CN112370032A - Traditional Chinese medicine internal medicine clinical detection system based on Internet of things - Google Patents
Traditional Chinese medicine internal medicine clinical detection system based on Internet of things Download PDFInfo
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Abstract
The invention relates to a traditional Chinese medicine internal medicine clinical detection system based on the Internet of things, which is based on the Internet of things technology, the image processing analysis technology and the traditional Chinese medicine clinical technology, the physiological data and the environmental data of the patient are obtained through the mutual cooperation of the clinical data collection unit, the clinical data analysis unit, the clinical data decision unit and the clinical data storage unit, the data analysis and decision are carried out, the corresponding diagnosis confidence decision and the diagnosis adjustment decision result are given by combining the corresponding diagnosis scheme, the problem that the body condition of the patient with respiratory system disease can be monitored in real time at present is effectively solved, the diagnosis parameter monitoring is carried out based on the body condition of the patient, the confidence coefficient of the diagnosis scheme is judged, the problem of the corresponding treatment scheme is adjusted, the subjectivity of the corresponding diagnosis and treatment is changed, and the technical effects of comprehensively monitoring the state of the patient in real time in an all-round way and dynamically adjusting the diagnosis decision in real time based on objective data are achieved.
Description
Technical Field
The invention relates to a clinical detection system for a traditional Chinese medicine internal medicine, which realizes disease diagnosis and treatment, disease rehabilitation and promotion of health and longevity by utilizing multidisciplinary technologies such as wireless mobile internet, medical informatization, internet of things and the like.
Background
At present, the medical system in China mainly has the following problems: (1) the medical resources are totally seriously insufficient and are distributed unevenly. China accounts for 22% of the world, but medical and health resources account for only 2% of the world. As such, 80% of the only 2% of medical resources are concentrated in cities, where 80% of the resources are concentrated in major hospitals (as shown below). (2) The problems of difficult and expensive medical treatment are generally existed due to unreasonable medical resource allocation. On one hand, a lot of people can travel in a long distance and seek medical advice in other places, so that the difficulty in seeking medical advice is increased, and the economic burden is increased; on the other hand, the patients are full of the large hospitals. According to the statistics of the third national health survey data, the average outpatient service cost and the hospitalization cost of residents in China rise by 57.5 percent and 76.1 percent respectively from 1998-2003, which is far faster than the growth speed of the income of the residents. This makes the doctor feel a lot of pain. In a word, the health service system in China also has the problems of too fast increase of medical service cost, poor accessibility of medical service, unbalanced medical resource allocation, low health service efficiency, uneven medical service quality and the like, and if the health service system is not reformed, the new challenges are difficult to deal with. Deepening the reform of the medical health system, people have personal interests and social harmony development. As is known, the establishment of an efficient health regime is a worldwide problem, and it can be seen from the way of the improvement of the health regime in various countries, although the improvement ideas and methods are different, the improvement of the health regime is promoted by using an informatization means, and the balance between the medical and health service needs and the service supply is better solved. The utilization of informatization technology, remote wireless technology and Internet technology to solve the problems in the field of medical health is an opportunity and challenge for technicians in various countries.
The traditional Chinese medicine rehabilitation treatment is based on the theory of yin and yang and the theory of channels and collaterals, and guided by the holistic concept and dialectical treatment of the traditional Chinese medicine, and has unique theoretical characteristics and treatment advantages for promoting the functional rehabilitation of patients with nervous system diseases. At present, respiratory diseases mainly comprise chronic obstructive pulmonary disease, bronchial asthma, lung cancer after operation, coronary heart disease, heart failure and the like, the respiratory disease patients generally have impaired mobility, and the upper half body of each part of the limb, particularly the upper half body of the limb, often moves away from normal people due to obstructed breathing, so that diagnosis and treatment of the corresponding diseases need to be comprehensively and effectively carried out in multiple parts, stages, long time and the like to achieve the purpose of functional recovery. According to clinical experimental data, the traditional Chinese medicine rehabilitation multi-mode and multi-part digital system for respiratory system disease patients is constructed based on the acquired rehabilitation action characteristic parameters for analysis and reconstruction, and a solid foundation can be laid for the rehabilitation of nervous system disease patients.
However, currently, the treatment and rehabilitation training for respiratory disease patients generally only performs diagnosis and treatment by checking the respiratory condition of a user and describes the change condition of the patient based on the change condition of the patient, and medical staff adjusts the diagnosis scheme, so that the subjectivity is strong, and a system which can monitor the physical condition of the patient in real time, monitor diagnosis parameters based on the physical condition of the patient, judge the confidence level of the diagnosis scheme and adjust the corresponding treatment scheme is lacked.
