Portable asthma monitoring terminal analysis system

Abstract

The invention provides a portable asthma monitoring terminal analysis system, which comprises a human body characteristic monitoring module, a state analysis module, an early warning transmission module and a big data storage module, and is characterized in that the human body characteristic monitoring module is used for detecting physiological data of a terminal carrier; the state analysis module is used for analyzing detected physiological data and judging the state of a terminal carrier, the early warning transmission module is used for outputting early warning information when the state is abnormal, and the big data storage module is used for storing feature information of all terminal users; the system can perform personalized analysis on the data acquired by the monitoring terminal, timely and accurately identify the abnormal state of the terminal wearer and give out early warning, and the terminal wearer can be effectively protected.

Description

Technical Field

[0001] The invention relates to the field of electrical digital data processing, and in particular to a portable asthma monitoring terminal analysis system. Background Art

[0002] Asthma is a common chronic respiratory disease, the main characteristics of which are chronic inflammation of the airways, airway hyperresponsiveness and reversible airflow limitation. When an asthma attack occurs, the patient will experience symptoms such as wheezing, shortness of breath, chest tightness and coughing. If not treated in time, it will seriously endanger the patient's life and health. Therefore, a monitoring terminal is needed to monitor the patient in real time and accurately analyze the monitoring data, so as to detect asthma attacks in a timely and effective manner and carry out early warning treatment.

[0003] The foregoing discussion of the background art is intended only to facilitate an understanding of the present invention. This discussion does not acknowledge or admit that any of the material referred to is part of the common general knowledge.

[0004] Many asthma analysis systems have been developed. After a lot of searching and reference, it is found that the existing analysis systems include the system disclosed in the publication number CN109475304B. These systems generally include: a monitoring unit, which is configured to monitor at least one objective asthma symptom of the subject; an analysis unit, which is configured to determine the asthma condition of the subject based on the at least one objective asthma symptom monitored by the monitoring unit; a test unit, which is configured to provide a test for collecting subjective asthma symptoms from the subject when the at least one objective asthma symptom monitored by the monitoring unit has changed in the following manner; however, the system directly judges the collected data when monitoring and analyzing the patient, lacks in-depth processing of the data, and is prone to misjudgment. Summary of the invention

[0005] The purpose of the present invention is to propose a portable asthma monitoring terminal analysis system in view of the existing deficiencies.

[0006] The present invention adopts the following technical solution:

[0007] A portable asthma monitoring terminal analysis system, comprising a human feature monitoring module, a state analysis module, an early warning transmission module and a big data storage module;

[0008] The human characteristic monitoring module is used to detect the physiological data of the terminal carrier, the state analysis module is used to analyze the detected physiological data to determine the state of the terminal carrier, the early warning transmission module is used to output early warning information when the state is abnormal, and the big data storage module is used to save the characteristic information of all terminal users;

[0009] The human feature monitoring module includes a vibration detection unit and a breathing detection unit, wherein the vibration detection unit is used to detect vibration information when the terminal carrier coughs, and the breathing detection unit is used to detect breathing information of the terminal carrier;

[0010] The state analysis module includes a basic matching unit, a vibration analysis unit, a breathing analysis unit and a comprehensive evaluation unit. The basic matching unit matches the big data storage module to obtain corresponding basic data based on personal information. The vibration analysis unit analyzes and processes the vibration information based on the basic data. The breathing analysis unit analyzes and processes the breathing information based on the basic data. The comprehensive evaluation unit is used to comprehensively evaluate the asthma status of the terminal carrier.

[0011] The warning transmission module includes a network access unit, a warning processing unit and a general processing unit, wherein the network access unit is used to connect the terminal to the Internet, the warning processing unit is used to send warning information to a specific target when the state is abnormal, and the general processing unit is used to send normal detection information to the display terminal;

[0012] The big data storage module includes a classification and sorting unit and an information storage unit, wherein the classification and sorting unit is used to classify the received data, and the information storage unit is used to store the sorted data;

[0013] Further, the basic matching unit includes a matching screening processor and a basic information register, the matching screening processor is used to input personal information and match the data in the big data storage module according to the personal information, and the basic information register is used to store the matched data information;

[0014] Further, the vibration analysis unit includes a vibration extraction processor, a feature analysis processor and a comparison output processor, wherein the vibration extraction processor is used to directly extract feature information from the vibration data, the feature analysis processor is used to perform change analysis on the extracted feature information, and the comparison output processor is used to compare the analysis result with the basic data and output the cough status;

[0015] The comparison output processor calculates the cough risk index Ch according to the following formula:

