Vital sign data acquisition system based on artificial intelligence

Abstract

The invention belongs to the field of intelligent medical acquisition, and discloses a vital sign data acquisition system based on artificial intelligence. According to the invention, human body bioelectricity signals, user information characteristic data, user medical diagnosis focus parts and user preferred vital signs are collected to construct a vital sign collecting and positioning model; the constructed vital sign collecting and positioning model generates real-time user preferred vital signs according to real-time human body bioelectricity signals, real-time user information characteristic data and real-time user medical diagnosis focus parts received by the biological information detector, and the real-time user preferred vital signs are acquired according to pathological tissues corresponding to the real-time user preferred vital signs; and carrying out biological information repair on the pathological tissue by adopting a biological electromagnetic therapy. According to the method, the abnormal vital sign data of the user is accurately recognized, the pathological position is obtained, artificial intelligent vital sign data optimization collection is realized, the working intensity of medical personnel is reduced, and a basis is provided for selecting a living body pathological part for targeted biological electromagnetic feedback treatment in a subsequent biological electromagnetic therapy.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent medical data collection technology, and in particular relates to a vital sign data collection system based on artificial intelligence. Background Art

[0002] Home-based care has become a social norm, and the vital signs and status of the elderly cannot be monitored in real time. The monitoring alarm devices on the market almost always send out alarm messages after a problem occurs. At the same time, after the patient leaves the hospital, the status cannot be interacted with medical staff in time. Therefore, how to monitor the status of the monitored target in real time, assist doctors in finding the location of the patient's pathological tissue, and use bio-electromagnetic technology for subsequent electromagnetic therapy for the pathological point has become an urgent problem to be solved.

[0003] In addition, how to accurately identify the user's abnormal vital signs data and obtain the pathological location, realize artificial intelligence-based optimization of vital signs data collection, reduce the workload of medical personnel, and provide a basis for subsequent bio-electromagnetic therapy to select pathological parts of the vital body for targeted bio-electromagnetic feedback treatment has become an important issue in quickly identifying the patient's real-time health status.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art.

[0005] Therefore, there is an urgent need for a vital sign data acquisition system based on artificial intelligence to solve the above problems. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a vital sign data collection system based on artificial intelligence.

[0007] In a first aspect of the present invention, a method for collecting vital sign data based on artificial intelligence is provided, wherein the method comprises the following steps:

[0008] S1, obtaining a first bioelectric signal of a human body and first user information characteristic data, a first user medical diagnosis lesion site collected and transmitted from a user hospital end, and a first user preferred vital sign;

[0009] S2, a first acquisition vector feature constructed according to the first bioelectric signal, the first user information feature data, and the first user medical diagnosis lesion site;

[0010] S3, constructing a vital sign collection and positioning model by using the first collection vector feature and the first user's preferred vital sign;

[0011] S4. The bio-information detector receives the second bioelectric signal and second user information characteristic data of the human body, and the second user medical diagnosis lesion site collected and transmitted from the user hospital end, and constructs a second acquisition vector feature based on the second bioelectric signal, the second user information characteristic data, and the second user medical diagnosis lesion site. The vital sign positioning model processes the second acquisition vector feature to generate a second user preferred vital sign. According to the second user preferred vital sign, the corresponding pathological tissue site of the human body is obtained, and bio-electromagnetic feedback treatment is selectively performed on the pathological tissue site.

[0012] Furthermore, the first bioelectric signal or the second bioelectric signal utilizes the bioinformation sensing sensor on the bioinformation detector to obtain the muscle electrical signal from the arm, and utilizes the wireless EEG acquisition device to collect the EEG signal and transmit it to the bioinformation detector, and the EEG signal is combined with the muscle electrical signal to form the first bioelectric signal or the second bioelectric signal.

[0013] Further, the first user information characteristic data or the first user information characteristic data includes user gender, user age, user height and user weight;

[0014] The first user medical diagnosis lesion site or the second user medical diagnosis lesion site is determined by collecting the user's previous first medical record data or second medical record data and transmitting it to the hospital.