Disclosure of Invention
The invention provides a traditional Chinese medicine internal medicine clinical detection system based on the Internet of things, and aims to solve the problems that the physical condition of a respiratory disease patient can be monitored in real time, diagnosis parameters are monitored based on the physical condition of the patient, the confidence coefficient of a diagnosis scheme is judged, and a corresponding treatment scheme is adjusted.
The invention requests to protect a traditional Chinese medicine internal medicine clinical detection system based on the Internet of things, which is characterized by comprising the following components:
the clinical data collection unit comprises a plurality of sensors of the internet of things, and is arranged on the body of a clinical detector and a rehabilitation treatment place of the clinical detector, the rehabilitation treatment place of the clinical detector comprises a home or a treatment room of the clinical detector, and the plurality of sensors of the internet of things of the clinical data collection unit collect physiological data of the clinical detector and environmental data of the rehabilitation treatment place of the clinical detector;
a clinical data analysis unit including an algorithm analyzer for performing motion recognition and environment analysis of the clinical examiner based on physiological data of the clinical examiner and environmental data of a rehabilitation treatment place of the clinical examiner;
the clinical data decision unit is used for carrying out diagnosis confidence decision and diagnosis adjustment decision on the clinical detection personnel based on the motion recognition and environment analysis results of the clinical detection personnel and the clinical data storage unit;
the clinical data storage unit is used for storing the diagnosis information of the clinical detection personnel, and the diagnosis information comprises action data, medicine data and diagnosis confidence data of the clinical detection personnel.
The invention relates to a traditional Chinese medicine internal medicine clinical detection system based on the Internet of things, which is based on the Internet of things technology, the image processing analysis technology and the traditional Chinese medicine clinical technology, the physiological data and the environmental data of the patient are obtained through the mutual cooperation of the clinical data collection unit, the clinical data analysis unit, the clinical data decision unit and the clinical data storage unit, the data analysis and decision are carried out, the corresponding diagnosis confidence decision and the diagnosis adjustment decision result are given by combining the corresponding diagnosis scheme, the problem that the body condition of the patient with respiratory system disease can be monitored in real time at present is effectively solved, the diagnosis parameter monitoring is carried out based on the body condition of the patient, the confidence coefficient of the diagnosis scheme is judged, the problem of the corresponding treatment scheme is adjusted, the subjectivity of the corresponding diagnosis and treatment is changed, and the technical effects of comprehensively monitoring the state of the patient in real time in an all-round way and dynamically adjusting the diagnosis decision in real time based on objective data are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a block diagram of a clinical detection system of internal medicine of traditional Chinese medicine based on Internet of things according to the present invention;
fig. 2 is a flowchart of the operation of the clinical detection system of internal medicine of traditional Chinese medicine based on the internet of things in embodiment 1.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to the attached drawings 1 and 2, the invention claims a clinical detection system of internal medicine of traditional Chinese medicine based on the internet of things, which is characterized by comprising:
the clinical data collection unit comprises a plurality of sensors of the internet of things, and is arranged on the body of a clinical detector and a rehabilitation treatment place of the clinical detector, the rehabilitation treatment place of the clinical detector comprises a home or a treatment room of the clinical detector, and the plurality of sensors of the internet of things of the clinical data collection unit collect physiological data of the clinical detector and environmental data of the rehabilitation treatment place of the clinical detector;
a clinical data analysis unit including an algorithm analyzer for performing motion recognition and environment analysis of the clinical examiner based on physiological data of the clinical examiner and environmental data of a rehabilitation treatment place of the clinical examiner;
the clinical data decision unit is used for carrying out diagnosis confidence decision and diagnosis adjustment decision on the clinical detection personnel based on the motion recognition and environment analysis results of the clinical detection personnel and the clinical data storage unit;
the clinical data storage unit is used for storing the diagnosis information of the clinical detection personnel, and the diagnosis information comprises action data, medicine data and diagnosis confidence data of the clinical detection personnel.