[0016]

[0017] Among them, a is the cough base, b is the intensity conversion coefficient, T0, f0, P0 are the cough duration, cough frequency and cough intensity in the basic information, T is the cough duration, P is the cough intensity, and f is the cough frequency;

[0018] Further, the breathing analysis unit includes a frequency analysis processor, a depth analysis processor and a breathing comparison processor, wherein the frequency analysis processor is used to analyze the frequency information of breathing, the depth analysis processor is used to analyze the depth information of breathing, and the breathing comparison processor is used to compare the analysis result with the basic data and output the breathing state;

[0019] The breathing comparison processor calculates the breathing abnormality index Bn within the corresponding time of the breathing feature package according to the following formula:

[0020]

[0021] Where c is the breathing base, d is the depth weight coefficient, is the frequency mean of the breathing feature package, is the mean value of the breathing depth of the breathing feature package, δBd is the standard deviation of the breathing depth of the breathing feature package, m is the frequency number of the breathing feature package, fb0 is the breathing frequency in the basic information, and Bd0 is the breathing depth value in the basic information;

[0022] Further, the comprehensive evaluation unit includes a mode selection processor and a comprehensive judgment processor, the mode selection processor selects a corresponding judgment mode based on the cough risk index, and the comprehensive judgment processor judges and evaluates the morbidity status of the terminal carrier based on the selected judgment mode;

[0023] The mode selection processor selects a time window, and when a cough risk index exists within the time window, selects the first judgment mode, otherwise selects the second judgment mode;

[0024] The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the first mode:

[0025]

[0026] The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the second mode:

[0027]

[0028] Among them, λ1 is the first mode parameter, λ2 is the second mode parameter, Bn(i) is the respiratory abnormality index of the i-th respiratory feature package in the time window, T(i) is the time corresponding to the i-th respiratory feature package in the time window, and N is the number of respiratory feature packages in the time window.

[0029] The beneficial effects achieved by the present invention are:

[0030] This system matches basic data from big data based on personal information, and uses the basic data to analyze and process the collected data, making subsequent analysis results more reliable. This system mainly collects and analyzes data from two angles: coughing and breathing, and ultimately uses two modes to judge the onset status, which can more comprehensively identify asthma attacks.

[0031] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the overall structural framework of the present invention;

[0033] Figure 2 This is a schematic diagram of the state analysis module of the present invention;

[0034] Figure 3 It is a schematic diagram of the vibration analysis unit of the present invention;

[0035] Figure 4 This is a schematic diagram of the respiratory analysis unit of the present invention;

[0036] Figure 5 It is a schematic diagram of the comprehensive evaluation unit of the present invention. DETAILED DESCRIPTION

[0037] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0038] Embodiment 1.

[0039] This embodiment provides a portable asthma monitoring terminal analysis system, combined with Figure 1 , including human characteristics monitoring module, status analysis module, early warning transmission module and big data storage module;

[0040] The human characteristic monitoring module is used to detect the physiological data of the terminal carrier, the state analysis module is used to analyze the detected physiological data to determine the state of the terminal carrier, the early warning transmission module is used to output early warning information when the state is abnormal, and the big data storage module is used to save the characteristic information of all terminal users;

[0041] The human feature monitoring module includes a vibration detection unit and a breathing detection unit, wherein the vibration detection unit is used to detect vibration information when the terminal carrier coughs, and the breathing detection unit is used to detect breathing information of the terminal carrier;

[0042] The state analysis module includes a basic matching unit, a vibration analysis unit, a breathing analysis unit and a comprehensive evaluation unit. The basic matching unit matches the big data storage module to obtain corresponding basic data based on personal information. The vibration analysis unit analyzes and processes the vibration information based on the basic data. The breathing analysis unit analyzes and processes the breathing information based on the basic data. The comprehensive evaluation unit is used to comprehensively evaluate the asthma status of the terminal carrier.

