CN117064350A - Safety detection and intelligent evaluation system - Google Patents
Safety detection and intelligent evaluation system Download PDFInfo
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Abstract
The invention relates to the technical field of medical equipment, and provides a safety detection and intelligent evaluation system which comprises a server, a data acquisition module, a data analysis module, an early warning module, a risk prediction module and a physiological index monitoring module, wherein the data acquisition module acquires image data of patient activities, the physiological index monitoring module acquires physiological indexes of patients, the data analysis module comprehensively analyzes the physiological indexes acquired by the physiological index monitoring module to form analysis results, the risk prediction module performs risk prediction according to the image data of the patient activities acquired by the data acquisition module and the analysis results of the data analysis module, and the early warning module gives a warning to medical staff according to the prediction results of the risk prediction module. According to the invention, the risk prediction module and the early warning module are matched with each other, so that abnormal behaviors of a patient can be detected, and early warning prompts are actively sent to medical staff, so that the whole system has the advantages of rapid abnormal behavior early warning and strong nursing supervision capability.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to a safety detection and intelligent evaluation system.
Background
Patients with unconsciousness, delirium, restlessness, and passive self-injury often suffer from nerve, psychiatric wards, and intensive care units, and often suffer from agitation and agitation due to mental disorders, or from hurting themselves and others.
If CN112057257B prior art discloses an unusual nursing device of psychiatric diseases, at present, when nursing mental disorder to mental patient, generally tie patient on the sick bed to fix patient's four limbs and head, but current nursing device is when spacing fixed to the head, often need to move patient to the below that corresponds clamping mechanism on the sick bed slowly, then press from both sides tightly again, make the fixed inefficiency of patient's head, and the speed of moving after the nursing is accomplished is slow simultaneously, and when fixing of four limbs simultaneously, the fixed inefficiency appears simultaneously, the problem of speed is slow.
Another typical psychiatric nursing device disclosed in the prior art such as CN109077904B, at present, mental patients often have abnormal situations, which are easy to cause abnormal discharge of brain during sleep, cause various physical reactions, affect personal safety of oneself and others, and in the past, cannot well control sleep of mental patients, and can not well restrict sudden behaviors of mental patients, and the night time is long, if the mental patients cannot sleep normally, public safety of hospitals is affected, and a great deal of manpower is wasted for monitoring.
The invention is designed for solving the problems of insufficient recognition capability, low intelligent degree, lack of early warning of abnormal behaviors, high difficulty in nursing supervision and the like commonly existing in the field.
Disclosure of Invention
The invention aims to provide a security detection and intelligent assessment system for psychiatric department aiming at the defects existing at present.
In order to overcome the defects in the prior art, the invention adopts the following technical scheme:
the safety detection and intelligent evaluation system comprises a server, and further comprises a data acquisition module, a data analysis module, an early warning module, a risk prediction module and a physiological index monitoring module, wherein the server is respectively connected with the data acquisition module, the data analysis module, the early warning module, the risk prediction module and the physiological index monitoring module;
the data acquisition module acquires the image data of the patient movement, the physiological index monitoring module acquires the physiological index of the patient, the data analysis module performs comprehensive analysis according to the physiological index acquired by the physiological index monitoring module to form an analysis result, the risk prediction module performs risk prediction according to the image data of the patient movement acquired by the data acquisition module and the analysis result, and the early warning module sends a warning to medical staff according to the prediction result of the risk prediction module;
the data analysis module acquires the physiological index data of the patient acquired by the physiological index monitoring module, and calculates a Comprehensive evaluation index according to the following formula:
Comprehensive＝α 1 ·Move+α 2 ·HR+α 3 ·OX+α 4 ·RR；
wherein alpha is 1 、α 2 、α 3 、α 4 As the weight coefficient, the system adjusts according to the actual situation and the requirement, and Move isThe physiological index monitoring module acquires the movement data of the patient, HR is heart rate data of the patient acquired by the physiological index monitoring module, OX is blood oxygen saturation data of the patient acquired by the physiological index monitoring module, and RR is respiratory rate data of the patient acquired by the physiological index monitoring module;
the risk prediction module calls the data acquisition module to acquire the image data of the patient activity and the analysis result generated by the data analysis module, and triggers the prediction of the risk of the patient according to the analysis result.
