal behavioral characteristics or even dangerous behaviors, including frequent self-harm and attacks on others. The mental state is extremely unstable, and anxiety and depression are serious psychological problems, requiring urgent psychotherapy and medical intervention.

8. The all-round remote monitoring system according to claim 1, characterized in that: The remote intervention and treatment module includes an online diagnosis and treatment unit and a mobile device unit; The online diagnosis and treatment unit provides targeted remote treatment to patients based on the scores of the psychological behavior analysis module, and determines the time and frequency of online diagnosis and treatment based on the scores; The mobile device unit has the ability to access the online diagnosis and treatment unit. It also provides a large number of free learning and educational resources related to mental illness. The patient communication community can be accessed online through the mobile device unit. The mobile device unit also has a guardian mode that can remotely view the patient's real-time status.

9. The all-round remote monitoring system according to claim 1, characterized in that: When it is monitored that the patient's physiological indicators are below the life safety red line or dangerous behavior occurs, the emergency response mechanism will be triggered, which will automatically send an alarm message to the emergency contact and notify the nearest medical institution for rescue.

10. The all-round remote monitoring system according to claim 1, characterized in that: The remote intervention and treatment module will recommend personalized treatment plans and rehabilitation plans for patients based on the evaluation results of the psychological and behavioral analysis module and the patient's historical treatment records, and implement them in the remote intervention and treatment module.

Images