[0043] The warning transmission module includes a network access unit, a warning processing unit and a general processing unit, wherein the network access unit is used to connect the terminal to the Internet, the warning processing unit is used to send warning information to a specific target when the state is abnormal, and the general processing unit is used to send normal detection information to the display terminal;

[0044] The big data storage module includes a classification and sorting unit and an information storage unit, wherein the classification and sorting unit is used to classify the received data, and the information storage unit is used to store the sorted data;

[0045] The basic matching unit includes a matching screening processor and a basic information register, wherein the matching screening processor is used to input personal information and match the data in the big data storage module according to the personal information, and the basic information register is used to store the matched data information;

[0046] The vibration analysis unit includes a vibration extraction processor, a feature analysis processor and a comparison output processor, wherein the vibration extraction processor is used to directly extract feature information from the vibration data, the feature analysis processor is used to perform change analysis on the extracted feature information, and the comparison output processor is used to compare the analysis result with the basic data and output the cough status;

[0047] The comparison output processor calculates the cough risk index Ch according to the following formula:

[0048]

[0049] Among them, a is the cough base, b is the intensity conversion coefficient, T0, f0, P0 are the cough duration, cough frequency and cough intensity in the basic information, T is the cough duration, P is the cough intensity, and f is the cough frequency;

[0050] The breathing analysis unit includes a frequency analysis processor, a depth analysis processor and a breathing comparison processor, wherein the frequency analysis processor is used to analyze the frequency information of breathing, the depth analysis processor is used to analyze the depth information of breathing, and the breathing comparison processor is used to compare the analysis result with the basic data and output the breathing state;

[0051] The breathing comparison processor calculates the breathing abnormality index Bn within the corresponding time of the breathing feature package according to the following formula:

[0052]

[0053] Where c is the breathing base, d is the depth weight coefficient, is the frequency mean of the breathing feature package, is the mean value of the breathing depth of the breathing feature package, δBd is the standard deviation of the breathing depth of the breathing feature package, m is the frequency number of the breathing feature package, fb0 is the breathing frequency in the basic information, and Bd0 is the breathing depth value in the basic information;

[0054] The comprehensive evaluation unit includes a mode selection processor and a comprehensive judgment processor, wherein the mode selection processor selects a corresponding judgment mode based on the cough risk index, and the comprehensive judgment processor judges and evaluates the morbidity status of the terminal carrier based on the selected judgment mode;

[0055] The mode selection processor selects a time window, and when a cough risk index exists within the time window, selects the first judgment mode, otherwise selects the second judgment mode;

[0056] The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the first mode:

[0057]

[0058] The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the second mode:

[0059]

[0060] Among them, λ1 is the first mode parameter, λ2 is the second mode parameter, Bn(i) is the respiratory abnormality index of the i-th respiratory feature package in the time window, T(i) is the time corresponding to the i-th respiratory feature package in the time window, and N is the number of respiratory feature packages in the time window.

[0061] Embodiment 2.

[0062] This embodiment includes all the contents of the first embodiment, and provides a portable asthma monitoring terminal analysis system, including a human feature monitoring module, a state analysis module, an early warning transmission module, and a big data storage module;

[0063] The human characteristic monitoring module is used to detect the physiological data of the terminal carrier, the state analysis module is used to analyze the detected physiological data to determine the state of the terminal carrier, the early warning transmission module is used to output early warning information when the state is abnormal, and the big data storage module is used to save the characteristic information of all terminal users;

[0064] The human feature monitoring module includes a vibration detection unit and a breathing detection unit, wherein the vibration detection unit is used to detect vibration information when the terminal carrier coughs, and the breathing detection unit is used to detect breathing information of the terminal carrier;

[0065] Combination Figure 2 The state analysis module includes a basic matching unit, a vibration analysis unit, a breathing analysis unit and a comprehensive evaluation unit. The basic matching unit matches the corresponding basic data from the big data storage module based on personal information. The vibration analysis unit analyzes and processes the vibration information based on the basic data. The breathing analysis unit analyzes and processes the breathing information based on the basic data. The comprehensive evaluation unit is used to comprehensively evaluate the asthma status of the terminal carrier.

[0066] The warning transmission module includes a network access unit, a warning processing unit and a general processing unit, wherein the network access unit is used to connect the terminal to the Internet, the warning processing unit is used to send warning information to a specific target when the state is abnormal, and the general processing unit is used to send normal detection information to the display terminal;

[0067] Specific targets refer to people set by themselves on the terminal, such as exclusive doctors and guardian relatives, and the display end refers to the mobile device with the corresponding APP installed;

[0068] The big data storage module includes a classification and sorting unit and an information storage unit, wherein the classification and sorting unit is used to classify the received data, and the information storage unit is used to store the sorted data;

[0069] The vibration detection unit includes an acceleration sensor, a signal amplification processor and a cough determination processor, wherein the acceleration sensor is used to collect acceleration information generated by the vibration, the signal amplification processor is used to amplify the collected information, and the cough determination processor is used to determine whether the vibration is caused by a cough;