Optionally, the data acquisition module comprises an acquisition unit and a support frame, the support frame supports the acquisition unit, the acquisition unit acquires image data of the patient in an active area, the acquisition unit comprises an acquisition probe and a data memory, the acquisition probe acquires the image data of the patient in the active area, and the data memory stores the image data of the patient acquired by the acquisition probe;
the collecting unit is arranged on the supporting frame, and the supporting frame is fixed in the movable area.
Optionally, the physiological index monitoring module includes a binding unit, a mobile monitoring unit, a blood oxygen monitoring unit, a heart rate monitoring unit and a respiration monitoring unit, the binding unit supports the mobile monitoring unit, the blood oxygen monitoring unit, the heart rate monitoring unit and the respiration monitoring unit and is bound on the patient, the mobile monitoring unit collects movement amount data of the patient, the respiration monitoring unit monitors respiratory rate data of the patient, the heart rate monitoring unit monitors heart rate data of the patient, and the blood oxygen monitoring unit monitors blood oxygen saturation data of the patient;
the mobile monitoring unit, the blood oxygen monitoring unit, the heart rate monitoring unit and the respiration monitoring unit transmit acquired data to the data analysis module in a wireless transmission mode.
Optionally, the risk prediction module includes a data calling unit and a risk assessment unit, the data calling unit calls the image data acquired by the data acquisition module and the analysis result of the data analysis module, and the risk assessment unit assesses the patient risk based on the data called by the data calling unit;
the calling unit comprises a calling requester and a timer, wherein the timer is used for timing time intervals of two adjacent calling requests, and the calling requester triggers the calling of the image data acquired by the data acquisition module and the analysis result of the data analysis module according to the time intervals of the timer.
Optionally, the early warning module includes early warning unit and interaction suggestion unit, early warning unit acquires the prediction result of risk prediction module, interaction suggestion unit is with the prediction result of early warning unit to the medical personnel suggestion, in order to suggestion medical personnel nurses the patient.
Optionally, the risk assessment unit acquires the image data acquired by the data acquisition module and the analysis result of the data analysis module, and calculates the risk index Worn of the patient according to the following formula:
Worn＝β·Comprehensive+γ·F(ImageData)；
wherein, comprehensive is a Comprehensive evaluation index, F (ImageData) is a function for processing and utilizing the output result of the patient detection algorithm, beta and gamma are weight coefficients for adjusting the relative importance of the Comprehensive evaluation index Comprehensive and the function F (ImageData) for processing and utilizing the output result of the patient detection algorithm in the risk index, and the two weight coefficients are adjusted according to the practical application environment and the target;
and if the risk index Worn is not in the set safety threshold range, triggering the early warning module to prompt the medical staff. Optionally, the function F (ImageData) of the output result of the patient detection algorithm is calculated according to the following formula:
where n is the total number of detected patients in the image, i is the ith detected patient, confidence_i is the confidence of the ith detected patient, the value is usually automatically obtained by the patient identification model, area_i is the area of the patient's bounding box, and the value is set by the system.
The beneficial effects obtained by the invention are as follows:
1. through the cooperation among the binding unit, the mobile monitoring unit, the blood oxygen clamping unit, the blood oxygen monitoring unit, the heart rate monitoring unit and the respiration monitoring unit, physiological data of a patient can be accurately detected, so that the identification capability of abnormal behaviors of the patient is improved, and the intelligent detection capability of the whole device is also improved;
2. the data acquisition module is used for acquiring images of the patient in the active area, so that the active range of the patient can be grasped dynamically, and the abnormal evaluation capability of the whole system to the patient is improved;
3. through the mutual coordination of the data calling unit and the risk assessment unit, the whole system can carry out risk assessment on abnormal behaviors of a patient according to the image data acquired by the data acquisition module and the analysis result of the data analysis module, so that the whole system has the advantages of accurate abnormal recognition capability, high intelligent degree and early warning on the abnormal behaviors;
4. through the cooperation of risk prediction module and early warning module for patient's unusual action can be detected, and initiatively send the early warning, with sending the suggestion to medical personnel, make entire system have unusual action early warning rapidly and nursing supervisory ability strong advantage.
Drawings
The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate like parts in the different views.
Fig. 1 is a schematic block diagram of the overall structure of the present invention.
Fig. 2 is a block diagram of a physiological index monitoring module and a data analysis module according to the present invention.
