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CN119015036A - A medical restraint belt intelligent management method and system - Google Patents

A medical restraint belt intelligent management method and system Download PDF

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Publication number
CN119015036A
CN119015036A CN202411094784.9A CN202411094784A CN119015036A CN 119015036 A CN119015036 A CN 119015036A CN 202411094784 A CN202411094784 A CN 202411094784A CN 119015036 A CN119015036 A CN 119015036A
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Prior art keywords
restraint
constraint
biological information
central platform
intensity
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CN119015036B (en
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胡兰英
张晶
杨艳
吴晓华
房丽
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Affiliated Hospital Of Yangzhou University (yangzhou First People's Hospital)
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Affiliated Hospital Of Yangzhou University (yangzhou First People's Hospital)
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Priority claimed from CN202411094784.9A external-priority patent/CN119015036B/en
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F5/00—Orthopaedic methods or devices for non-surgical treatment of bones or joints; Nursing devices ; Anti-rape devices
    • A61F5/37—Restraining devices for the body or for body parts; Restraining shirts
    • 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 for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6831—Straps, bands or harnesses
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6843—Monitoring or controlling sensor contact pressure
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

本发明公开了一种医疗约束带智能管理方法及系统，涉及约束带智能控制技术领域，包括在约束带上安装传感器收集约束者生物信息进行初步约束并将收集的生物信息传输至中央平台；中央平台根据约束者生物信息定义个人约束强度分级，实时调整约束强度；中央平台接收医护人员语音命令控制约束带，并检测约束者异常行为自动预警；医护人员远程连接中央平台进行约束监测；中央平台将约束者生物信息和个人约束强度分级进行分类存储。本发明通过医疗约束带安装传感器收集约束者生物信息，并根据生物信息计算约束者个人的约束强度分级，同步实时对约束者约束强度进行调整，从而使约束带的使用更加贴合约束者。

The present invention discloses a medical restraint belt intelligent management method and system, which relates to the field of intelligent control technology of restraint belts, including installing sensors on the restraint belt to collect biological information of the restrainer for preliminary restraint and transmitting the collected biological information to the central platform; the central platform defines the personal restraint strength classification according to the restrainer's biological information, and adjusts the restraint strength in real time; the central platform receives voice commands from medical staff to control the restraint belt, and detects abnormal behavior of the restrainer and automatically warns; the medical staff remotely connects to the central platform for restraint monitoring; the central platform classifies and stores the restrainer's biological information and personal restraint strength classification. The present invention collects the restrainer's biological information by installing sensors on the medical restraint belt, calculates the restrainer's personal restraint strength classification based on the biological information, and simultaneously adjusts the restrainer's restraint strength in real time, so that the use of the restraint belt is more suitable for the restrainer.

Description

Medical restraint strap intelligent management method and system
Technical Field
The invention relates to the technical field of intelligent control of restraint bands, in particular to an intelligent management method and system for medical restraint bands.
Background
With the continuous progress of medical technology and the intelligent development of medical facilities in recent years, medical restraint belts are increasingly widely applied in medical institutions, traditional medical restraint belts are mainly used for limiting activities of patients to prevent self injuries or injuries to others, however, the use of traditional restraint belts often lacks intelligent management, depending on manual adjustment and monitoring of medical staff, the medical staff usually needs to check the state of the patients regularly and manually adjust the tightness of the restraint belts to ensure the safety and comfort of the patients, and the manual operation not only consumes time and labor, but also is easy to cause human negligence or misjudgment, so that the situation of over-constraint or under-constraint occurs, and therefore, the problem of insufficient intellectualization and convenience in the use process of the traditional restraint belts needs to be solved.
Disclosure of Invention
The present invention has been made in view of the above-mentioned problems occurring in the conventional medical restraint band intelligent management method and system.
Therefore, the invention aims to solve the problem that the traditional restraint strap is not intelligent and convenient in the use process.