[0015] Furthermore, the wireless EEG acquisition device includes the NeuroHUB series wireless EEG acquisition system, which can acquire brain signals from 64 brain EEG channels; the specific brain acquisition areas of the EEG data are F7, F3, Fz, F4, F8, Cz, C3, C4, O1 and O2;

[0016] Furthermore, the muscle electrical signal M t It is obtained by processing the inverse of the distance between the source position of the muscle electrical signal and the first user medical diagnosis lesion site or the second user medical diagnosis lesion site.

[0017] Furthermore, the first acquisition vector feature is composed of the first bioelectric signal, the first user information feature data and the first user medical diagnosis lesion site, and the second acquisition vector feature is composed of the second bioelectric signal, the second user information feature data and the second user medical diagnosis lesion site, and the specific composition method is vector feature splicing.

[0018] Furthermore, the vital signs collection and positioning model adopts an improved neural network model.

[0019] A vital sign data collection system based on artificial intelligence is also provided, the system comprising a biological information monitoring module, a user information registration module, a user information storage module, a medical diagnosis information collection module, a vital sign collection positioning model construction module, and a vital sign data collection module, characterized in that:

[0020] The biological information monitoring module is used to collect and obtain the first bioelectric signal of the human body, and is also used to collect and obtain the second bioelectric signal of the human body;

[0021] The user information registration module is used for user registration to upload the first user information characteristic data, and is also used for uploading the second user information characteristic data;

[0022] The medical diagnosis information collection module is connected to the hospital client to receive the first user medical diagnosis lesion site collected and transmitted from the user hospital end and the second user medical diagnosis lesion site collected and transmitted from the user hospital end;

[0023] The user information storage module is used to receive the first user information characteristic data transmitted by the user information registration module, and also receives the second user information characteristic data, stores the first user's preferred vital signs, and is connected to the medical diagnosis information collection module to receive the first user's medical diagnosis lesion site and the second user's medical diagnosis lesion site;

[0024] The vital signs collection and positioning model construction module receives the first bioelectric signal, the first user information feature data, and the first user's medical diagnosis lesion site, processes to obtain a first collection vector feature, and uses the first collection vector feature and the first user's preferred vital signs to construct a vital signs collection and positioning model;

[0025] The vital signs data acquisition module: the bioinformation detector receives the second bioelectric signal and second user information feature data of the human body, and the second user medical diagnosis lesion site collected and transmitted from the user hospital end, and constructs a second acquisition vector feature based on the second bioelectric signal, the second user information feature data, and the second user medical diagnosis lesion site. The vital signs positioning model processes the second acquisition vector feature to generate a second user preferred vital sign. According to the second user preferred vital sign, the corresponding pathological tissue site of the human body is obtained, and bio-electromagnetic feedback treatment is selectively performed on the pathological tissue site.

[0026] Furthermore, the first bioelectric signal or the second bioelectric signal uses the bioinformation sensing sensor on the bioinformation detector to obtain the muscle electrical signal from the arm, and uses the wireless EEG acquisition device to collect the EEG signal and transmit it to the bioinformation detector, and the EEG signal is combined with the muscle electrical signal to form the first bioelectric signal or the second bioelectric signal;

[0027] The muscle electrical signal M t Obtained by processing the inverse of the distance between the location of the muscle electrical signal source and the first user's medical diagnosis lesion location or the second user's medical diagnosis lesion location;

[0028] The first acquisition vector feature is composed of the first bioelectric signal, the first user information feature data and the first user medical diagnosis lesion site, and the second acquisition vector feature is composed of the second bioelectric signal, the second user information feature data and the second user medical diagnosis lesion site, and the specific composition method is vector feature splicing.

[0029] Furthermore, the vital signs collection and positioning model adopts an improved neural network model.