FIG. 3 is a block diagram of the calculation of the overall evaluation index according to the present invention.
Fig. 4 is a schematic structural view of the connecting seat and the binding band of the present invention.
Fig. 5 is a schematic view of an application scenario between the blood oxygen clamping unit and a patient of the present invention.
Fig. 6 is a schematic partial cross-sectional view of an oximetry unit according to the present invention.
Fig. 7 is a schematic structural view of a display screen and an indicator lamp according to the present invention.
Fig. 8 is a schematic side view of a hospital bed of the present invention.
Fig. 9 is a schematic top view of a hospital bed of the present invention.
Reference numerals illustrate: 1. binding a belt; 2. a fixing seat; 3. chest belt heart rate sensor; 4. a clamping seat; 5. a first emission light source; 6. a second emission light source; 7. a photodetector; 8. a patient; 9. an indicator light; 10. a display screen; 11. a pressure detection plate; 12. a side edge detection rod; 13. a sickbed.
Detailed Description
The following embodiments of the present invention are described in terms of specific examples, and those skilled in the art will appreciate the advantages and effects of the present invention from the disclosure herein. The invention is capable of other and different embodiments and its several details are capable of modification and variation in various respects, all without departing from the spirit of the present invention. The drawings of the present invention are merely schematic illustrations, and are not intended to be drawn to actual dimensions. The following embodiments will further illustrate the related art content of the present invention in detail, but the disclosure is not intended to limit the scope of the present invention.
Embodiment one: according to fig. 1, 2, 3, 4, 5, 6, 7, 8 and 9, the embodiment provides a security detection and intelligent evaluation system, which comprises a server, and the security detection and intelligent evaluation system further comprises a data acquisition module, a data analysis module, an early warning module, a risk prediction module and a physiological index monitoring module, wherein the server is respectively connected with the data acquisition module, the data analysis module, the early warning module, the risk prediction module and the physiological index monitoring module, and transmits intermediate data of the data acquisition module, the data analysis module, the early warning module, the risk prediction module and the physiological index monitoring module to a database of the server for storage;
the data acquisition module acquires image data of patient movement, the physiological index monitoring module acquires physiological indexes of the patient, the data analysis module comprehensively analyzes the physiological index acquired by the physiological index monitoring module to form an analysis result, the risk prediction module acquires the image data of patient movement and the analysis result to perform risk prediction, and the early warning module sends a warning to medical staff according to the prediction result of the risk prediction module;
the safety detection and intelligent evaluation system further comprises a central processor, wherein the central processor is respectively in control connection with the data acquisition module, the data analysis module, the early warning module, the risk prediction module and the physiological index monitoring module, and the data acquisition module, the data analysis module, the early warning module, the risk prediction module and the physiological index monitoring module are controlled in a centralized manner based on the central processor, so that the modules can be matched in a coordinated manner and run efficiently;
the data analysis module acquires the physiological index data of the patient acquired by the physiological index monitoring module, and calculates a Comprehensive evaluation index according to the following formula:
Comprehensive＝α 1 ·Move+α 2 ·HR+α 3 ·OX+α 4 ·RR；
wherein alpha is 1 、α 2 、α 3 、α 4 As weight coefficient, the system is adjusted according to actual situation and requirement, move is the movement data of the patient acquired by the physiological index monitoring module, HR is the heart rate data of the patient acquired by the physiological index monitoring module, and OX is the physiological index monitoringThe RR is the respiratory frequency data of the patient collected by the physiological index monitoring module;
the risk prediction module calls the data acquisition module to acquire the image data of the patient activity and the analysis result generated by the data analysis module, and triggers the prediction of the risk of the patient according to the analysis result;
optionally, the physiological index monitoring module includes a binding unit, a mobile monitoring unit, a blood oxygen clamping unit, a blood oxygen monitoring unit, a heart rate monitoring unit and a respiration monitoring unit, where the binding unit supports the mobile monitoring unit, the heart rate monitoring unit and the respiration monitoring unit and is bound to the patient 8, the blood oxygen clamping unit is used for supporting the blood oxygen monitoring unit to form a blood oxygen detection part, the blood oxygen detection part is clamped at the fingertip of the patient, the mobile monitoring unit is used for detecting the movement amount of the patient 8, the respiration monitoring unit monitors the respiration frequency data of the patient 8, the heart rate monitoring unit monitors the heart rate data of the patient 8, and the blood oxygen monitoring unit monitors the blood oxygen saturation data of the patient 8;
