CN116898444A - Intelligent monitoring method and system based on emotion recognition - Google Patents
Intelligent monitoring method and system based on emotion recognition Download PDFInfo
- Publication number
- CN116898444A CN116898444A CN202311004276.2A CN202311004276A CN116898444A CN 116898444 A CN116898444 A CN 116898444A CN 202311004276 A CN202311004276 A CN 202311004276A CN 116898444 A CN116898444 A CN 116898444A
- Authority
- CN
- China
- Prior art keywords
- evoked
- monitoring
- abnormal
- emotion
- abnormal emotion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 71
- 230000008909 emotion recognition Effects 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000002159 abnormal effect Effects 0.000 claims abstract description 67
- 238000011282 treatment Methods 0.000 claims abstract description 50
- 230000008451 emotion Effects 0.000 claims abstract description 49
- 208000024891 symptom Diseases 0.000 claims abstract description 21
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 59
- 230000000763 evoking effect Effects 0.000 claims description 50
- 238000004590 computer program Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 12
- 238000011269 treatment regimen Methods 0.000 claims description 12
- 201000010099 disease Diseases 0.000 claims description 11
- 230000006698 induction Effects 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 7
- 238000013145 classification model Methods 0.000 claims description 5
- 230000006866 deterioration Effects 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 7
- 238000011369 optimal treatment Methods 0.000 abstract description 3
- 208000035475 disorder Diseases 0.000 description 35
- 230000005856 abnormality Effects 0.000 description 10
- 230000007246 mechanism Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 4
- 230000004913 activation Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000001939 inductive effect Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 230000036772 blood pressure Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 206010008111 Cerebral haemorrhage Diseases 0.000 description 1
- 208000031226 Hyperlipidaemia Diseases 0.000 description 1
- 206010020772 Hypertension Diseases 0.000 description 1
- 206010049418 Sudden Cardiac Death Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013019 agitation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 208000029078 coronary artery disease Diseases 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000008921 facial expression Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 208000010125 myocardial infarction Diseases 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 208000014221 sudden cardiac arrest Diseases 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
- A61B5/1128—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique using image analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14546—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring analytes not otherwise provided for, e.g. ions, cytochromes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
Abstract
The invention provides an intelligent monitoring method and system based on emotion recognition, and belongs to the technical field of intelligent monitoring. According to the invention, the emotion abnormal condition is comprehensively analyzed by adopting a plurality of physiological indexes, so that the emotion abnormal recognition accuracy can be greatly improved, the potential symptoms possibly induced by the emotion abnormal condition are determined according to the abnormal emotion category, and the monitoring treatment scheme comprising the alarm signal and the treatment personnel, equipment, modes and the like aiming at the potential symptoms is determined, so that the monitoring personnel can more favorably take the optimal treatment, and the better monitoring treatment effect is obtained.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to an intelligent monitoring method, an intelligent monitoring system, electronic equipment and a computer storage medium based on emotion recognition.
Background
The existing emotion monitoring mode mainly monitors a plurality of physiological indexes (heart rate, blood pressure and the like) of a monitored object through intelligent wearing equipment, and can give an alarm and treat when abnormality exists in the emotion monitoring mode. The monitoring mode can only carry out analysis and judgment on whether the normal physiological range of a single index is exceeded or not to alarm, but the abnormal emotion index is caused by various factors, the abnormal emotion of a monitored object cannot be accurately reflected, and the alarm is easy to be excessive. And, simple alarm treatment mode is unfavorable for the treatment personnel to confirm suitable treatment mode in advance, leads to the treatment effect not good.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present invention specifically provides an intelligent monitoring method, system, electronic device and computer storage medium based on emotion recognition.
The first aspect of the invention provides an intelligent monitoring method based on emotion recognition, which comprises the following steps:
abnormal emotion recognition is carried out according to a plurality of physiological indexes of the monitored object, and abnormal emotion categories are determined;
and determining a potential evoked disorder corresponding to the abnormal emotion according to the abnormal emotion type, and determining a monitoring treatment scheme according to the potential evoked disorder.
Further, the abnormal emotion recognition is performed according to the physiological indexes of the subject, and an abnormal emotion category is determined, including:
obtaining abnormal emotion recognition results according to a plurality of physiological indexes of the monitored object;
and inputting the abnormal emotion recognition result and the physiological indexes into an abnormal emotion classification model to obtain the abnormal emotion category.