Preferably, the clinical data collection unit includes a plurality of internet of things sensors, install on clinical testing personnel physically with clinical testing personnel's rehabilitation department, clinical testing personnel's rehabilitation department includes clinical testing personnel's family or treatment room, a plurality of internet of things sensors of clinical data collection unit gather clinical testing personnel's physiological data with the environmental data of clinical testing personnel's rehabilitation department specifically includes:
the sensors on the body of the clinical detection personnel comprise a myoelectric sensor, a respiration pulse sensor and an acceleration sensor;
the sensor at the rehabilitation treatment place of the clinical detection personnel comprises a depth infrared sensor;
the physiological data of the clinical detection personnel comprise specific muscle movement data detected by the myoelectric sensor, pulse data detected by the respiratory pulse sensor and limb movement data detected by the acceleration sensor;
the environment data of the rehabilitation treatment place of the clinical detection personnel comprises infrared data detected by the deep infrared sensor, and the infrared data comprises personnel data and temperature data of the rehabilitation treatment place.
Preferably, the clinical data analysis unit includes an algorithm analyzer, and performs motion recognition and environment analysis of the clinical testing staff based on the physiological data of the clinical testing staff and the environment data of the rehabilitation treatment place of the clinical testing staff, and specifically includes:
acquiring signal sample data of an electromyographic sensor on the body of the clinical detection personnel, denoising an electromyographic signal containing interference noise by adopting a signal denoising method based on wavelet energy spectrum entropy, performing feature extraction on the acquired electromyographic signal, solving the L-Z complexity, the fractal dimension and the maximum fractal length of the electromyographic signal, inputting the acquired L-Z complexity and the fractal dimension as feature vectors into a K nearest neighbor model incremental learning algorithm classifier, acquiring a recognition result, and determining the muscle tension degree of the clinical detection personnel by using the acquired maximum fractal length of the electromyographic signal as a control quantity;
acquiring a respiratory pulse sensor on the body of the clinical detection personnel, extracting a first derivative and a second derivative of the preprocessed respiratory signal based on the respiratory signal of the clinical detection personnel, judging a peak/valley value according to the first derivative and the second derivative, introducing a refractory period, finishing marking a peak/valley value interval, and obtaining the peak/valley value and the current respiratory rate; finally, obtaining a final respiration signal by selecting a proper selectable threshold, solving a first derivative of the preprocessed pulse signal, performing Hilbert transformation, and selecting a proper time window width to segment the transformed data; calculating the maximum value and the root mean square value of each data segment, comparing and judging to automatically obtain the threshold value of each segment, marking the peak value interval to further obtain the peak value, calculating the current pulse, and introducing a second threshold value to carry out peak leakage detection; finally, a proper selectable threshold value is selected to obtain a needed pulse signal;
further, the specific process of extracting the respiratory signal based on the clinical testers is to apply constant-voltage excitation in the front and back directions of the thoracic cavity, a respiratory signal measuring resistor of 10 Ω is connected in series in the excitation loop, and the respiratory signal can be measured due to the fact that the whole thoracic impedance changes periodically in the respiratory process, and the voltage drop on the respiratory signal measuring resistor changes synchronously with respiration. Preprocessing the obtained respiratory signal, wherein the preprocessing comprises using an amplifier and a band-pass filter, the total amplification factor of the amplifier is set to be 50, the passband of the band-pass filter is selected to be 0.1Hz-10Hz, and the signal is amplified to obtain the preprocessed respiratory signal;
the pulse chart signals are mapped by standard II leads. And preprocessing the pulse signals obtained through II lead detection, wherein the preprocessing comprises using an amplifier and a band-pass filter, the total amplification factor of the amplifier is set to be 1000, the pass band of the band-pass filter is selected to be 0.5Hz-40Hz, and the signals are amplified to obtain the preprocessed pulse signals, wherein the power frequency interference, the high-frequency noise, the baseline drift and the like in the pulse signals are filtered.
Acquiring limb movement data collected by an acceleration sensor on the body of the clinical detection personnel, inputting a human skeleton point two-dimensional coordinate sequence of a registered action video sequence into a feature extraction module of a neural network matching model based on LSTM, extracting a feature sequence, estimating a possible movement track of the limb by using a Hidden Markov Model (HMM), applying point cloud as prior knowledge into the hidden Markov model, after the position track of the limb is obtained, the position of the limb can be determined by arranging the position of the limb along the direction of the limb, for the length of the lower limb translated by the upper limb, the length of the lower limb translated by the lower limb can obtain a characteristic sequence, the obtained characteristic sequences are respectively subjected to similarity calculation through a perception function module of an LSTM-based neural network matching model, and the motion evaluation result of the clinical detection personnel corresponding to the registered human body action video sequence in a matching library with the highest similarity is determined;
further, the estimation of the possible motion trajectory of the limb is to obtain a point cloud P of T positions from each time TiIs a point cloud PiSelecting a point, and connecting the points according to the time sequence, thereby equivalently recovering a possible limb movement track; sampling interval is deltatThe velocity v of the time interval can be calculated from the positions of two adjacent moments, and the acceleration acc can be calculated from the velocities of two adjacent momentselbSelecting trajectories in T point clouds
And according to
The position point in (1) obtains the corresponding acceleration
Then find the best match
Then the track is
Is the most likely movement trajectory of the limb.