the blood oxygen monitoring unit, the heart rate monitoring unit and the respiration monitoring unit transmit acquired data to the data analysis module in a wireless transmission mode;
the binding unit comprises a binding belt 1, a connecting seat and a quick-release component, wherein the binding belt 1 is arranged on the connecting seat, the connecting seat is bound on the chest of a patient 8 and is clung to the skin of the chest, two clamping cavities are symmetrically arranged at the joint of the connecting seat and the binding belt 1, the quick-release component is arranged at the joint of the binding belt 1 and the connecting seat, and the binding belt 1 is inserted into the clamping cavities of the fixing seat 2 when fixing the connecting seat;
as shown in fig. 4, when the mobile monitoring unit, the heart rate monitoring unit and the respiration monitoring unit are all arranged on the connecting seat;
the mobile monitoring unit comprises an accelerometer for detecting the displacement of the patient 8 and a gyroscope for measuring the angular velocity of the patient 8;
wherein the accelerometer and the gyroscope are assembled on the connecting seat;
in addition, when the object moves linearly or receives gravity, the sensing element in the accelerometer can displace, and the magnitude of the acceleration can be obtained by measuring the displacement; if the initial velocity and displacement of the object are known, the velocity and displacement of the object can be calculated by integrating the acceleration;
meanwhile, when the object rotates, the rotating shaft in the gyroscope can keep a stable direction due to conservation of angular momentum, and the angular speed of the object can be obtained by measuring the deflection angle of the rotating shaft; if the initial angle of the object is known, the rotation angle of the object can be calculated by integrating the angular velocity;
the movement data Move of the patient acquired by the physiological index monitoring module is calculated according to the following formula:
wherein M is the real-time displacement acquired by the mobile monitoring unit, min_M is the minimum value of the movement amount of the patient history, and max_M is the maximum value of the movement amount of the patient history;
the respiratory monitoring unit comprises at least one abdominal cavity/abdomen motion sensor and an elastic belt, wherein the at least one abdominal cavity abdomen motion sensor is arranged on the elastic belt to form a detection part, and the detection part is nested on the binding belt 1;
wherein the elastic band is internally woven with a resistive material, and the resistance of the internal resistive material changes when the elastic band is elongated or compressed; this change in resistance can be detected by the circuit and converted into an electrical signal; by analysis of this electrical signal, the frequency and depth of respiration are also obtained;
the respiratory rate data RR of the patient collected by the physiological index monitoring module is calculated according to the following formula:
wherein R is i For the respiratory rate value detected by the respiratory monitoring unit, μ_rr is the mean value of the respiratory rate of the patient, σ_rr is the standard deviation of the respiratory rate of the patient;
the binding unit further comprises a first power supply battery, wherein the first power supply battery is used for electrically connecting the mobile monitoring unit, the respiration monitoring unit and the heart rate monitoring unit;
the heart rate monitoring unit comprises at least one chest belt heart rate sensor 3 and at least one fixing seat 2, wherein the fixing seat 2 is respectively supported with the at least one chest belt heart rate sensor 3 to form a sampling part, the sampling part is respectively arranged on the binding belt 1 and the connecting seat, and specifically, the chest of a patient 8 is opposite to a heart activity area;
the chest belt heart rate sensor 3 is fixed on the chest, and adopts an electronic signal detection technology similar to an Electrocardiogram (ECG), which is a technical means well known to those skilled in the art, so in this embodiment, a detailed description is omitted;
the heart rate data HR of the patient collected by the physiological index monitoring module is calculated according to the following formula:
wherein H is i For the respiratory rate values detected by the respiratory monitoring unit, μ_hr is the mean of the respiratory rate of the patient, σ_hr is the standard deviation of the respiratory rate of the patient;
the blood oxygen monitoring unit comprises a first emission light source 5, a second emission light source 6 and a light detector 7, wherein the first emission light source 5 is used for emitting 660 nanometers of red light, the second emission light source 6 emits 940 nanometers of infrared light, and the light detector 7 is arranged on the other side of the fingertip so as to receive the light transmitted by the second emission light source 6 of the first emission light source 5;
one of which emits 660 nm red light and the other emits 940 nm infrared light; the two lights are selected because there is a significant difference in absorption coefficients of Oxyhemoglobin (O2 Hb) and reduced hemoglobin (HHb) at these two wavelengths;