Further, the determining the potential evoked disorder corresponding to the abnormal emotion according to the abnormal emotion category comprises the following steps:
determining a plurality of initial evoked symptoms according to the abnormal emotion categories and the disease induction association table;
and evaluating and sequencing each initial evoked disorder based on probability according to the abnormal emotion recognition result to obtain a plurality of potential evoked disorders.
Further, the determining a monitored treatment regimen from the potentially evoked disorder includes:
generating the monitored treatment regimen from a guardian and a device associated with each of the potentially evoked disorders;
the monitored treatment regimen is performed.
Further, the performing the monitored treatment regimen comprises:
monitoring the subject based on an initial monitoring treatment scheme, and evaluating the physiological index of the subject in a specified period at the end time of the specified period;
and when the evaluation result is deterioration, predicting a new potential evoked disorder based on the physiological index of the patient in the appointed period, updating the monitoring treatment scheme according to the new potential evoked disorder, and executing.
Further, the end time of the specified period is determined based on the number of potentially evoked disorders.
Further, the determining the associated guardian and device from each of the potentially evoked disorders, generating the guardian treatment plan from the guardian and device, includes:
screening a specified number of target evoked disorders from a plurality of potential evoked disorders according to the service capacity of a monitoring institution corresponding to the monitored object;
an associated guardian and device is determined from each of the target evoked disorders, and the guardian and device are used to generate the guardian treatment plan.
The second aspect of the invention provides an intelligent monitoring system based on emotion recognition, which comprises an acquisition module, a processing module and a storage module; the processing module is electrically connected with the acquisition module and the storage module;
the memory module is used for storing executable computer program codes;
the acquisition module is at least used for acquiring a plurality of physiological indexes of the monitored object and transmitting the physiological indexes to the processing module;
the processing module is configured to perform the method of any of the preceding claims by invoking the executable computer program code in the storage module.
A third aspect of the present invention provides an electronic device comprising: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the method of any one of the preceding claims.
A fourth aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs a method as claimed in any one of the preceding claims.
The invention has the beneficial effects that:
according to the invention, the emotion abnormal condition is comprehensively analyzed by adopting a plurality of physiological indexes, so that the emotion abnormal recognition accuracy can be greatly improved, the potential symptoms possibly induced by the emotion abnormal condition are determined according to the abnormal emotion category, and the monitoring treatment scheme comprising the alarm signal and the treatment personnel, equipment, modes and the like aiming at the potential symptoms is determined, so that the monitoring personnel can more favorably take the optimal treatment, and the better monitoring treatment effect is obtained.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of an intelligent monitoring method based on emotion recognition according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an intelligent monitoring system based on emotion recognition according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Referring to a flow chart shown in fig. 1, an embodiment of the present invention provides an intelligent monitoring method based on emotion recognition, including the following steps:
abnormal emotion recognition is carried out according to a plurality of physiological indexes of the monitored object, and abnormal emotion categories are determined;
and determining a potential evoked disorder corresponding to the abnormal emotion according to the abnormal emotion type, and determining a monitoring treatment scheme according to the potential evoked disorder.
According to the invention, the emotion abnormal condition is comprehensively analyzed by adopting a plurality of physiological indexes, so that the emotion abnormal recognition accuracy can be greatly improved, the potential symptoms possibly induced by the emotion abnormal condition are determined according to the abnormal emotion category, and the monitoring treatment scheme comprising the alarm signal and the treatment personnel, equipment, modes and the like aiming at the potential symptoms is determined, so that the monitoring personnel can more favorably take the optimal treatment, and the better monitoring treatment effect is obtained.
The physiological indexes can be obtained by monitoring by adopting intelligent wearable equipment (including external equipment, penetrating equipment and the like) as described in the background technology, and mainly comprise heart rate, blood pressure, blood sugar, blood fat and the like; the system can also be acquired and identified by the monitoring camera equipment, and mainly comprises limb actions, facial expressions, areas (such as safe areas and unsafe areas) and the like. Of course, the two modes can be adopted comprehensively, so that the accuracy of emotion recognition results can be improved.
Further, the abnormal emotion recognition is performed according to the physiological indexes of the subject, and an abnormal emotion category is determined, including:
obtaining abnormal emotion recognition results according to a plurality of physiological indexes of the monitored object;
and inputting the abnormal emotion recognition result and the physiological indexes into an abnormal emotion classification model to obtain the abnormal emotion category.