Importing the collected rehabilitation force and rehabilitation track data of the shoulder, elbow, wrist, medulla, knee and stepping part of 89 patients of the hand, upper limb and lower limb into a rehabilitation action characteristic analysis system developed in cooperation for analysis, adding the data measured by 7 force measurement points, calculating the rehabilitation force, and counting the maximum value of each action rehabilitation force as the rehabilitation force characteristic; taking 5 periods of the track data of each action, calculating the minimum angle and the movement time of the joint moving in each period, and taking an average value as the movement angle and frequency characteristics; the rehabilitation force and the treatment action applied by the rehabilitation personnel in real time are collected and displayed by the rehabilitation personnel for the clinical detection personnel, and meanwhile, the rehabilitation force is displayed and the rehabilitation action is drawn according to the analyzed traditional Chinese medicine rehabilitation action characteristic information, so that a doctor is prompted whether the strength, the angle and the time of the rehabilitation action are accurate, and reference is provided for clinical traditional Chinese medicine treatment. Acquiring environmental data of a rehabilitation treatment place of the clinical testers comprises infrared data detected by the deep infrared sensor, the infrared data comprises personnel data and temperature data of the rehabilitation treatment place, current corresponding to a specific temperature, a high temperature higher than the specific temperature and a low temperature lower than the specific temperature is output to the deep infrared sensor mainly through a circuit of the deep infrared sensor by detecting the near infrared condition of the rehabilitation treatment place of the clinical detection personnel, so that the deep infrared sensor can be positioned at different temperatures, the inputted data and the prestored temperature data are compared to calibrate the parameters of the relationship between the heat radiation quantity and the temperature, receiving the digital electric signal of the object to be detected output by the infrared detection device, and forming picture frame data corresponding to the temperature of the object to be detected according to the parameter and the digital electric signal; before the object to be detected is detected, the infrared detection device realizes detection according to the specific temperature, high temperature and low temperature current provided by the circuit device without external light input and respectively outputs data information to obtain the conditions of people nearby the rehabilitation treatment place of the clinical detection personnel, including monitoring the physical condition of the visiting person based on infrared temperature data.
Further, the clinical data decision unit performs a diagnosis confidence decision and a diagnosis adjustment decision on the clinical testing staff based on the motion recognition and environment analysis result of the clinical testing staff and the clinical data storage unit, and specifically includes:
classifying, including mild, moderate, severe, critical, based on the results of the motion recognition and environmental analysis of the clinical test person and the clinical data storage unit;
when the clinical test person belongs to the light subclass, the clinical test person is characterized by mild airflow limitation with or without cough and cough;
when the clinical test person belongs to the medium category, the clinical test person is characterized by further worsening of airflow limitation with symptom progression and shortness of breath, which is more evident after exercise;
when the clinical testers belong to the strict category, the clinical testers are characterized in that the airflow limitation is further worsened, the short breath is aggravated, and the acute exacerbation repeatedly occurs, so that the life quality of the clinical testers is influenced;
when the clinical test personnel belong to the critical category, the clinical test personnel are characterized by severe airflow limitation or combined chronic respiratory failure, the quality of life of the patient is obviously reduced, and if acute exacerbation occurs, the patient is in danger of life;
acquiring a traditional Chinese medicine treatment scheme aiming at the clinical detection personnel at present, and adjusting the traditional Chinese medicine treatment scheme when the classification condition of the clinical detection personnel is monitored to develop from slight to critical.
Specifically, the mild patients use salbutamol aerosol according to the needs; the moderate patients take aminophylline orally on a mild basis for rehabilitation; the dosage of the medicines for patients with the same grade is consistent; clinical testers in severe and critical categories take the lung tonifying decoction orally 1 dose each time and 1 time each day on the basis.