when blood pulsates, the volume change of the blood can cause the absorption amount of red light and infrared light to be different, so that a difference signal is generated;
the oxygen saturation of bleeding can be calculated by measuring the absorption ratio of red light and infrared light; because in blood, oxyhemoglobin absorbs little red light and absorbs more infrared light; the reduced hemoglobin has more absorption to red light and less absorption to infrared light; the blood oxygen saturation is the ratio of the amount of oxygenated hemoglobin to the total hemoglobin;
therefore, the calculation formula of the blood oxygen saturation is:
OX＝(O2Hb/(O2Hb+HHb))*100％
where OX is the blood oxygen saturation, O2Hb is the absorbance of oxygenated hemoglobin, and HHB is the absorbance of reduced hemoglobin;
the blood oxygen clamping unit comprises a clamping seat 4 and a second power supply battery, the clamping seat 4 is O-shaped, the first emission light source 5 and the second emission light source 6 are arranged on one side of the clamping seat 4, the light detector 7 is arranged on the other side of the clamping seat 4 and is opposite to the first emission light source 5 and the second emission light source 6 so as to form a nested cavity clamped on the fingertip of the patient 8, and the second power supply battery is used for supplying power to the first emission light source 5, the second emission light source 6 and the light detector 7;
in the use process, an operator nests the blood oxygen clamping unit on the fingertip of the patient 8, so that the monitoring of the blood oxygen of the patient 8 is realized;
through the coordination among the binding unit, the mobile monitoring unit, the blood oxygen clamping unit, the blood oxygen monitoring unit, the heart rate monitoring unit and the respiration monitoring unit, the physiological data of the patient 8 can be accurately detected, so that the identification capability of the abnormal behaviors of the patient 8 is improved, and the intelligent detection capability of the whole device is also improved;
optionally, the data acquisition module includes an acquisition unit and a support frame, the support frame supports the acquisition unit, the acquisition unit acquires image data of the patient 8 in an active area, the acquisition unit includes an acquisition probe and a data memory, the acquisition probe acquires the image data of the patient 8 in the active area, and the data memory stores the image data of the patient 8 acquired by the acquisition probe;
the collecting unit is arranged on the supporting frame, and the supporting frame is fixed in the movable area;
optionally, the risk prediction module includes a data calling unit and a risk assessment unit, the data calling unit calls the image data acquired by the data acquisition module and the analysis result of the data analysis module, and the risk assessment unit assesses the risk of the patient 8 based on the data called by the data calling unit;
the calling unit comprises a calling requester and a timer, wherein the timer is used for timing the time interval of two adjacent calling requests, and the calling requester triggers the calling of the image data acquired by the data acquisition module and the analysis result of the data analysis module according to the time interval of the timer;
in this embodiment, the time interval of the timer is set by an operator according to an actual situation, and after the time countdown is reached, the call requester is triggered to call the image data acquired by the data acquisition module and the analysis result of the data analysis module, where after the call requester sends a call request, the server responds to the call request, and then transmits the image data acquired by the data acquisition module and the analysis result of the data analysis module to the wind direction evaluation unit for evaluation;
through the mutual matching of the data calling unit and the risk assessment unit, the whole system can carry out risk assessment on the abnormal behavior of the patient 8 according to the image data acquired by the data acquisition module and the analysis result of the data analysis module, and has the advantages of accurate abnormal recognition capability, high intelligent degree and early warning on the abnormal behavior;
optionally, the risk assessment unit acquires the image data acquired by the data acquisition module and the analysis result of the data analysis module, and calculates the risk index Worn of the patient according to the following formula:
Worn＝β·Comprehensive+γ·F(ImageData)；
wherein, comprehensive is a Comprehensive evaluation index, F (ImageData) is a function for processing and utilizing the output result of the patient detection algorithm, beta and gamma are weight coefficients for adjusting the relative importance of the Comprehensive evaluation index Comprehensive and the function F (ImageData) for processing and utilizing the output result of the patient detection algorithm in the risk index, and the selection of the two weight coefficients can be adjusted according to the practical application environment and the target;
if the risk index Worn is not in the set safety threshold range, triggering the early warning module to prompt the medical staff;
if the risk index Worn is within the set safety threshold range, indicating that the patient is in an acceptable state, and continuing to monitor the patient;