[0070] The respiration detection unit includes a piezoelectric sensor and a respiration conversion processor, wherein the piezoelectric sensor is used to collect electrical signals when the chest cavity expands and contracts, and the respiration conversion processor is used to convert the collected electrical signals into respiration information;

[0071] The basic matching unit includes a matching screening processor and a basic information register, wherein the matching screening processor is used to input personal information and match the data in the big data storage module according to the personal information, and the basic information register is used to store the matched data information;

[0072] Personal information included gender, age, and asthma history;

[0073] Combination Figure 3 The vibration analysis unit includes a vibration extraction processor, a feature analysis processor and a comparison output processor, wherein the vibration extraction processor is used to directly extract feature information from the vibration data, the feature analysis processor is used to perform change analysis on the extracted feature information, and the comparison output processor is used to compare the analysis result with the basic data and output the cough status;

[0074] The feature information extracted by the vibration extraction processor includes cough duration and vibration peak value, wherein the cough duration is the duration occupied by the entire vibration data segment, and the vibration peak value is a set of peaks whose amplitude exceeds a threshold value;

[0075] The feature analysis processor calculates the cough frequency according to the following formula:

[0076]

[0077] Where T is the duration of cough, n is the number of peaks in the peak set;

[0078] The feature analysis processor calculates the cough intensity P according to the following formula:

[0079]

[0080] Where A(i) represents the i-th peak value in the peak set;

[0081] The comparison output processor calculates the cough risk index Ch according to the following formula:

[0082]

[0083] Among them, a is the cough base, b is the intensity conversion coefficient, T0, f0, P0 are the cough duration, cough frequency and cough intensity in the basic information;

[0084] Combination Figure 4The breathing analysis unit includes a frequency analysis processor, a depth analysis processor and a breathing comparison processor, wherein the frequency analysis processor is used to analyze the frequency information of breathing, the depth analysis processor is used to analyze the depth information of breathing, and the breathing comparison processor is used to compare the analysis result with the basic data and output the breathing state;

[0085] The process of analyzing and processing the frequency information of breathing by the frequency analysis processor comprises the following steps:

[0086] S1, count the frequency value of each respiratory cycle and obtain the frequency sequence;

[0087] S2. Create a stable set, obtain a frequency value from the frequency sequence, and put it into the stable set;

[0088] S3, calculate the average value of the frequency in the stable set;

[0089] S4, obtaining a frequency value from the frequency sequence, if the difference between the frequency value and the average value in the stable set is less than the stable threshold, proceed to step S5, otherwise proceed to step S6;

[0090] S5, putting the frequency value into the stable set, and returning to step S3;

[0091] S6, pack the stable set into a frequency feature package containing the frequency mean and the frequency quantity, recreate a new stable set, put the frequency value into the stable set, and return to step S3;

[0092] The depth analysis processor calculates the breathing depth value Bd of each breathing cycle according to the following formula:

[0093]

[0094] Wherein, V(t) represents the electrical signal acquisition value of a respiratory cycle at time t, and k is the depth conversion coefficient;

[0095] The depth analysis processor aligns each breathing depth value with the frequency feature package, then calculates the mean and standard deviation of the breathing depth value corresponding to each frequency feature package, and then sends the frequency mean, frequency number, breathing depth mean and breathing depth standard deviation as a breathing feature package to the breathing comparison processor;

[0096] The breathing comparison processor calculates the breathing abnormality index Bn within the corresponding time of the breathing feature package according to the following formula:

[0097]

[0098] Where c is the breathing base, d is the depth weight coefficient, is the frequency mean of the breathing feature package, is the mean value of the breathing depth of the breathing feature package, δBd is the standard deviation of the breathing depth of the breathing feature package, m is the frequency number of the breathing feature package, fb0 is the breathing frequency in the basic information, and Bd0 is the breathing depth value in the basic information;

[0099] Combination Figure 5 The comprehensive evaluation unit includes a mode selection processor and a comprehensive judgment processor, wherein the mode selection processor selects a corresponding judgment mode based on the cough risk index, and the comprehensive judgment processor judges and evaluates the morbidity status of the terminal carrier based on the selected judgment mode;

[0100] The mode selection processor selects a time window, and when a cough risk index exists within the time window, selects the first judgment mode, otherwise selects the second judgment mode;

[0101] The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the first mode:

[0102]

[0103] The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the second mode:

[0104]

[0105] Wherein, λ1 is the first mode parameter, λ2 is the second mode parameter, Bn(i) is the respiratory abnormality index of the i-th respiratory feature package in the time window, T(i) is the time corresponding to the i-th respiratory feature package in the time window, and N is the number of respiratory feature packages in the time window;

[0106] When the morbidity index is greater than the morbidity threshold, the warning processing unit triggers a warning;

[0107] The a, b, c, d, λ1 and λ2 mentioned above are all system parameters and are set in advance by the staff;

[0108] The i appearing in the above text is an ordinal number used to indicate a sequence number.