The respiratory system diseases repeatedly attack with the illness state, the disease is deep, the lung, the spleen and the kidney are involved, the spleen qi and the kidney qi are necessary to be deficient, and finally, the lung, the spleen and the kidney are all deficient; therefore, the treatment should be performed by taking the lung, spleen and kidney into consideration, the spleen and lung should be treated during the treatment of the lung, and the kidney should be protected from being affected by the excessive spleen and kidney, so as to prevent the disease from developing to the point of consuming the innate original; the theory of traditional Chinese medicine for treating the disease is embodied; although the disease is manifested as a lung system symptom, the disease is closely related to the spleen and kidney, and the treatment in the stable stage not only can benefit the lung and invigorate the spleen, but also can nourish the kidney yang; the occurrence and development of the disease are related to congenital deficiency and acquired malnutrition, and the disease is treated by methods of lung protection, spleen strengthening, kidney tonifying and the like according to different individual differences; in the stable period, the development of the disease is greatly delayed by adopting methods of strengthening spleen, tonifying lung, warming kidney, receiving qi and the like; the decoction for strengthening body resistance, reducing phlegm and removing blood stasis, which can tonify qi, blood, yin and yang of lung, spleen and kidney, is used for treating patients in a stable stage, the clinical symptoms are obviously improved, and the lung health for tonifying lung, spleen and kidney is adopted and comprises ginseng, bighead atractylodes rhizome, honey-fried licorice root, prepared rhizome of rehmannia, fructus corni euryale, Chinese yam, pubescent holly root, fructus trichosanthis, tree peony bark, rhizoma alismatis, radix ophiopogonis, meadowrue seed and schisandra chinensis for treating patients in a slow obstructive lung remission stage, so that the lung function and the clinical symptoms of the patients can be.
Further, the clinical data storage unit is configured to store diagnostic information of clinical testing personnel, where the diagnostic information includes action data, drug data, and diagnostic confidence data of the clinical testing personnel, and specifically includes:
storing the acquired physiological data of the clinical testers and the environmental data of the rehabilitation treatment places of the clinical testers by adopting an unstructured data storage device;
the medicine data comprises medicine diagnosis scheme data, the medicine diagnosis scheme data consists of a plurality of specific medicines and relates to different classes of medicines, the overall medicine taking condition of the tonifying prescription, namely all related medicines, is counted by using a frequency counting method, the medicine taking frequency of each medicine is sorted from high to low according to the medicine taking frequency, high-frequency medicines are searched, core medicines of the tonifying prescription are obtained, then the core medicines are classified according to the medicine efficacy, and the medicine taking rule of the tonifying prescription is explored and analyzed;
the medicines of the medical diagnosis scheme data can be divided into 11 types of medicines according to categories, wherein the medicines are used for dispelling wind and eliminating evil, promoting blood circulation to remove blood stasis, strengthening body resistance and tonifying, dredging viscera and purging excess, calming liver and extinguishing fire, stopping wind and relieving convulsion, warming stomach and strengthening spleen, filtering phlegm and inducing resuscitation, dispelling wind and eliminating dampness, clearing heat and detoxicating, promoting qi circulation and regulating qi, and the like. In clinical applications, it may be selected as appropriate.
Finding out a common prescription pair, namely 2-medicine compatibility and a prescription group, namely 3-medicine combination and a prescription group, namely 4-medicine combination and a prescription group, namely 5-medicine combination through a frequent item set, and mining the combination correlation among medicines by using an association rule method so as to discuss the regularity of compatibility among medicines;
analyzing the subclasses of the medicines in the tonifying prescription by using a frequent itemset;
when the classification condition of the clinical detection personnel is monitored to develop from slight to critical, the confidence coefficient of the traditional Chinese medicine treatment scheme is reduced when the traditional Chinese medicine treatment scheme is adjusted;
acquiring a traditional Chinese medicine treatment scheme aiming at the clinical detection personnel at present, and when the classification condition of the clinical detection personnel is monitored to develop from a critical condition to a slight direction, improving the confidence coefficient of the traditional Chinese medicine treatment scheme.