the set safety threshold range is set by a system or a medical staff according to the actual situation of the monitoring environment, which is a technical means well known to those skilled in the art, and those skilled in the art can query the related technical manual to obtain the technology, so that the description is omitted in this embodiment;
optionally, the function F (ImageData) that processes and utilizes the output of the patient detection algorithm is calculated according to the following equation:
where n is the total number of detected patients in the image, i is the image tag of the ith detected patient, confidence_i is the confidence of the ith detected patient, the value is usually obtained automatically by the patient identification model, area_i is the area of the patient's bounding box, the value is set by the system, for example, the length and width of the identified bounding box set by the patient identification model can obtain the area of the patient's bounding box;
total_area is the total area of an image, and generally refers to the total number of pixels of the image, that is, the product of the width (width) and the height (height) of the image; specifically, if you have an image with a width of W pixels and a height of H pixels, then the total area of the image is w×h, because each pixel can be considered as part of the area of the image, so for the calculation formula of total_area, if we use W to represent the width of the image and H to represent the height of the image (these two values can typically be obtained directly from the metadata of the image), then there are: total_area=w×h;
the meaning of Σ (confidence_i) is understood to be the sum of the degree of confidence of the model for each object and the integrated evaluation value of the space occupied by the object in the image for all detected objects. The larger this value, the more likely the number of detected objects (patients) or the higher the model's certainty of the detection result;
in this embodiment, the patient detection algorithm may be performed by using YOLO or R-CNN, which is a technical means well known to those skilled in the art, and those skilled in the art may query the related technical manual to obtain the technology, so that the description is omitted in this embodiment;
the data acquisition module is used for acquiring the image of the patient in the active area, so that the active range of the patient can be grasped dynamically, and the evaluation capability of the whole system on the abnormality of the patient is improved;
meanwhile, in the present embodiment, there is provided a step of constructing a patient identification model:
s1, collecting a large amount of patient image data; such data may be obtained by capturing and saving images on a camera of the patient's active area;
s2, after the images are collected, labeling is needed; the labeling process includes drawing a bounding box for each patient in the image and assigning a category (in this scenario "patient"); this process can be done manually or using a semi-automated labeling tool;
s3, scaling, normalization and the like of the image so as to adapt to the input requirement of the model;
s4, selecting an existing object detection algorithm, such as YOLO, faster R-CNN and the like; the selection of the algorithms is mainly based on actual requirements, such as that YOLO is suitable for scenes with high requirements on real-time performance, and that fast R-CNN is suitable for scenes with high requirements on accuracy;
s5, evaluating: after training is completed, the performance of the model needs to be evaluated; this typically involves using a separate test set (data that the model did not see during the training process) to evaluate; the evaluation index may include accuracy, recall, mAP, etc.; optimizing: based on the evaluation result, we may need to optimize the model; this may include altering parameters of the model, using different optimization algorithms, or altering the structure of the model, etc.;
s6, deploying the trained model into an actual environment, analyzing the input image in real time, and identifying and positioning a patient;
the above is a step of constructing a patient identification model provided in the art, and a person skilled in the art may replace or substitute another method to implement a manner of identifying and positioning a patient, so that a detailed description is omitted in this embodiment;
optionally, the early warning module includes an early warning unit and an interaction prompting unit, the early warning unit obtains the prediction result of the risk prediction module, and the interaction prompting unit prompts the prediction result of the early warning unit to a medical staff so as to prompt the medical staff to care the patient;
the interaction prompt unit is arranged on a display device which can be easily touched or watched by the medical staff so as to obtain the real-time early warning state of the patient;
the interaction prompting unit comprises a display screen and an indicator lamp, wherein the display screen displays the prediction result of the risk prediction module, and the indicator lamp displays different middle colors according to the prediction result of the risk prediction module so as to prompt the medical staff;
through the cooperation of risk prediction module and early warning module for patient's unusual action can be detected, and initiatively send the early warning, with give the suggestion for medical personnel makes the overall system have unusual action early warning rapidly and nursing supervisory ability strong advantage.