In this embodiment, by comprehensively analyzing a plurality of physiological indexes, it can be determined whether the emotion of the subject at the moment/period is abnormal, and the abnormal degree value may be included in the result. The abnormality determination in this step may be obtained by calculating a weighted average of the degrees to which the respective physiological indexes exceed the normal range, and specifically, the abnormality determination may be performed when the weighted average exceeds an empirical value. Obviously, the abnormality degree value may be determined based on a weighted average and an empirical value.
And constructing and training in advance by using a deep learning algorithm for example to obtain an abnormal emotion classification model, wherein the model can carry out classification prediction on a plurality of input real-time physiological indexes and abnormal emotion recognition results so as to obtain an abnormal emotion category corresponding to the secondary physiological indexes. The training optimization process of the classification model before use belongs to quite conventional prior art and is not described in detail herein.
Further, the determining the potential evoked disorder corresponding to the abnormal emotion according to the abnormal emotion category comprises the following steps:
determining a plurality of initial evoked symptoms according to the abnormal emotion categories and the disease induction association table;
and evaluating and sequencing each initial evoked disorder based on probability according to the abnormal emotion recognition result to obtain a plurality of potential evoked disorders.
In this embodiment, the disease-induced association table may be determined manually or by statistical analysis of the system, and the basic structure of the disease-induced association table is as follows:
| the existing diseases | Abnormal emotion category | Inducing a condition |
| Hypertension and hyperlipidemia | N-level activation (N represents the degree of activation, the greater the activation) | Cerebral hemorrhage and myocardial infarction … |
| Coronary heart disease | N-level agitation/M-level depression | Sudden cardiac arrest |
| … | … | … |
The disease-inducing correlation table reflects the diseases that a subject with certain basic diseases will induce under certain emotion, and a plurality of initial induced symptoms are calculated by matching the disease-inducing correlation table. Meanwhile, the abnormal degree value involved in the abnormal emotion recognition result is considered to further evaluate and sort the induction probability of each initial induction symptom, and one or more initial induction symptoms with the prior induction probability (i.e. high induction probability) are selected as potential induction symptoms.
The evaluation calculation of the evoked probability may determine a new weighted average value by comprehensively considering the first sub-abnormality degree value, which is the abnormality of the physiological index associated with each initial evoked disorder, and thereby determine the second sub-abnormality degree value corresponding to the initial evoked disorder. At this time, the abnormality degree value includes two contents, namely, an abnormality degree value representing the overall abnormality degree and a first sub abnormality degree value representing the abnormality degree of a single physiological index.
Further, the determining a monitored treatment regimen from the potentially evoked disorder includes:
generating the monitored treatment regimen from a guardian and a device associated with each of the potentially evoked disorders;
the monitored treatment regimen is performed.
In this embodiment, after determining the disease type possibly induced by the current abnormal emotion type of the monitored object, the monitored person and the required equipment to be treated can be determined, so that the relevant person can be scheduled to carry the required equipment for treatment in time, and the monitoring effect is improved.
Further, the performing the monitored treatment regimen comprises:
monitoring the subject based on an initial monitoring treatment scheme, and evaluating the physiological index of the subject in a specified period at the end time of the specified period;
and when the evaluation result is deterioration, predicting a new potential evoked disorder based on the physiological index of the patient in the appointed period, updating the monitoring treatment scheme according to the new potential evoked disorder, and executing.
In this embodiment, after the monitoring person executes the monitoring treatment scheme in place for a period of time, the change condition of the physiological index of the monitored object in the treatment stage can be evaluated, if the evaluation result is good, the monitoring treatment scheme is effective, otherwise (deterioration or no obvious change) the monitoring treatment scheme is ineffective, at this time, the physiological index in the monitoring treatment period is predicted again according to the above-mentioned mode, and the updating of the monitoring treatment scheme is further realized, and the cycle is repeated for a plurality of times.