Acupuncture treatment is carried out in the acute stage of cough and asthma, and acupuncture information can be continuously transmitted into muscle tissues while peripheral receptors are repeatedly stimulated, so that cerebral cortex functions are activated. Meanwhile, acupuncture can also regulate the metabolism of neurotransmitters such as catecholamine, acetylcholine, serotonin and enkephalin in brain, reduce sympathetic nerve tension, expand cerebral vessels, improve the energy deficiency state of brain and restore the metabolic balance of muscle tissues; the limb function is stimulated by a neural pathway. After the filiform needle is inserted into the body of patient according to a certain acupoint, the acupuncture stimulation is sensed by the receptor in the deep part of acupoint, so that the receptor is excited and is conducted in the form of nerve impulse, and is transmitted to spinal cord through afferent nerve, then transmitted to brain, and transmitted to the motor end plate of spasm muscle through the efferent fiber of spinal cord, so as to produce muscle contraction reaction and prevent disuse atrophy. Thus, stimulation not only stimulates the injured miss brain tissue, helps restore and reestablish normal function and promotes the compensation of uninvolved muscle tissue and the collateral circulation and the creation of new reflex arcs, but also induces the contraction of spastic muscles through the spinal cord proper. Meanwhile, the muscle spindle receptors under the acupuncture points are stimulated by acupuncture at , and acupuncture information is transmitted to the spinal cord through sensory neurons to cause stretch reflex, so that the muscle on the spasm side generates autonomic contraction. Secondly, the body fluid efferent pathway is adopted, namely after the central nervous system is stimulated by acupuncture to generate excitation, endocrine glands and central nerve nuclei in the body are indirectly influenced, so that the metabolism of various hormones and neurotransmitters in the body is regulated, and the physiological function of patients with cough and asthma is improved.
The content related to the invention is not only applicable to the rehabilitation of respiratory system diseases, but also applicable to the rehabilitation of nervous system diseases: various dysfunctions such as stroke, parkinson, senile dementia, etc.; rehabilitation of the old: diabetes, hypertension, degenerative diseases, etc.; bone joint rehabilitation: pain in the neck, shoulder, waist and legs, sports injuries, fractures, etc.; the children recover: infantile cerebral palsy, autism, behavioral development retardation, and the like; cardiopulmonary rehabilitation: chronic obstructive pulmonary disease, bronchial asthma, post-lung cancer surgery, coronary heart disease and post-surgery, heart failure, etc.; and (3) postpartum recovery: postpartum lumbago, urinary incontinence, vaginal relaxation, etc.; recovery of breast cancer: mainly for postoperative rehabilitation; recovery from obesity; the rehabilitation of sub-health people and the like can play a better monitoring role.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (5)
1. The utility model provides a traditional chinese medical science internal medicine clinical detection system based on thing networking which characterized in that includes:
the clinical data collection unit comprises a plurality of sensors of the internet of things, and is arranged on the body of a clinical detector and a rehabilitation treatment place of the clinical detector, the rehabilitation treatment place of the clinical detector comprises a home or a treatment room of the clinical detector, and the plurality of sensors of the internet of things of the clinical data collection unit collect physiological data of the clinical detector and environmental data of the rehabilitation treatment place of the clinical detector;
a clinical data analysis unit including an algorithm analyzer for performing motion recognition and environment analysis of the clinical examiner based on physiological data of the clinical examiner and environmental data of a rehabilitation treatment place of the clinical examiner;
the clinical data decision unit is used for carrying out diagnosis confidence decision and diagnosis adjustment decision on the clinical detection personnel based on the motion recognition and environment analysis results of the clinical detection personnel and the clinical data storage unit;
the clinical data storage unit is used for storing the diagnosis information of the clinical detection personnel, and the diagnosis information comprises action data, medicine data and diagnosis confidence data of the clinical detection personnel.
2. The system of claim 1, wherein the system comprises:
clinical data collection unit, including a plurality of thing networking sensors, install on clinical measurement personnel physically with clinical measurement personnel's rehabilitation department, clinical measurement personnel's rehabilitation department includes clinical measurement personnel's family or treatment room, a plurality of thing networking sensors of clinical data collection unit gather clinical measurement personnel's physiological data with the environmental data of clinical measurement personnel's rehabilitation department specifically includes:
the sensors on the body of the clinical detection personnel comprise a myoelectric sensor, a respiration pulse sensor and an acceleration sensor;
the sensor at the rehabilitation treatment place of the clinical detection personnel comprises a depth infrared sensor;
the physiological data of the clinical detection personnel comprise specific muscle movement data detected by the myoelectric sensor, pulse data detected by the respiratory pulse sensor and limb movement data detected by the acceleration sensor;
the environment data of the rehabilitation treatment place of the clinical detection personnel comprises infrared data detected by the deep infrared sensor, and the infrared data comprises personnel data and temperature data of the rehabilitation treatment place.