[0030] The present invention provides an artificial intelligence-based vital sign data acquisition system, which collects muscle electrical signals and brain electrophysiological signals from key parts of the body, and combines personal height, age and other related factors that affect physical examination vital sign data, and improves a neural network model suitable for optimal vital sign data acquisition, thereby accurately identifying the user's abnormal vital sign data and obtaining the pathological location, thereby realizing artificial intelligence-based optimized vital sign data acquisition, reducing the workload of medical personnel, and providing a basis for subsequent bio-electromagnetic therapy to select pathological parts of the living body for targeted bio-electromagnetic feedback treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of a vital sign data collection method based on artificial intelligence of the present invention;

[0032] Figure 2 It is a structural schematic diagram of a vital sign data acquisition system based on artificial intelligence of the present invention;

[0033] Figure 3 It is a partial schematic diagram of the biological information detector in the present invention;

[0034] Figure 4 It is a schematic diagram of the coordinates of human body points after two-dimensional coordinate conversion in this method;

[0035] Figure 5 This is an EEG lead diagram in an embodiment of the present invention DETAILED DESCRIPTION

[0036] The invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0037] The first embodiment of the present invention:

[0038] In a first aspect of the present invention, in order to solve the above technical problems, the present invention provides a vital sign data acquisition system based on artificial intelligence.

[0039] In a first aspect of the present invention, a method for collecting vital sign data based on artificial intelligence is provided, wherein the method comprises the following steps:

[0040] S1, obtaining a first bioelectric signal of a human body and first user information characteristic data, a first user medical diagnosis lesion site collected and transmitted from a user hospital end, and a first user preferred vital sign;

[0041] S2, a first acquisition vector feature constructed according to the first bioelectric signal, the first user information feature data, and the first user medical diagnosis lesion site;

[0042] S3, constructing a vital sign collection and positioning model by using the first collection vector feature and the first user's preferred vital sign;

[0043] S4. The bio-information detector receives the second bioelectric signal and second user information characteristic data of the human body, and the second user medical diagnosis lesion site collected and transmitted from the user hospital end, and constructs a second acquisition vector feature based on the second bioelectric signal, the second user information characteristic data, and the second user medical diagnosis lesion site. The vital sign positioning model processes the second acquisition vector feature to generate a second user preferred vital sign. According to the second user preferred vital sign, the corresponding pathological tissue site of the human body is obtained, and bio-electromagnetic feedback treatment is selectively performed on the pathological tissue site.

[0044] In this embodiment, the first user preferred vital sign or the second user preferred vital sign is a vector feature composed of four eigenvector values ​​consisting of the human body point coordinates after the human body is converted into two-dimensional coordinates, the point coordinate body temperature and muscle electrical signal parameters. Electromagnetic feedback treatment can be performed on the corresponding points according to the user preferred vital sign.

[0045] Furthermore, the first bioelectric signal or the second bioelectric signal utilizes the bioinformation sensing sensor on the bioinformation detector to obtain the muscle electrical signal from the arm, and utilizes the wireless EEG acquisition device to collect the EEG signal and transmit it to the bioinformation detector, and the EEG signal is combined with the muscle electrical signal to form the first bioelectric signal or the second bioelectric signal.

[0046] Further, the first user information characteristic data or the first user information characteristic data includes user gender, user age, user height and user weight;

[0047] The first user medical diagnosis lesion site or the second user medical diagnosis lesion site is determined by collecting the user's previous first medical record data or second medical record data and transmitting it to the hospital.

[0048] Furthermore, the wireless EEG acquisition device includes the NeuroHUB series wireless EEG acquisition system, which can acquire brain signals from 64 brain EEG channels; the specific brain acquisition areas of the EEG data are F7, F3, Fz, F4, F8, Cz, C3, C4, O1 and O2;

[0049] The calculation formula of the EEG signal is as follows:

[0050]

[0051] In the formula, R f is the EEG signal, k is the gender adjustment coefficient, n is the number of EEG channels selected for collection, S is the number of EEG channels not collected, R1, R2, ..., R 10 It is the amplitude of EEG signals collected corresponding to F7, F3, Fz, F4, F8, Cz, C3, C4, O1 and O2, in μV.