Embodiment two: this embodiment should be understood to include all the features of any one of the foregoing embodiments, and further improve thereon, as shown in fig. 1, 2, 3, 4, 5, 6, 7, 8, and 9, further in that the data acquisition module further includes a bed sensing unit that senses a signal of the mental patient 8 on a hospital bed to acquire sleeping posture and sitting posture data (sitting on a bed) of the patient 8, and a sensing analysis unit that analyzes the patient 8 according to the bed sensing unit acquisition of sleeping posture and sitting posture data of the patient 8;
the bed induction unit comprises at least one pressure detection plate 11, a side edge detection rod 12 and a data collector, wherein the at least one pressure detection plate 11 is used for collecting pressure data of the patient 8 acting on the bed plate, the side edge detection rod 12 is arranged on the upper surface of the side edge of the bed plate and collecting pressure data of the patient 8 acting on the side edge of the bed plate, and the data collector is used for collecting data of the at least one pressure detection plate 11 and the side edge detection rod 12;
the induction analysis unit acquires the data acquired by the bed induction unit and calculates a pressure evaluation index Press according to the following formula:
wherein w is 1 、w 2 、w 3 、w 4 The weight coefficient is obtained by inputting normal_P_i which is pressure data detected on the ith pressure detection plate 11 and normalized by medical staff according to actual conditions and from a human-computer interaction interface, normal_S_j which is pressure data detected on the jth side edge detection rod 12 and normalized by medical staff, N which is the number of the pressure detection plates 11, M which is the number of the side edge detection rods 12 according to actual conditions of a sickbed, K which is an adjustment coefficient, the value of which is set by a system, normal_B which is the weight coefficient of the patient 8, a value obtained by normalizing the real-time weight of the patient 8 with the maximum value and the minimum value of the weight data of the patient, normal_T which is the soft and hard degree coefficient of a bed, normal_A which is the age coefficient of the patient 8, normal_D which is the disease state coefficient of the patient 8 according to the actual conditions of the patient 8, and the patient 8 which is monitored by medical staff;
for the normalized pressure data normalized_p_i detected on the i-th pressure detection plate 11, calculation is performed according to the following formula:
wherein P_i is real-time pressure data detected by the ith pressure detection plate 11, and min_P and max_P are the minimum and maximum values of the historical pressure data detected by all the pressure detection plates 11 respectively;
for normalized pressure data normalized_s_j detected on the jth side edge detector bar 12, the calculation is performed according to the following equation:
wherein S_j is real-time pressure data detected by the jth side edge detection rod 12, and min_S and max_S are the minimum value and the maximum value of the historical pressure data detected by all the side edge detection rods 12 respectively;
after the sensing analysis unit obtains a pressure evaluation index Press, comparing the pressure evaluation index Press with a set monitoring threshold, if the pressure evaluation index Press is smaller than the set monitoring threshold monitor, indicating that a patient is not in a bed, triggering prompt of the medical staff to monitor and manage mental patients with inconvenient actions;
if the pressure evaluation index Press is larger than the set monitoring threshold monitor, indicating that the patient is in the bed, and continuing to control the patient;
the set monitoring threshold monitor is set by a system or a medical staff according to the condition of the patient, which is a technical means well known to those skilled in the art, and those skilled in the art can query related technical manuals to obtain the technology, so that the details are not repeated in the embodiment;
through the cooperation of the bed sensing unit and the sensing analysis unit, the state of the patient on the bed can be collected, and the automatic identification and management of the state of the patient on the bed are promoted, so that the aim of accurately monitoring the patient is fulfilled.
The foregoing disclosure is only a preferred embodiment of the present invention and is not intended to limit the scope of the invention, so that all equivalent technical changes made by applying the description of the present invention and the accompanying drawings are included in the scope of the present invention, and in addition, elements in the present invention can be updated as the technology develops.