3. The system of claim 2, wherein the system comprises:
the clinical data analysis unit comprises an algorithm analyzer, performs motion recognition and environment analysis of the clinical testing staff based on the physiological data of the clinical testing staff and the environment data of the rehabilitation treatment place of the clinical testing staff, and specifically comprises:
acquiring signal sample data of an electromyographic sensor on the body of the clinical detection personnel, denoising an electromyographic signal containing interference noise by adopting a signal denoising method based on wavelet energy spectrum entropy, performing feature extraction on the acquired electromyographic signal, solving the L-Z complexity, the fractal dimension and the maximum fractal length of the electromyographic signal, inputting the acquired L-Z complexity and the fractal dimension as feature vectors into a K nearest neighbor model incremental learning algorithm classifier, acquiring a recognition result, and determining the muscle tension degree of the clinical detection personnel by using the acquired maximum fractal length of the electromyographic signal as a control quantity;
acquiring a respiratory pulse sensor on the body of the clinical detection personnel, extracting a first derivative and a second derivative of the preprocessed respiratory signal based on the respiratory signal of the clinical detection personnel, judging a peak/valley value according to the first derivative and the second derivative, introducing a refractory period, finishing marking a peak/valley value interval, and obtaining the peak/valley value and the current respiratory rate; finally, obtaining a final respiration signal by selecting a proper selectable threshold, solving a first derivative of the preprocessed pulse signal, performing Hilbert transformation, and selecting a proper time window width to segment the transformed data; calculating the maximum value and the root mean square value of each data segment, comparing and judging to automatically obtain the threshold value of each segment, marking the peak value interval to further obtain the peak value, calculating the current pulse, and introducing a second threshold value to carry out peak leakage detection; finally, a proper selectable threshold value is selected to obtain a needed pulse signal;
acquiring limb movement data collected by an acceleration sensor on the body of the clinical detection personnel, inputting a human skeleton point two-dimensional coordinate sequence of a registered action video sequence into a feature extraction module of a neural network matching model based on LSTM, extracting a feature sequence, estimating a possible movement track of the limb by using a Hidden Markov Model (HMM), applying point cloud as prior knowledge into the hidden Markov model, after the position track of the limb is obtained, the position of the limb can be determined by arranging the position of the limb along the direction of the limb, for the length of the lower limb translated by the upper limb, the length of the lower limb translated by the lower limb can obtain a characteristic sequence, the obtained characteristic sequences are respectively subjected to similarity calculation through a perception function module of an LSTM-based neural network matching model, and the motion evaluation result of the clinical detection personnel corresponding to the registered human body action video sequence in a matching library with the highest similarity is determined;
acquiring environmental data of a rehabilitation treatment place of the clinical testers comprises infrared data detected by the deep infrared sensor, the infrared data comprises personnel data and temperature data of the rehabilitation treatment place, current corresponding to a specific temperature, a high temperature higher than the specific temperature and a low temperature lower than the specific temperature is output to the deep infrared sensor mainly through a circuit of the deep infrared sensor by detecting the near infrared condition of the rehabilitation treatment place of the clinical detection personnel, so that the deep infrared sensor can be positioned at different temperatures, the input data and the prestored temperature data are compared to calibrate the parameters of the relationship between the heat radiation quantity and the temperature, receiving the digital electric signal of the object to be detected output by the infrared detection device, and forming picture frame data corresponding to the temperature of the object to be detected according to the parameter and the digital electric signal; before the object to be detected is detected, the infrared detection device realizes detection according to the specific temperature, high temperature and low temperature current provided by the circuit device without external light input and respectively outputs data information to obtain the conditions of people nearby the rehabilitation treatment place of the clinical detection personnel, including monitoring the physical condition of the visiting personnel based on infrared temperature data.
4. The system of claim 2, wherein the system comprises:
the clinical data decision unit performs a diagnosis confidence decision and a diagnosis adjustment decision on the clinical testing staff based on the motion recognition and environment analysis result of the clinical testing staff and the clinical data storage unit, and specifically includes:
classifying, including mild, moderate, severe, critical, based on the results of the motion recognition and environmental analysis of the clinical test person and the clinical data storage unit;
when the clinical test person belongs to the light subclass, the clinical test person is characterized by mild airflow limitation with or without cough and cough;
when the clinical test person belongs to the medium category, the clinical test person is characterized by further worsening of airflow limitation with symptom progression and shortness of breath, which is more evident after exercise;
when the clinical testers belong to the strict category, the clinical testers are characterized in that the airflow limitation is further worsened, the short breath is aggravated, and the acute exacerbation repeatedly occurs, so that the life quality of the clinical testers is influenced;
when the clinical test personnel belong to the critical category, the clinical test personnel are characterized by severe airflow limitation or combined chronic respiratory failure, the quality of life of the patient is obviously reduced, and if acute exacerbation occurs, the patient is in danger of life;
acquiring a traditional Chinese medicine treatment scheme aiming at the clinical detection personnel at present, and adjusting the traditional Chinese medicine treatment scheme when the classification condition of the clinical detection personnel is monitored to develop from slight to critical.