[0052] Furthermore, the muscle electrical signal M t The calculation formula is as follows:

[0053] M t =k1M h +k2M l +k3M d

[0054] Wherein, k1, k2, k3 are the reciprocals of the distances between the source of the muscle electrical signal and the first user's medical diagnosis lesion site or the second user's medical diagnosis lesion site, M h is the muscle electrical signal of the arm, M l is the leg muscle electrical signal, M d It is the electrical signal of abdominal muscle, in mV.

[0055] Furthermore, the first acquisition vector feature is composed of the first bioelectric signal, the first user information feature data and the first user medical diagnosis lesion site, and the second acquisition vector feature is composed of the second bioelectric signal, the second user information feature data and the second user medical diagnosis lesion site, and the specific composition method is vector feature splicing.

[0056] Furthermore, the vital signs collection and positioning model adopts an improved neural network model, and its improved activation function calculation formula is as follows:

[0057]

[0058] e(X) is the activation function value, X is the input acquisition vector feature, k1, k2, k3 are the reciprocals of the distances between the source location of the muscle electrical signal and the medical diagnosis lesion site of the first user or the medical diagnosis lesion site of the second user, n is the number of EEG channels selected for acquisition, and S is the number of EEG channels not acquired.

[0059] A vital sign data collection system based on artificial intelligence is also provided, the system comprising a biological information monitoring module, a user information registration module, a user information storage module, a medical diagnosis information collection module, a vital sign collection positioning model construction module, and a vital sign data collection module, characterized in that:

[0060] The biological information monitoring module is used to collect and obtain the first bioelectric signal of the human body, and is also used to collect and obtain the second bioelectric signal of the human body;

[0061] The user information registration module is used for user registration to upload the first user information characteristic data, and is also used for uploading the second user information characteristic data;

[0062] The medical diagnosis information collection module is connected to the hospital client to receive the first user medical diagnosis lesion site collected and transmitted from the user hospital end and the second user medical diagnosis lesion site collected and transmitted from the user hospital end;

[0063] The user information storage module is used to receive the first user information characteristic data transmitted by the user information registration module, and also receives the second user information characteristic data, stores the first user's preferred vital signs, and is connected to the medical diagnosis information collection module to receive the first user's medical diagnosis lesion site and the second user's medical diagnosis lesion site;

[0064] The vital signs collection and positioning model construction module receives the first bioelectric signal, the first user information feature data, and the first user's medical diagnosis lesion site, processes to obtain a first collection vector feature, and uses the first collection vector feature and the first user's preferred vital signs to construct a vital signs collection and positioning model;

[0065] The vital signs data acquisition module: the bioinformation detector receives the second bioelectric signal and second user information feature data of the human body, and the second user medical diagnosis lesion site collected and transmitted from the user hospital end, and constructs a second acquisition vector feature based on the second bioelectric signal, the second user information feature data, and the second user medical diagnosis lesion site. The vital signs positioning model processes the second acquisition vector feature to generate a second user preferred vital sign. According to the second user preferred vital sign, the corresponding pathological tissue site of the human body is obtained, and bio-electromagnetic feedback treatment is selectively performed on the pathological tissue site.

[0066] Furthermore, the first bioelectric signal or the second bioelectric signal uses the bioinformation sensing sensor on the bioinformation detector to obtain muscle electrical signals from the arms, thighs, and abdomen, and uses a wireless EEG acquisition device to collect EEG signals and transmit them to the bioinformation detector, and the EEG signals are combined with the muscle electrical signals to form the first bioelectric signal or the second bioelectric signal;

[0067] The calculation formula of the EEG signal is as follows:

[0068]

[0069] In the formula, R f is the EEG signal, k is the gender adjustment coefficient, n is the number of EEG channels selected for collection, S is the number of EEG channels not collected, R1, R2, ..., R 10 is the amplitude of the EEG signals collected corresponding to F7, F3, Fz, F4, F8, Cz, C3, C4, O1, and O2, in μV;

[0070] The muscle electrical signal M t The calculation formula is as follows:

[0071] M t =k1M h +k2M l +k3M d

[0072] Wherein, k1, k2, k3 are the reciprocals of the distances between the source of the muscle electrical signal and the first user's medical diagnosis lesion site or the second user's medical diagnosis lesion site, M his the muscle electrical signal of the arm, M l is the leg muscle electrical signal, M d It is the electrical signal of abdominal muscle, in mV;

[0073] The first acquisition vector feature is composed of the first bioelectric signal, the first user information feature data and the first user medical diagnosis lesion site, and the second acquisition vector feature is composed of the second bioelectric signal, the second user information feature data and the second user medical diagnosis lesion site, and the specific composition method is vector feature splicing.