Claims (7)
1. The safety detection and intelligent evaluation system comprises a server, and is characterized by further comprising a data acquisition module, a data analysis module, an early warning module, a risk prediction module and a physiological index monitoring module, wherein the server is respectively connected with the data acquisition module, the data analysis module, the early warning module, the risk prediction module and the physiological index monitoring module;
the data acquisition module acquires the image data of the patient movement, the physiological index monitoring module acquires the physiological index of the patient, the data analysis module performs comprehensive analysis according to the physiological index acquired by the physiological index monitoring module to form an analysis result, the risk prediction module performs risk prediction according to the image data of the patient movement acquired by the data acquisition module and the analysis result of the data analysis module, and the early warning module sends a warning to a medical staff according to the prediction result of the risk prediction module;
the data analysis module acquires the physiological index data of the patient acquired by the physiological index monitoring module, and calculates a Comprehensive evaluation index according to the following formula:
Comprehensive＝α 1 ·Move+α 2 ·HR+α 3 ·OX+α 4 ·RR；
wherein alpha is 1 、α 2 、α 3 、α 4 As a weight coefficient, the system is adjusted according to actual conditions and requirements, move is the movement data of the patient acquired by the physiological index monitoring module, HR is the heart rate data of the patient acquired by the physiological index monitoring module, OX is the blood oxygen saturation data of the patient acquired by the physiological index monitoring module, and RR is the respiratory rate data of the patient acquired by the physiological index monitoring module;
the risk prediction module calls the data acquisition module to acquire the image data of the patient activity and the analysis result generated by the data analysis module, and triggers the prediction of the risk of the patient according to the analysis result.
2. The safety inspection and intelligent assessment system according to claim 1, wherein the data acquisition module comprises an acquisition unit and a support frame, the support frame supporting the acquisition unit, the acquisition unit acquiring image data of the patient in an active area, the acquisition unit comprising an acquisition probe and a data storage, the acquisition probe acquiring image data of the patient in the active area, the data storage storing the image data of the patient acquired by the acquisition probe;
the collecting unit is arranged on the supporting frame, and the supporting frame is fixed in the movable area.
3. The safety detection and intelligent assessment system according to claim 2, wherein the physiological index monitoring module comprises a binding unit, a mobile monitoring unit, a blood oxygen monitoring unit, a heart rate monitoring unit and a respiration monitoring unit, the binding unit supports the mobile monitoring unit, the blood oxygen monitoring unit, the heart rate monitoring unit and the respiration monitoring unit and binds the mobile monitoring unit on the patient, the mobile monitoring unit collects movement amount data of the patient, the respiration monitoring unit monitors respiratory rate data of the patient, the heart rate monitoring unit monitors heart rate data of the patient, and the blood oxygen monitoring unit monitors blood oxygen saturation data of the patient;
the mobile monitoring unit, the blood oxygen monitoring unit, the heart rate monitoring unit and the respiration monitoring unit transmit acquired data to the data analysis module in a wireless transmission mode.
4. The system according to claim 3, wherein the risk prediction module comprises a data calling unit and a risk assessment unit, the data calling unit calls the image data acquired by the data acquisition module and the analysis result of the data analysis module, and the risk assessment unit assesses the patient risk based on the data called by the data calling unit;
the calling unit comprises a calling requester and a timer, wherein the timer is used for timing time intervals of two adjacent calling requests, and the calling requester triggers the calling of the image data acquired by the data acquisition module and the analysis result of the data analysis module according to the time intervals of the timer.
5. The system of claim 4, wherein the pre-warning module comprises a pre-warning unit and an interactive prompt unit, the pre-warning unit obtains the prediction result of the risk prediction module, and the interactive prompt unit prompts the prediction result of the pre-warning unit to a medical staff to prompt the medical staff to care the patient.
6. The system according to claim 5, wherein the risk assessment unit obtains the image data collected by the data collection module and the analysis result of the data analysis module and calculates the risk index Worn of the patient according to the following formula:
Worn＝β·Comprehensive+γ·F(ImageData)；
wherein, comprehensive is a Comprehensive evaluation index, F (ImageData) is a function for processing and utilizing the output result of the patient detection algorithm, beta and gamma are weight coefficients for adjusting the relative importance of the Comprehensive evaluation index Comprehensive and the function F (ImageData) for processing and utilizing the output result of the patient detection algorithm in the risk index, and the two weight coefficients are adjusted according to the practical application environment and the target;
and if the risk index Worn is not in the set safety threshold range, triggering the early warning module to prompt the medical staff.
7. The system of claim 6, wherein the function F (ImageData) of the output result of the patient detection algorithm is calculated according to the following equation:
where n is the total number of detected patients in the image, i is the ith detected patient,
confidence_i is the confidence level of the ith detected patient, its value is typically automatically derived from the patient identification model, and area_i is the area of the patient's bounding box, its value is set by the system.
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