5. The system of claim 4, wherein the system comprises:
the clinical data storage unit is used for storing diagnostic information of clinical detection personnel, the diagnostic information comprises action data, medicine data and diagnostic confidence data of the clinical detection personnel, and the clinical data storage unit specifically comprises:
storing the acquired physiological data of the clinical testers and the environmental data of the rehabilitation treatment places of the clinical testers by adopting an unstructured data storage device;
the medicine data comprises medicine diagnosis scheme data, the medicine diagnosis scheme data consists of a plurality of specific medicines and relates to different classes of medicines, the overall medicine taking condition of the tonifying prescription, namely all related medicines, is counted by using a frequency counting method, the medicine taking frequency of each medicine is sorted from high to low according to the medicine taking frequency, high-frequency medicines are searched, core medicines of the tonifying prescription are obtained, then the core medicines are classified according to the medicine efficacy, and the medicine taking rule of the tonifying prescription is explored and analyzed;
finding out a common prescription pair, namely 2-medicine compatibility and a prescription group, namely 3-medicine combination and a prescription group, namely 4-medicine combination and a prescription group, namely 5-medicine combination through a frequent item set, and mining the combination correlation among medicines by using an association rule method so as to discuss the regularity of compatibility among medicines;
analyzing the subclasses of the medicines in the tonifying prescription by using a frequent itemset;
the acquiring of the current treatment scheme of the traditional Chinese medicine aiming at the clinical testers, when the classification situation of the clinical testers is monitored to develop from slight to critical direction and the treatment scheme of the traditional Chinese medicine is adjusted,
reducing the confidence level of the treatment regimen of the traditional Chinese medicine;
acquiring a traditional Chinese medicine treatment scheme aiming at the clinical detection personnel at present, and when the classification condition of the clinical detection personnel is monitored to develop from a critical condition to a slight direction, improving the confidence coefficient of the traditional Chinese medicine treatment scheme.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113180971A (en) * | 2021-04-29 | 2021-07-30 | 新乡市中心医院 | Traditional Chinese medicine nursing device and control method |
| CN113936767A (en) * | 2021-09-30 | 2022-01-14 | 逸群海豚信息科技(上海)有限公司 | Health management method for asthma patient |
| CN115101156A (en) * | 2022-08-26 | 2022-09-23 | 南京网金网络科技有限公司 | Family doctor information management system based on multi-source data analysis |
| CN115105035A (en) * | 2022-06-16 | 2022-09-27 | 深圳市华屹医疗科技有限公司 | Physiological characteristic detection method and device, computer equipment and storage medium |
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2020
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113180971A (en) * | 2021-04-29 | 2021-07-30 | 新乡市中心医院 | Traditional Chinese medicine nursing device and control method |
| CN113180971B (en) * | 2021-04-29 | 2023-09-22 | 新乡市中心医院 | Traditional Chinese medicine nursing device and control method |
| CN113936767A (en) * | 2021-09-30 | 2022-01-14 | 逸群海豚信息科技(上海)有限公司 | Health management method for asthma patient |
| CN115105035A (en) * | 2022-06-16 | 2022-09-27 | 深圳市华屹医疗科技有限公司 | Physiological characteristic detection method and device, computer equipment and storage medium |
| CN115105035B (en) * | 2022-06-16 | 2023-06-16 | 深圳市华屹医疗科技有限公司 | Physiological characteristic detection method, device, computer equipment and storage medium |
| CN115101156A (en) * | 2022-08-26 | 2022-09-23 | 南京网金网络科技有限公司 | Family doctor information management system based on multi-source data analysis |
| CN115101156B (en) * | 2022-08-26 | 2022-12-20 | 南京网金网络科技有限公司 | Family doctor information management system based on multi-source data analysis |
| CN117041906A (en) * | 2023-10-08 | 2023-11-10 | 天津鹏萱汇智信息技术有限公司 | Data monitoring service management system based on Internet of things |
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