[0074] Furthermore, the vital signs collection and positioning model adopts an improved neural network model, and its improved activation function calculation formula is as follows:

[0075]

[0076] e(X) is the activation function value, X is the input acquisition vector feature, k1, k2, k3 are the reciprocals of the distances between the source location of the muscle electrical signal and the medical diagnosis lesion site of the first user or the medical diagnosis lesion site of the second user, n is the number of EEG channels selected for acquisition, and S is the number of EEG channels not acquired.

[0077] The present invention provides an artificial intelligence-based vital sign data acquisition system, which collects muscle electrical signals and brain electrophysiological signals from key parts of the body, and combines personal height, age and other related factors that affect physical examination vital sign data, and improves a neural network model suitable for optimal vital sign data acquisition, thereby accurately identifying the user's abnormal vital sign data and obtaining the pathological location, thereby realizing artificial intelligence-based optimized vital sign data acquisition, reducing the workload of medical personnel, and providing a basis for subsequent bio-electromagnetic therapy to select pathological parts of the living body for targeted bio-electromagnetic feedback treatment.

[0078] A combination of multiple embodiments of the present invention can achieve all of the above effects, but it is not required that each embodiment of the present invention achieve all of the above advantages and effects, because each embodiment of the present invention can constitute an independent technical solution and make one or more contributions to the prior art.

[0079] The present invention does not particularly specify the structure of some modules, which shall be subject to the contents recorded in the prior art. The prior art mentioned in the above background technology section and the specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters. The scope of protection of the present invention shall be subject to the contents actually recorded in the claims.

Claims

1. A method for collecting vital sign data based on artificial intelligence, characterized in that: The method comprises the following steps: S1, obtaining a first bioelectric signal of a human body and first user information characteristic data, a first user medical diagnosis lesion site collected and transmitted from a user hospital end, and a first user preferred vital sign; S2, a first acquisition vector feature constructed according to the first bioelectric signal, the first user information feature data, and the first user medical diagnosis lesion site; S3, constructing a vital sign collection and positioning model by using the first collection vector feature and the first user's preferred vital sign; S4. The bio-information detector receives the second bioelectric signal and second user information characteristic data of the human body, and the second user medical diagnosis lesion site collected and transmitted from the user hospital end, and constructs a second acquisition vector feature based on the second bioelectric signal, the second user information characteristic data, and the second user medical diagnosis lesion site. The vital sign positioning model processes the second acquisition vector feature to generate a second user preferred vital sign. According to the second user preferred vital sign, the corresponding pathological tissue site of the human body is obtained, and bio-electromagnetic feedback treatment is selectively performed on the pathological tissue site.

2. The method for collecting vital sign data based on artificial intelligence according to claim 1, characterized in that: The first bioelectric signal or the second bioelectric signal uses the bioinformation sensing sensor on the bioinformation detector to obtain the muscle electrical signal from the arm, and uses the wireless EEG acquisition device to collect the EEG signal and transmit it to the bioinformation detector. The EEG signal is combined with the muscle electrical signal to form the first bioelectric signal or the second bioelectric signal.

3. The method for collecting vital sign data based on artificial intelligence as claimed in claim 1, characterized in that: The first user information characteristic data or the first user information characteristic data includes user gender, user age, user height and user weight; The first user medical diagnosis lesion site or the second user medical diagnosis lesion site is determined by collecting the user's previous first medical record data or second medical record data and transmitting it to the hospital.