The evaluation of the change condition of the physiological index may be based on a weighted average of the degrees of all physiological indexes exceeding the normal range, or may be based on the weighted average of several physiological indexes associated with the corresponding potentially induced symptoms, and may be specifically determined based on the specific type of the potentially induced symptoms, which is not limited by the present invention. The length of the designated period of time may also be determined based on the specific type of the potentially induced disorder, and may specifically take into account the resulting jeopardy, treatment urgency, etc. of the potentially induced disorder, for example, when the resulting jeopardy is no disability risk, disability, mortality, etc., the treatment urgency is general urgency, special urgency, etc., the designated period of time is progressively shorter, so that the monitoring treatment effect may be improved.
Of course, the update of the monitoring treatment plan in this embodiment may also be determined based on the feedback signal of the relevant person, for example, output to the terminal device of the on-site monitor person or coordinator first when the evaluation result is poor, e.g. "whether the physiological index is not optimized, is a new monitoring treatment plan generated/executed? The on-site guardianship personnel or the coordinator feeds back the information, if the feedback is 'confirmation', a new guardianship treatment scheme is generated/executed, and if the feedback is 'refusal', the physiological indexes can be evaluated, output and other operations after the appointed duration.
Further, the end time of the specified period is determined based on the number of potentially evoked disorders.
In addition to the aforementioned factors of harmfulness, urgency of treatment, etc., the present embodiment determines the field end of a given period according to the number of potentially induced conditions. Specifically, the more the determined potential induced symptoms are, the higher the uncertainty of the subsequent health trend of the monitored subject is, and the greater the monitoring treatment difficulty is correspondingly. For this, the present invention determines the update timing (i.e. the duration between the end time and the initial time of the specified period) for updating the monitoring treatment plan according to the number of the potential induced symptoms, specifically, the greater the number of the potential induced symptoms, the smaller the duration between the end time and the initial time of the specified period, i.e. the more advanced the monitoring treatment plan is updated, so that the higher-frequency update calculation of the complex health trend can be realized. Of course, the embodiment may also implement the above-mentioned output and feedback schemes, which are not described in detail.
Further, the determining the associated guardian and device from each of the potentially evoked disorders, generating the guardian treatment plan from the guardian and device, includes:
screening a specified number of target evoked disorders from a plurality of potential evoked disorders according to the service capacity of a monitoring institution corresponding to the monitored object;
an associated guardian and device is determined from each of the target evoked disorders, and the guardian and device are used to generate the guardian treatment plan.
In this embodiment, the present invention further screens the previously identified potentially-evoked disorders for a specified number of the previously-identified potentially-evoked disorders to identify one or more target evoked disorders. The screening process considers the service capacity of the corresponding monitoring mechanism, when the service capacity of the monitoring mechanism is strong, the types, the quantity and the like of personnel and equipment which can be coordinated are sufficient, high-strength synchronous response to a plurality of monitoring treatment conditions can be realized, and the monitoring mechanism with poor service capacity can not realize the problem. Therefore, the specified number of target induced symptoms are screened out based on the service capacity of the monitoring mechanism, the overall monitoring treatment effect of the monitoring mechanism can be improved, and the use efficiency of personnel and equipment is improved.
The service capacity of the monitoring mechanism can be determined at least according to the number of personnel and equipment, the number of monitored objects and the like, even the distribution situation of abnormal occurrence time in the monitoring treatment statistical data can be considered, and the designated number can be set to be more in a small abnormal occurrence period (such as late night), so that the better response to the uncertain situation possibly occurring by the monitored objects can be improved; during periods of greater occurrence of anomalies (e.g., daytime), the designated number can be set to be small to ensure the ability to handle other subjects who may need monitoring treatment by reducing the number of personnel and equipment uses of a single subject.
As shown in fig. 2, the intelligent monitoring system based on emotion recognition according to the embodiment of the invention comprises an acquisition module, a processing module and a storage module; the processing module is connected with the acquisition module and the storage module;
the memory module is used for storing executable computer program codes;
the acquisition module is at least used for acquiring a plurality of physiological indexes of the monitored object and transmitting the physiological indexes to the processing module;
the processing module is configured to perform the method of any of the preceding claims by invoking the executable computer program code in the storage module.
The embodiment of the invention also discloses an electronic device, which comprises: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the method as described in the previous embodiment.