In order to solve the technical problems, the invention provides the following technical scheme: the intelligent management method of the medical restraint strap comprises the steps of installing a sensor on the restraint strap to collect biological information of a restraint person for preliminary restraint and transmitting the collected biological information to a central platform; the central platform defines personal constraint intensity classification according to the biological information of the constrainer, and adjusts the constraint intensity in real time; the central platform receives the voice command control restriction belts of medical staff and detects abnormal behaviors of the restrainers for automatic early warning; medical staff remotely connect the central platform for constraint monitoring; the central platform stores the restraint biological information and the personal restraint intensity in a classified mode.
As a preferable scheme of the intelligent management method of the medical restraint belt, the invention comprises the following steps: the method is characterized in that a sensor is arranged on the restraint belt to collect biological information of the restraint person for preliminary restraint and transmit the collected biological information to the central platform, the biological information sensor is arranged on the restraint belt to collect biological information of the restraint person, including heart rate, skin electric reaction, blood oxygen saturation, skin pressure, body type and activity posture information, and the restraint person is preliminary restrained and fixed according to the collected biological information until the restraint belt is matched with the body type of the restraint person and skin pressure is generated, and the biological information sensor transmits the collected biological information of the restraint person to the central platform for pretreatment through a network.
As a preferable scheme of the intelligent management method of the medical restraint belt, the invention comprises the following steps: the central platform defines personal constraint intensity classification according to the biological information of the constrainer, adjusts the constraint intensity in real time, namely, the central platform extracts a mean value, a standard deviation, a minimum value, a maximum value, a skewness and a kurtosis as characteristics according to the collected biological information of the constrainer, normalizes each characteristic to form a characteristic F n, analyzes the principal component of the normalized characteristic F n, comprises,
Calculating a covariance matrix:
where Σ is the covariance matrix, m is the feature total number, mu F is the feature mean, For the ith feature, T is a transpose operation;
decomposing the covariance matrix to obtain a matrix eigenvalue lambda and a matrix eigenvector v:
selecting matrix eigenvectors corresponding to the first k matrix eigenvalues, and constructing a matrix eigenvector matrix V:
Projecting the features into a matrix feature vector matrix V to obtain a dimension reduction feature vector Z:
The method comprises the steps of constructing a multi-layer perceptron model, wherein the multi-layer perceptron model comprises an input layer, a hidden layer and an output layer, the input layer is in a dimension-reducing feature vector, the output layer is in a biological information index BI, and the range of values is 0 to 1;
Performing multi-layer perceptron model iterative training by using a training set, performing model parameter optimization by using a loss function and an Adam optimizer, and inputting a dimension reduction feature vector Z i into the multi-layer perceptron model to obtain a biological information index BI of a constraint;
Taking skin pressure as a constraint intensity grading standard, wherein the difference value between each constraint intensity grade is the same, setting the maximum skin pressure of constraint band constraint as P max, the minimum skin pressure as P min and the grading number as b, and calculating the skin pressure value delta P of each constraint intensity grade of the person of the constraint person:
Defining fuzzy sets and rules according to the calculated biological information indexes BI of the constrainer, mapping the biological information indexes BI into the fuzzy sets, defining a fuzzy membership function, calculating membership x i of each fuzzy set, and calculating constraint intensity level S of the constrainer according to the membership of the fuzzy set:
calculating constraint intensity according to the constraint intensity level of the constraint:
Pe＝Pmin+(S-1)*ΔP；
Where P e is the skin pressure value of the constraint intensity.
As a preferable scheme of the intelligent management method of the medical restraint belt, the invention comprises the following steps: the central platform receives the voice command of medical staff to control the restriction band, the central platform collects the voice of the medical staff through the microphone array to carry out denoising and filtering treatment, a convolutional neural network model is used for training the voice of the medical staff, a unique identification signal is generated according to the voice of the medical staff, when the microphone array collects the voice, the voice identification signal is firstly generated through the convolutional neural network, when the identification signals are consistent, the central platform starts to carry out voice analysis, converts the voice into a text command to carry out restriction band control, and when the identification signals are inconsistent, voice analysis is refused;
The band control commands include constraint, tighten, loosen, and un-constraint, wherein,
The restraint means that the restraint belt automatically restrains the restraint person preliminarily until skin pressure is generated;
tightening the finger strap increases the restraint intensity of the restraint by one level, and the tightening command is not effective when the skin pressure value of the restraint intensity of the restraint reaches the maximum skin pressure;
Releasing the finger restraint strap reduces the restraint intensity of the restraint by one level, and the release command is not effective when the skin pressure value of the restraint intensity of the restraint reaches the minimum skin pressure;
the unconstrained means that the constraint belt releases the constraint state of the constraint taker.