4. The method for collecting vital sign data based on artificial intelligence as claimed in claim 2, characterized in that: The wireless EEG acquisition device includes the Boreikon NeuroHUB series wireless EEG acquisition system, which can collect brain signals from 64 brain EEG channels; the specific brain acquisition areas of the EEG data are F7, F3, Fz, F4, F8, Cz, C3, C4, O1 and O2.

5. The method for collecting vital sign data based on artificial intelligence as claimed in claim 4, characterized in that: The muscle electric signal is obtained by processing according to the inverse of the distance between the muscle electric signal source position and the first user medical diagnosis lesion site or the second user medical diagnosis lesion site.

6. The method for collecting vital sign data based on artificial intelligence as claimed in claim 5, characterized in that: The first acquisition vector feature is composed of the first bioelectric signal, the first user information feature data and the first user medical diagnosis lesion site, and the second acquisition vector feature is composed of the second bioelectric signal, the second user information feature data and the second user medical diagnosis lesion site, and the specific composition method is vector feature splicing.

7. The method for collecting vital sign data based on artificial intelligence according to claim 6, characterized in that: The vital signs collection and positioning model adopts an improved neural network model.

8. A vital sign data collection system based on artificial intelligence, the system comprising a biological information monitoring module, a user information registration module, a user information storage module, a medical diagnosis information collection module, a vital sign collection positioning model construction module, and a vital sign data collection module, characterized in that: The biological information monitoring module is used to collect and obtain a first bioelectric signal of a human body, and is also used to collect and obtain a second bioelectric signal of a human body; The user information registration module is used for user registration to upload the first user information characteristic data, and is also used for uploading the second user information characteristic data; The medical diagnosis information collection module is connected to the hospital client to receive the first user medical diagnosis lesion site collected and transmitted from the user hospital end and the second user medical diagnosis lesion site collected and transmitted from the user hospital end; The user information storage module is used to receive the first user information characteristic data transmitted by the user information registration module, and also receives the second user information characteristic data, stores the first user's preferred vital signs, and is connected to the medical diagnosis information collection module to receive the first user's medical diagnosis lesion site and the second user's medical diagnosis lesion site; The vital signs collection and positioning model construction module receives the first bioelectric signal, the first user information feature data, and the first user's medical diagnosis lesion site, processes to obtain a first collection vector feature, and uses the first collection vector feature and the first user's preferred vital signs to construct a vital signs collection and positioning model; The vital signs data acquisition module: the bioinformation detector receives the second bioelectric signal and second user information feature data of the human body, and the second user medical diagnosis lesion site collected and transmitted from the user hospital end, and constructs a second acquisition vector feature based on the second bioelectric signal, the second user information feature data, and the second user medical diagnosis lesion site. The vital signs positioning model processes the second acquisition vector feature to generate a second user preferred vital sign. According to the second user preferred vital sign, the corresponding pathological tissue site of the human body is obtained, and bio-electromagnetic feedback treatment is selectively performed on the pathological tissue site.

9. The vital sign data collection system based on artificial intelligence as claimed in claim 8, characterized in that: The first bioelectric signal or the second bioelectric signal uses the bioinformation sensing sensor on the bioinformation detector to obtain the muscle electrical signal from the arm, and uses the wireless EEG acquisition device to collect the EEG signal and transmit it to the bioinformation detector, and the EEG signal is combined with the muscle electrical signal to form the first bioelectric signal or the second bioelectric signal; The muscle electric signal is obtained by processing the inverse of the distance between the muscle electric signal source position and the first user's medical diagnosis lesion site or the second user's medical diagnosis lesion site; The first acquisition vector feature is composed of the first bioelectric signal, the first user information feature data and the first user medical diagnosis lesion site, and the second acquisition vector feature is composed of the second bioelectric signal, the second user information feature data and the second user medical diagnosis lesion site, and the specific composition method is vector feature splicing.

10. The vital sign data collection system based on artificial intelligence as claimed in claim 9, characterized in that: The vital signs collection and positioning model adopts an improved neural network model.

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