[0109] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A portable asthma monitoring terminal analysis system, characterized in that: It includes human characteristics monitoring module, status analysis module, early warning transmission module and big data storage module; The human characteristic monitoring module is used to detect the physiological data of the terminal carrier, the state analysis module is used to analyze the detected physiological data to determine the state of the terminal carrier, the early warning transmission module is used to output early warning information when the state is abnormal, and the big data storage module is used to save the characteristic information of all terminal users; The human feature monitoring module includes a vibration detection unit and a breathing detection unit, wherein the vibration detection unit is used to detect vibration information when the terminal carrier coughs, and the breathing detection unit is used to detect breathing information of the terminal carrier; The state analysis module includes a basic matching unit, a vibration analysis unit, a breathing analysis unit and a comprehensive evaluation unit. The basic matching unit matches the big data storage module to obtain corresponding basic data based on personal information. The vibration analysis unit analyzes and processes the vibration information based on the basic data. The breathing analysis unit analyzes and processes the breathing information based on the basic data. The comprehensive evaluation unit is used to comprehensively evaluate the asthma status of the terminal carrier. The warning transmission module includes a network access unit, a warning processing unit and a general processing unit, wherein the network access unit is used to connect the terminal to the Internet, the warning processing unit is used to send warning information to a specific target when the state is abnormal, and the general processing unit is used to send normal detection information to the display terminal; The big data storage module includes a classification and sorting unit and an information storage unit. The classification and sorting unit is used to classify the received data, and the information storage unit is used to store the sorted data.

2. A portable asthma monitoring terminal analysis system as claimed in claim 1, characterized in that: The basic matching unit includes a matching screening processor and a basic information register. The matching screening processor is used to input personal information and match the data in the big data storage module according to the personal information. The basic information register is used to store the matched data information.

3. A portable asthma monitoring terminal analysis system as claimed in claim 2, characterized in that: The vibration analysis unit includes a vibration extraction processor, a feature analysis processor and a comparison output processor, wherein the vibration extraction processor is used to directly extract feature information from the vibration data, the feature analysis processor is used to perform change analysis on the extracted feature information, and the comparison output processor is used to compare the analysis result with the basic data and output the cough status; The comparison output processor calculates the cough risk index Ch according to the following formula: Among them, a is the cough base, b is the intensity conversion coefficient, T0, f0, and P0 are the cough duration, cough frequency, and cough intensity in the basic information, T is the cough duration, P is the cough intensity, and f is the cough frequency.

4. A portable asthma monitoring terminal analysis system as claimed in claim 3, characterized in that: The breathing analysis unit includes a frequency analysis processor, a depth analysis processor and a breathing comparison processor, wherein the frequency analysis processor is used to analyze the frequency information of breathing, the depth analysis processor is used to analyze the depth information of breathing, and the breathing comparison processor is used to compare the analysis result with the basic data and output the breathing state; The breathing comparison processor calculates the breathing abnormality index Bn within the corresponding time of the breathing feature package according to the following formula: Where c is the breathing base, d is the depth weight coefficient, is the frequency mean of the breathing feature package, is the mean value of the breathing depth of the breathing feature package, δBd is the standard deviation of the breathing depth of the breathing feature package, m is the frequency number of the breathing feature package, fb0 is the breathing frequency in the basic information, and Bd0 is the breathing depth value in the basic information.

5. A portable asthma monitoring terminal analysis system as claimed in claim 4, characterized in that: The comprehensive evaluation unit includes a mode selection processor and a comprehensive judgment processor, wherein the mode selection processor selects a corresponding judgment mode based on the cough risk index, and the comprehensive judgment processor judges and evaluates the morbidity status of the terminal carrier based on the selected judgment mode; The mode selection processor selects a time window, and when a cough risk index exists within the time window, selects the first judgment mode, otherwise selects the second judgment mode; The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the first mode: The comprehensive judgment processor calculates the morbidity index Q according to the following formula in the second mode: Among them, λ1 is the first mode parameter, λ2 is the second mode parameter, Bn(i) is the respiratory abnormality index of the i-th respiratory feature package in the time window, T(i) is the time corresponding to the i-th respiratory feature package in the time window, and N is the number of respiratory feature packages in the time window.

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