As a preferable scheme of the intelligent management method of the medical restraint belt, the invention comprises the following steps: the automatic early warning for detecting the abnormal behavior of the restraint means that the restraint belt automatically detects the moving gesture of the restraint by a sensor, when the restraint is detected to frequently move and the skin pressure is continuously changed, the restraint is judged to be in a struggling state, and the central platform automatically improves the restraint strength by one grade and informs a worker to check the restraint; when the constraint releasing command is not received, the constraint intensity skin pressure value is detected to be 0, the constraint person is judged to escape, and the central platform automatically sends out an alarm to inform the staff.
As a preferable scheme of the intelligent management method of the medical restraint belt, the invention comprises the following steps: the medical staff remote connection central platform carries out constraint monitoring and indicates that the medical staff looks over the constraint person and constraint intensity through the mobile terminal remote connection central platform to receive the notice of central platform through mobile terminal, central platform automatically generates constraint belt control record and sends to medical staff's mobile terminal after receiving medical staff's constraint belt voice control command.
As a preferable scheme of the intelligent management method of the medical restraint belt, the invention comprises the following steps: the central platform performs classified storage on the constraint biological information and the personal constraint intensity classification, namely the central platform generates constraint classification information according to the collected constraint biological information, and stores the personal constraint intensity classification into the constraint classification as a preset scheme, and when the central platform detects the same constraint biological information again through a constraint belt, the central platform automatically invokes the stored constraint personal constraint intensity classification.
It is another object of the present invention to provide a medical band intelligent management system, comprising,
The information collection module is used for collecting biological information of the restraint through the sensor and transmitting the biological information to the central platform;
The central processing module is used for receiving the biological information of the restraint person, preprocessing, defining personal restraint intensity classification according to the biological information of the restraint person, adjusting the restraint intensity in real time, and receiving the voice instruction control restraint belt of the medical staff;
the remote monitoring module is used for remotely connecting the mobile terminal of the medical staff and sending information to the mobile terminal for the medical staff to check;
The storage module is used for generating a constraint person classification according to the biological information of the constraint person, and storing the personal constraint intensity of the constraint person in the constraint person classification in a grading manner to be used as a preset scheme for calling.
A computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the medical restraint strap intelligent management method when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the medical restraint strap intelligent management method described above.
The invention has the beneficial effects that: according to the invention, biological information of the restraint is collected through the medical restraint belt installation sensor, personal restraint intensity grading of the restraint is calculated according to the biological information, and the restraint intensity of the restraint is synchronously and real-timely adjusted, so that the restraint belt is used for being more attached to the restraint, medical staff can be assisted in controlling the restraint belt and monitoring the restraint through voice control and abnormal behavior detection, the applicability of the restraint belt is improved, the safety of the restraint and the medical staff is ensured, and finally, calculation resources are saved and the work efficiency of the restraint belt is improved through storing restraint grading preset schemes of the restraint.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a medical restraint strap intelligent management method.
FIG. 2 is a schematic flow chart of medical band control.
Fig. 3 is a schematic structural diagram of a medical restraint strap intelligent management system.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 and 2, a first embodiment of the present invention provides an intelligent medical restraint strap management method, which includes,
S1, installing a sensor on a constraint belt to collect biological information of a constraint person for preliminary constraint and transmitting the collected biological information to a central platform;
specifically, installing a sensor on the restraint belt to collect the biological information of the restraint person for preliminary restraint and transmitting the collected biological information to the central platform means that installing a biological information sensor on the restraint belt to collect the biological information of the restraint person including heart rate, skin electric reaction, blood oxygen saturation, skin pressure, body type and activity posture information, and fixing the restraint person according to the collected biological information for preliminary restraint until the restraint belt is matched with the body type of the restraint person and skin pressure is generated, wherein the biological information sensor transmits the collected biological information of the restraint person to the central platform for pretreatment through a network.