The embodiment of the invention also discloses a computer storage medium, and a computer program is stored on the storage medium, and when the computer program is run by a processor, the computer program executes the method according to the previous embodiment.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable load balancing apparatus, such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (6)
1. An intelligent monitoring method based on emotion recognition is characterized by comprising the following steps:
abnormal emotion recognition is carried out according to a plurality of physiological indexes of the monitored object, and abnormal emotion categories are determined;
determining a potential evoked disorder corresponding to the abnormal emotion according to the abnormal emotion type, and determining a monitoring treatment scheme according to the potential evoked disorder;
the determining the potential evoked disorder corresponding to the abnormal emotion according to the abnormal emotion category comprises the following steps:
determining a plurality of initial evoked symptoms according to the abnormal emotion categories and the disease induction association table;
evaluating and sequencing each initial evoked disorder based on probability according to the abnormal emotion recognition result to obtain a plurality of potential evoked disorders;
the determining a monitored treatment regimen from the potentially evoked disorder, comprising:
generating the monitored treatment regimen from a guardian and a device associated with each of the potentially evoked disorders;
executing the monitored treatment regimen;
the executing the monitored treatment regimen comprises:
monitoring the subject based on an initial monitoring treatment scheme, and evaluating the physiological index of the subject in a specified period at the end time of the specified period;
when the evaluation result is deterioration, predicting a new potential evoked disorder based on the physiological index of the patient in the appointed period, updating the monitoring treatment scheme according to the new potential evoked disorder, and executing;
wherein the end time of the specified period is determined according to the number of the potential induced symptoms, specifically, the more the number of the potential induced symptoms is, the smaller the duration between the end time and the initial time of the specified period is, namely, the more advanced the monitoring treatment scheme is updated.
2. The intelligent monitoring method based on emotion recognition of claim 1, wherein: the abnormal emotion recognition is performed according to a plurality of physiological indexes of the monitored object, and an abnormal emotion category is determined, including:
obtaining abnormal emotion recognition results according to a plurality of physiological indexes of the monitored object;
and inputting the abnormal emotion recognition result and the physiological indexes into an abnormal emotion classification model to obtain the abnormal emotion category.
3. The intelligent monitoring method based on emotion recognition of claim 1, wherein: the determining the associated guardian and device from each of the potentially evoked disorders, generating the guardian treatment plan from the guardian and device, comprising:
screening a specified number of target evoked disorders from a plurality of potential evoked disorders according to the service capacity of a monitoring institution corresponding to the monitored object;
an associated guardian and device is determined from each of the target evoked disorders, and the guardian and device are used to generate the guardian treatment plan.
4. An intelligent monitoring system based on emotion recognition comprises an acquisition module, a processing module and a storage module; the processing module is electrically connected with the acquisition module and the storage module;
the memory module is used for storing executable computer program codes;
the acquisition module is at least used for acquiring a plurality of physiological indexes of the monitored object and transmitting the physiological indexes to the processing module;
the method is characterized in that: the processing module for performing the method of any of claims 1-3 by invoking the executable computer program code in the storage module.
5. An electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the method is characterized in that: the processor invokes the executable program code stored in the memory to perform the method of any one of claims 1-3.
6. A computer storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311004276.2A CN116898444A (en) | 2023-08-10 | 2023-08-10 | Intelligent monitoring method and system based on emotion recognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311004276.2A CN116898444A (en) | 2023-08-10 | 2023-08-10 | Intelligent monitoring method and system based on emotion recognition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116898444A true CN116898444A (en) | 2023-10-20 |
Family
ID=88364891