Through installing multiple biological information sensor on the restraint strap, can collect patient's heart rate, skin electric reaction, blood oxygen saturation, skin pressure, size and activity gesture information in real time, these information can provide patient's comprehensive physiological condition, help doctor and nurse to evaluate and manage patient's health more accurately, the process of preliminary restraint ensures restraint strap adaptation patient's size, produce appropriate skin pressure, promote the validity and the comfort level of restraint, reduce patient's discomfort and skin injury risk, biological information sensor carries out preliminary treatment through network with the biological information real-time transmission of patient who gathers to central platform, this kind of real-time data transmission and processing mechanism, the timeliness and the accuracy of medical monitoring have been improved, the risk of incident has been reduced.
S2, defining personal constraint intensity classification by the central platform according to biological information of the constrainer, and adjusting constraint intensity in real time;
Specifically, the central platform defines personal constraint intensity classification according to the biological information of the constraint person, and real-time adjustment of the constraint intensity means that the central platform extracts mean value, standard deviation, minimum value, maximum value, skewness and kurtosis as characteristics according to heart rate, galvanic skin response and blood oxygen saturation in the collected biological information of the constraint person;
The calculation formulas of the skewness sk and the kurtosis ku are as follows:
Where q is the total number of data for heart rate, galvanic skin response, and blood oxygen saturation, L i is the ith data, μ L is the data mean, σ L is the data standard deviation;
and normalizing each feature to form a feature F n, and performing principal component analysis on the normalized feature F n, including,
Calculating a covariance matrix:
where Σ is the covariance matrix, m is the feature total number, mu F is the feature mean, For the ith feature, T is a transpose operation;
decomposing the covariance matrix to obtain a matrix eigenvalue lambda and a matrix eigenvector v:
∑v＝λv；
det(∑-λI)＝0；
Wherein v is a matrix eigenvector, det is a determinant, lambda is a matrix eigenvalue, and I is a unit matrix;
selecting matrix eigenvectors corresponding to the first k matrix eigenvalues, and constructing a matrix eigenvector matrix V:
V＝[v1,v2,……,vk]；
Projecting the features into a matrix feature vector matrix V to obtain a dimension reduction feature vector Z:
wherein Z i is the ith dimension-reduction feature vector;
The method comprises the steps of constructing a multi-layer perceptron model, wherein the multi-layer perceptron model comprises an input layer, a hidden layer and an output layer, the input layer is in a dimension-reducing feature vector, the hidden layer is provided with a plurality of layers, each layer uses a sigmoid function as an activation function, the output layer is in a biological information index BI, and the range of values is 0 to 1;
Performing iterative training of the multi-layer perceptron model by using a training set, performing iterative optimization of model parameters by using a loss function and an Adam optimizer until the loss is not obviously reduced in a continuous iterative process, stopping iterative output of model parameters to update the multi-layer perceptron model, and inputting a dimension-reducing feature vector Z i into the multi-layer perceptron model to obtain a biological information index BI of a restraint;
Taking skin pressure as a constraint intensity grading standard, wherein the difference value between each constraint intensity grade is the same, setting the maximum skin pressure of constraint band constraint as P max, the minimum skin pressure as P min and the grading number as b, and calculating the skin pressure value delta P of each constraint intensity grade of the person of the constraint person:
defining a fuzzy set and rules according to the calculated constraint biological information index BI, mapping the biological information index BI into the fuzzy set and defining a fuzzy membership function:
Wherein x low、xme and x hi are membership functions of fuzzy sets, and a, b and c are thresholds of fuzzy sets, respectively;
Calculating membership degree x i of each fuzzy set, and calculating constraint intensity grade S of a constraint according to the membership degree of the fuzzy set:
calculating constraint intensity according to the constraint intensity level of the constraint:
Pe＝Pmin+(S-1)*ΔP；
Where P e is the skin pressure value of the constraint intensity.
The sensor arranged on the restraint strap can collect biological information such as heart rate, galvanic skin response, blood oxygen saturation and the like of the restraint person in real time, the physiological state of the restraint person can be comprehensively analyzed by extracting the mean value, standard deviation, minimum value, maximum value, skewness and kurtosis of the data, the characteristics can provide finer and more accurate individual health condition assessment, the more proper restraint intensity can be formulated, the dimensional difference between different characteristics can be eliminated by carrying out normalization processing on the extracted characteristics, the data can be compared and analyzed on the same scale, the Principal Component Analysis (PCA) is used for carrying out dimension reduction processing on the normalized characteristics, the redundancy of the data can be reduced, the main information is reserved, the efficiency and the accuracy of subsequent analysis and processing are improved, a multi-layer perceptron (MLP) model is constructed, the biological information characteristic is used for training, the accurate prediction of the biological information index (BI) of the restraint can be realized, the MLP model has strong nonlinear mapping capability, the relation between complex physiological characteristics and restraint intensity can be captured, thereby providing a more accurate restraint intensity adjustment scheme, the biological information index (BI) can be mapped into the fuzzy set by defining the fuzzy set and the fuzzy membership function to process uncertainty and ambiguity information, the fuzzy logic processing method can provide a more flexible and accurate restraint intensity adjustment scheme, adapt to individual differences of different patients, can realize accurate adjustment of restraint intensity by calculating membership degree of each fuzzy set and calculating restraint intensity level of the restraint according to the membership degree of the fuzzy set, and utilizes the fuzzy set defined by the biological information index (BI), the complex physiological information can be converted into specific constraint intensity adjusting parameters, so that the constraint intensity is ensured to be matched with the physiological state of the constraint.
S3, the central platform receives the voice command control restriction belts of the medical staff and detects abnormal behaviors of the restrainers for automatic early warning;
Specifically, the central platform receives a voice command control restriction band of medical staff, the central platform collects voice of the medical staff through the microphone array to perform denoising and filtering treatment, a convolutional neural network model is used for training the voice of the medical staff, a unique identification signal is generated according to the voice of the medical staff, when the microphone array collects the voice, the voice is firstly generated through the convolutional neural network to generate the identification signal, when the identification signals are consistent, the central platform starts to perform voice analysis, converts the voice into a text command to perform restriction band control, and when the identification signals are inconsistent, voice analysis is refused to be performed;
The band control commands include constraint, tighten, loosen, and un-constraint, wherein,
The restraint means that the restraint belt automatically restrains the restraint person preliminarily until skin pressure is generated;
tightening the finger strap increases the restraint intensity of the restraint by one level, and the tightening command is not effective when the skin pressure value of the restraint intensity of the restraint reaches the maximum skin pressure;
Releasing the finger restraint strap reduces the restraint intensity of the restraint by one level, and the release command is not effective when the skin pressure value of the restraint intensity of the restraint reaches the minimum skin pressure;
the unconstrained means that the constraint belt releases the constraint state of the constraint taker.
The central platform collects voice signals of medical staff through the microphone array, denoising and filtering processing are carried out, the microphone array can improve the capturing quality of the voice signals, background noise and irrelevant signals can be removed through denoising and filtering processing, the definition and accuracy of the voice signals are guaranteed, the step guarantees the basic data quality of subsequent voice recognition and analysis, the voice of the medical staff is trained through a Convolutional Neural Network (CNN), unique recognition signals are generated, the CNN model learns the voice characteristics of the medical staff, the unique voice recognition signals of each medical staff can be generated, the accuracy of voice recognition is improved, unauthorized staff can be effectively prevented from restricting a band through voice control, the safety of a system is enhanced, and when the microphone array collects voice, the recognition signals are generated through the convolutional neural network. If the recognition signals are consistent, the central platform performs voice analysis, converts voice into text commands to perform restraint strap control, the step ensures that only verified voice signals can trigger restraint strap control commands, the possibility of misidentification and misoperation is avoided, the voice analysis converts natural language commands into machine executable instructions, the intelligence and usability of the system are improved, the comfort and safety of patients can be improved through accurate restraint strength control, and medical risks caused by improper restraint are reduced.
Further, detecting abnormal behaviors of the restraint, namely automatically detecting the moving gesture of the restraint by a sensor, judging that the restraint is in a struggling state when the restraint is detected to frequently move and the skin pressure is continuously changed, automatically improving the restraint strength by one level by a central platform, and informing a worker to check the restraint; when the constraint releasing command is not received, the constraint intensity skin pressure value is detected to be 0, the constraint person is judged to escape, and the central platform automatically sends out an alarm to inform the staff.
The restraint belt monitors the movable posture of the restraint in real time through the sensor, and can capture fine posture change. The data collected by the sensor comprises the frequency, amplitude and direction of movement, the data can be used for judging the activity state of the restraint, whether the restraint is in a normal state or not can be known in real time by analyzing the activity posture data, abnormal behaviors such as struggling or escaping can be found in time, when the sensor detects that the restraint is frequently moved and the skin pressure is continuously changed, the central platform judges that the restraint is in the struggling state, the automatic judging mechanism reduces the dependence on manual monitoring, improves the monitoring efficiency and accuracy, the system automatically improves the restraint strength by one level so as to ensure that the restraint cannot be injured due to struggling, simultaneously informs the medical staff to check, prevents unexpected events, detects that the skin pressure value of the restraint strength becomes 0 when the system does not receive the restraint releasing command, and the central platform judges that the restraint can be escaped, at the moment, the system automatically sends out an alarm to inform staff to process, the automatic early warning mechanism can discover and deal with the escape behavior of the restraint person at the first time, the potential risk and potential safety hazard caused by escape are prevented, when the struggling state is detected, the central platform automatically increases the restraint intensity by one level to ensure the safety of the restraint person, meanwhile, the system can inform the medical staff to go to check and conduct necessary intervention, the automatic processing mode not only increases the response speed, but also can ensure to take measures at the first time, the injury risk caused by struggling of the restraint person is reduced, when the escape behavior of the restraint person is detected, the central platform automatically sends out an alarm to inform the medical staff to take action, the automatic alarm system can remind the medical staff at the first time to prevent the potential safety hazard caused by the escape behavior, through timely early warning, the escape behavior can be effectively reduced, and the safety and order of the medical environment are maintained.
S4, remotely connecting a medical staff with the central platform for constraint monitoring;
specifically, the medical staff remotely connected with the central platform for constraint monitoring means that the medical staff remotely connected with the central platform through the mobile terminal checks the constraint person and the constraint intensity, and receives the notification of the central platform through the mobile terminal, and the central platform automatically generates a constraint belt control record and sends the constraint belt control record to the mobile terminal of the medical staff after receiving the constraint belt voice control command of the medical staff.
The medical staff can be remotely connected to the central platform through the mobile terminal, the physiological information and the constraint intensity of the restraint can be checked in real time, the remote connection mode enables the medical staff not to be limited by a fixed monitoring place any time and any place, monitoring and management can be carried out, work flexibility and efficiency are improved, in addition, portability of the mobile terminal enables the medical staff to rapidly respond and process under emergency conditions, the medical staff can check the physiological information and the current constraint intensity of the restraint in real time through the mobile terminal, data such as heart rate, skin electric reaction, blood oxygen saturation and skin pressure can be checked in real time, real-time display of the data can help the medical staff to quickly know the state of the restraint, timely adjustment and intervention can be carried out, safety and comfort of the restraint are guaranteed, when the central platform detects abnormal conditions or needs intervention of the medical staff, the medical staff can timely receive the notifications and carry out corresponding processing, timeliness and accuracy of monitoring and management are improved, occurrence of sudden events can be effectively prevented through the real-time notification mechanism, and safety of the restraint is guaranteed.
S5, classifying and storing the biological information of the restraint person and the personal restraint intensity by the central platform;
Specifically, the central platform performs classified storage on the constraint biological information and the personal constraint intensity classification, namely, the central platform generates constraint classification information according to the collected constraint biological information, and stores the personal constraint intensity classification into the constraint classification as a preset scheme, and when the central platform detects the same constraint biological information again through a constraint belt, the central platform automatically invokes the stored constraint personal constraint intensity classification.
The central platform generates detailed classification information by analyzing the collected biological information of the constrainer, the classification information comprises physiological characteristics and health conditions of the constrainer, the system can be helped to identify and manage different constrainers more accurately, the generation of the classification information is helpful for personalized management, the adaptability and the precision of the system are improved, the constraint belt is ensured to be more scientific and reasonable to use, the central platform classifies and stores the individual constraint intensity according to the biological information of the constrainer and the generated classification information, the preset schemes can be quickly invoked when needed later, the time and the resource consumption of repeated calculation are reduced, the classification storage not only improves the efficiency of data management, but also ensures the accuracy and traceability of the data, and the subsequent analysis and optimization are facilitated.
Example 2
Referring to fig. 3, for a second embodiment of the present invention, which is different from the previous embodiment, there is provided a medical band intelligent management system, which includes,
The information collection module is used for collecting biological information of the restraint through the sensor and transmitting the biological information to the central platform;
The central processing module is used for receiving the biological information of the restraint person, preprocessing, defining personal restraint intensity classification according to the biological information of the restraint person, adjusting the restraint intensity in real time, and receiving the voice instruction control restraint belt of the medical staff;
the remote monitoring module is used for remotely connecting the mobile terminal of the medical staff and sending information to the mobile terminal for the medical staff to check;
The storage module is used for generating a constraint person classification according to the biological information of the constraint person, and storing the personal constraint intensity of the constraint person in the constraint person classification in a grading manner to be used as a preset scheme for calling.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (10)

1. An intelligent management method for medical restraint bands is characterized by comprising the following steps: comprising the steps of (a) a step of,
Installing a sensor on the restraint belt to collect biological information of the restraint person for preliminary restraint and transmitting the collected biological information to the central platform;
the central platform defines personal constraint intensity classification according to the biological information of the constrainer, and adjusts the constraint intensity in real time;
the central platform receives the voice command control restriction belts of medical staff and detects abnormal behaviors of the restrainers for automatic early warning;
medical staff remotely connect the central platform for constraint monitoring;
the central platform stores the restraint biological information and the personal restraint intensity in a classified mode.
2. The medical restraint strap intelligent management method of claim 1, wherein: the method is characterized in that a sensor is arranged on the restraint belt to collect biological information of the restraint person for preliminary restraint and transmit the collected biological information to the central platform, the biological information sensor is arranged on the restraint belt to collect biological information of the restraint person, including heart rate, skin electric reaction, blood oxygen saturation, skin pressure, body type and activity posture information, and the restraint person is preliminary restrained and fixed according to the collected biological information until the restraint belt is matched with the body type of the restraint person and skin pressure is generated, and the biological information sensor transmits the collected biological information of the restraint person to the central platform for pretreatment through a network.
3. The medical restraint strap intelligent management method of claim 2, wherein: the central platform defines personal constraint intensity classification according to the biological information of the constrainer, adjusts the constraint intensity in real time, namely, the central platform extracts a mean value, a standard deviation, a minimum value, a maximum value, a skewness and a kurtosis as characteristics according to the collected biological information of the constrainer, normalizes each characteristic to form a characteristic F n, analyzes the principal component of the normalized characteristic F n, comprises,
Calculating a covariance matrix:
where Σ is the covariance matrix, m is the feature total number, mu F is the feature mean, For the ith feature, T is a transpose operation;
decomposing the covariance matrix to obtain a matrix eigenvalue lambda and a matrix eigenvector v:
selecting matrix eigenvectors corresponding to the first k matrix eigenvalues, and constructing a matrix eigenvector matrix V:
Projecting the features into a matrix feature vector matrix V to obtain a dimension reduction feature vector Z:
The method comprises the steps of constructing a multi-layer perceptron model, wherein the multi-layer perceptron model comprises an input layer, a hidden layer and an output layer, the input layer is in a dimension-reducing feature vector, the output layer is in a biological information index BI, and the range of values is 0 to 1;
Performing multi-layer perceptron model iterative training by using a training set, performing model parameter optimization by using a loss function and an Adam optimizer, and inputting a dimension reduction feature vector Z i into the multi-layer perceptron model to obtain a biological information index BI of a constraint;
Taking skin pressure as a constraint intensity grading standard, wherein the difference value between each constraint intensity grade is the same, setting the maximum skin pressure of constraint band constraint as P max, the minimum skin pressure as P min and the grading number as b, and calculating the skin pressure value delta P of each constraint intensity grade of the person of the constraint person:
Defining fuzzy sets and rules according to the calculated biological information indexes BI of the constrainer, mapping the biological information indexes BI into the fuzzy sets, defining a fuzzy membership function, calculating membership x i of each fuzzy set, and calculating constraint intensity level S of the constrainer according to the membership of the fuzzy set:
calculating constraint intensity according to the constraint intensity level of the constraint:
Pe＝Pmin+(S-1)*ΔP；
Where P e is the skin pressure value of the constraint intensity.
4. The medical restraint strap intelligent management method of claim 3, wherein: the central platform receives the voice command of medical staff to control the restriction band, the central platform collects the voice of the medical staff through the microphone array to carry out denoising and filtering treatment, a convolutional neural network model is used for training the voice of the medical staff, a unique identification signal is generated according to the voice of the medical staff, when the microphone array collects the voice, the voice identification signal is firstly generated through the convolutional neural network, when the identification signals are consistent, the central platform starts to carry out voice analysis, converts the voice into a text command to carry out restriction band control, and when the identification signals are inconsistent, voice analysis is refused;
The band control commands include constraint, tighten, loosen, and un-constraint, wherein,
The restraint means that the restraint belt automatically restrains the restraint person preliminarily until skin pressure is generated;
tightening the finger strap increases the restraint intensity of the restraint by one level, and the tightening command is not effective when the skin pressure value of the restraint intensity of the restraint reaches the maximum skin pressure;
Releasing the finger restraint strap reduces the restraint intensity of the restraint by one level, and the release command is not effective when the skin pressure value of the restraint intensity of the restraint reaches the minimum skin pressure;
the unconstrained means that the constraint belt releases the constraint state of the constraint taker.
5. The medical restraint strap intelligent management method of claim 4, wherein: the automatic early warning for detecting the abnormal behavior of the restraint means that the restraint belt automatically detects the moving gesture of the restraint by a sensor, when the restraint is detected to frequently move and the skin pressure is continuously changed, the restraint is judged to be in a struggling state, and the central platform automatically improves the restraint strength by one grade and informs a worker to check the restraint; when the constraint releasing command is not received, the constraint intensity skin pressure value is detected to be 0, the constraint person is judged to escape, and the central platform automatically sends out an alarm to inform the staff.
6. The medical restraint strap intelligent management method of claim 5, wherein: the medical staff remote connection central platform carries out constraint monitoring and indicates that the medical staff looks over the constraint person and constraint intensity through the mobile terminal remote connection central platform to receive the notice of central platform through mobile terminal, central platform automatically generates constraint belt control record and sends to medical staff's mobile terminal after receiving medical staff's constraint belt voice control command.
7. The medical restraint strap intelligent management method of claim 6, wherein: the central platform performs classified storage on the constraint biological information and the personal constraint intensity classification, namely the central platform generates constraint classification information according to the collected constraint biological information, and stores the personal constraint intensity classification into the constraint classification as a preset scheme, and when the central platform detects the same constraint biological information again through a constraint belt, the central platform automatically invokes the stored constraint personal constraint intensity classification.
8. A medical restraint strap intelligent management system according to any one of claims 1-7, wherein: comprising the steps of (a) a step of,
The information collection module is used for collecting biological information of the restraint through the sensor and transmitting the biological information to the central platform;
The central processing module is used for receiving the biological information of the restraint person, preprocessing, defining personal restraint intensity classification according to the biological information of the restraint person, adjusting the restraint intensity in real time, and receiving the voice instruction control restraint belt of the medical staff;
the remote monitoring module is used for remotely connecting the mobile terminal of the medical staff and sending information to the mobile terminal for the medical staff to check;
The storage module is used for generating a constraint person classification according to the biological information of the constraint person, and storing the personal constraint intensity of the constraint person in the constraint person classification in a grading manner to be used as a preset scheme for calling.
9. A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the medical restraint strap intelligent management method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the medical restraint strap intelligent management method of any one of claims 1 to 7.
CN202411094784.9A 2024-08-10 A medical restraint belt intelligent management method and system Active CN119015036B (en)

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