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311004276.2A Pending CN116898444A (en) | 2023-08-10 | 2023-08-10 | Intelligent monitoring method and system based on emotion recognition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116898444A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2279246C2 (en) * | 2004-06-08 | 2006-07-10 | Анатолий Иванович Коряков | Method for predicting ischemic cardiac disease (icd) with the help of holter's electrocardiogram monitoring |
| UA24909U (en) * | 2006-11-24 | 2007-07-25 | Shupyk Nat Med Acad Adv Train | Method of diagnostics of the stress induced heart rhythm disturbances in patients with acute psychoemotional stress |
| US7493264B1 (en) * | 2001-06-11 | 2009-02-17 | Medco Health Solutions, Inc, | Method of care assessment and health management |
| US20160147958A1 (en) * | 2013-07-26 | 2016-05-26 | Koninklijke Philips N.V. | Computerization and visualization of clinical rules and definitions for patient monitoring systems |
| US20180184963A1 (en) * | 2015-05-19 | 2018-07-05 | Beyond Verbal Communication Ltd | System and method for improving emotional well-being by vagal nerve stimulation |
| US20200303073A1 (en) * | 2019-03-19 | 2020-09-24 | Cambridge Cognition Limited | Method and Uses of Diagnosing and Recommending Treatment for a Psychotic Disorder |
| WO2023075746A1 (en) * | 2021-10-25 | 2023-05-04 | Earkick, Inc. | Detecting emotional state of a user |
-
2023
- 2023-08-10 CN CN202311004276.2A patent/CN116898444A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7493264B1 (en) * | 2001-06-11 | 2009-02-17 | Medco Health Solutions, Inc, | Method of care assessment and health management |
| RU2279246C2 (en) * | 2004-06-08 | 2006-07-10 | Анатолий Иванович Коряков | Method for predicting ischemic cardiac disease (icd) with the help of holter's electrocardiogram monitoring |
| UA24909U (en) * | 2006-11-24 | 2007-07-25 | Shupyk Nat Med Acad Adv Train | Method of diagnostics of the stress induced heart rhythm disturbances in patients with acute psychoemotional stress |
| US20160147958A1 (en) * | 2013-07-26 | 2016-05-26 | Koninklijke Philips N.V. | Computerization and visualization of clinical rules and definitions for patient monitoring systems |
| US20180184963A1 (en) * | 2015-05-19 | 2018-07-05 | Beyond Verbal Communication Ltd | System and method for improving emotional well-being by vagal nerve stimulation |
| US20200303073A1 (en) * | 2019-03-19 | 2020-09-24 | Cambridge Cognition Limited | Method and Uses of Diagnosing and Recommending Treatment for a Psychotic Disorder |
| CN113840569A (en) * | 2019-03-19 | 2021-12-24 | 剑桥认知有限公司 | Methods and uses for diagnosing psychiatric disorders and recommending treatment for psychiatric disorders |
| WO2023075746A1 (en) * | 2021-10-25 | 2023-05-04 | Earkick, Inc. | Detecting emotional state of a user |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6530085B2 (en) | Analysis of Health Phenomena Using Recursive Neural Network | |
| CN111681726B (en) | Processing method, device, equipment and medium of electronic medical record data | |
| JP6909078B2 (en) | Disease onset prediction device, disease onset prediction method and program | |
| JP6975253B2 (en) | Learning and applying contextual similarity between entities | |
| US11923094B2 (en) | Monitoring predictive models | |
| CN113345577B (en) | Diagnosis and treatment auxiliary information generation method, model training method, device, equipment and storage medium | |
| CN113080986A (en) | Method and system for detecting exercise fatigue based on wearable equipment | |
| CN106104539B (en) | Use the optimization of the regenerated alarm setting for alarm consulting of alarm | |
| CN107145715B (en) | Clinical medicine intelligence discriminating gear based on electing algorithm | |
| CN104823195A (en) | Method and system to reduce nuisance alarm load in clinical setting | |
| WO2022233027A1 (en) | Wearable device-based method and system for motion fatigue detection | |
| WO2016104498A1 (en) | Drowsiness estimation device and drowsiness estimation program | |
| Chen et al. | Edge2Analysis: a novel AIoT platform for atrial fibrillation recognition and detection | |
| Bajpai et al. | Early Prediction of Cardiac Arrest Using Hybrid Machine Learning Models | |
| CN115719640B (en) | Chinese medicine primary and secondary symptom recognition system, device, electronic equipment and storage medium thereof | |
| CN116898444A (en) | Intelligent monitoring method and system based on emotion recognition | |
| CN111785340A (en) | Medical data processing method, device, equipment and storage medium | |
| Tomoda et al. | Predicting glaucomatous progression with piecewise regression model from heterogeneous medical data | |
| CN115376692A (en) | Health data processing method and device, electronic equipment and storage medium | |
| JP7346419B2 (en) | Learning and applying contextual similarities between entities | |
| CN116741414B (en) | Intelligent abnormal emotion relieving method and system | |
| CN113712513A (en) | Physiological detection method and device | |
| Bakar et al. | HDP: heart disease prediction tool using neural network | |
| Karmakar et al. | HeartHealth: an intelligent model for multi-attribute based heart condition monitoring using fuzzy-TOPSIS method | |
| CN113223698B (en) | Emergency hierarchical processing method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |