Fall early warning and anti-falling protection vest and detection method thereof

Abstract

The application discloses a fall early warning and anti-falling protection vest and a detection method thereof, relates to the technical field of protection vests, and provides the most suitable protection vest for personalized protection, realizes personalized protection and preventive measures, meanwhile, through means such as real-time monitoring, multi-dimensional evaluation, alarm prompt and preventive measures, the safety and comfort of patients are effectively improved, the burden of medical staff is reduced, the efficiency and quality of medical care are improved, the off-bed situation and posture change of the target medical patient are monitored in real time, the off-bed or unstable posture of the patient is found in time, and thus the risk of falling is reduced, the risk of falling is early warned, the medical staff can take corresponding preventive measures, for example, adjusting the posture of the patient, providing auxiliary equipment and the like, so that the risk of falling is effectively reduced, the safety of the patient is protected, and according to the off-bed danger level and the falling risk level obtained through analysis, an alarm prompt is timely sent to the medical staff.

Description

Technical Field

[0001] This invention relates to the field of protective vest technology, specifically to a fall warning and anti-fall protective vest and its testing method. Background Technology

[0002] Preventing falls has become a key focus in ward management. Despite the implementation of preventative measures such as education, guidance, and shadow care management, falls still occur. How to prevent falls, protect patient safety, promptly alert caregivers and medical staff, and reduce secondary injuries from falls requires our collective consideration. Therefore, a fall warning and anti-fall protective vest and its detection method are needed.

[0003] Existing technologies typically only assess a patient's ability to get out of bed and stand from a single dimension. Relying solely on visual observation or simple sensor data may result in incomplete and inaccurate assessments. Clearly, this detection method has at least the following problems: 1. It usually cannot provide personalized protective equipment based on the patient's specific physical indicators. This may lead to a mismatch between the protective equipment and the patient's physical characteristics, significantly reducing the protective effect. At the same time, it cannot monitor the patient's behavior and postural stability in real time, creating blind spots in monitoring. It is impossible to detect potential dangerous behaviors in a timely manner, and the inability to monitor the patient's behavior and postural changes in real time may prevent medical staff from promptly noticing when the patient gets out of bed or becomes unsteady, thus increasing the risk of falls.

[0004] 2. Existing technologies typically only provide emergency treatment after a fall has occurred, lacking effective preventative measures. Without advance warning systems, healthcare professionals cannot take targeted measures to prevent falls. The lack of accurate fall risk assessments and alerts means healthcare professionals may be unable to take timely and effective preventative measures, increasing the risk of patient falls and impacting patient safety and the quality of care. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a fall warning and anti-fall protective vest and its testing method.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In its first aspect, the present invention provides a method for detecting a fall warning and anti-fall protective vest, comprising:

[0007] Step 1: Finding the most suitable protective vest: Obtain the target medical patient's body measurements, including chest circumference, waist circumference, and shoulder width. Then, analyze the most suitable protective vest for the target medical patient, provide the most suitable protective vest for the target medical patient, and ensure that the vest is worn correctly.

[0008] Step 2: Acquisition of bed exit data: After the target medical patient puts on the protective vest, the system monitors when the target medical patient leaves the bed. Several data collection points are set at each time point when the target medical patient leaves the bed, and the corresponding bed exit data is collected at each time point. The bed exit data includes the distance from the bed, the duration of bed exit, the distance between each danger zone and the duration of stay. The risk assessment coefficient of bed exit for the target medical patient at each data collection time point is obtained by analysis.

[0009] Step 3: Analysis of Bed Exit Risk Level: Based on the bed exit risk assessment coefficient corresponding to the target medical patients at each collection time point, the bed exit risk level corresponding to the target medical patients at each collection time point is analyzed, and alarm prompts are issued for the target medical patients at each collection time point according to the corresponding bed exit risk level.

[0010] Step 4: Acquisition of stability and symmetry data: Acquire stability and symmetry data for the target medical patient at each collection time point. Stability data includes body acceleration, body tilt angle, and body center of gravity height. Symmetry data includes pressure balance on each side and positional offset at each location. Analyze these data to obtain the stability and symmetry evaluation coefficients for the target medical patient at each collection time point.

[0011] Step 5: Obtaining the comprehensive standing assessment coefficient: Based on the stability assessment coefficient and symmetry assessment coefficient of the target medical patient at each collection time point, the comprehensive standing assessment coefficient of the target medical patient at each collection time point is obtained.

[0012] Step Six: Analysis of Fall Risk Levels: Based on the comprehensive standing assessment coefficients of the target medical patients at each data collection time point, the fall risk levels of the target medical patients at each data collection time point are analyzed, and alarm prompts are issued to the target medical patients at each data collection time point according to their corresponding fall risk levels.

[0013] Preferably, the analysis of the most suitable protective vest for the target medical patient is carried out as follows: the chest circumference, waist circumference, and shoulder width of the target medical patient are compared with the corresponding chest circumference range, waist circumference range, and shoulder width range of each protective vest in the database. If the chest circumference, waist circumference, and shoulder width of the target medical patient are located within the corresponding chest circumference range, waist circumference range, and shoulder width range of a certain protective vest in the database, then the protective vest in the database is taken as the most suitable protective vest for the target medical patient.

[0014] Preferably, the analysis obtains the risk assessment coefficient for leaving the bed for the target medical patient at each collection time point. Specifically, the analysis yields: the distance from the bed, the duration of time away from the bed, the distance between each risk area, and the duration of stay for the target medical patient at each collection time point are denoted as Q.g W g Y gi and P gi Where g represents the number corresponding to each collection time point, g = 1, 2, ..., n, i represents the number corresponding to each danger zone, i = 1, 2, ..., u, n is any integer greater than 2, u is any integer greater than 2, and so on, into the calculation formula.

[0015] In the process, the risk assessment coefficient κ for leaving the bed corresponding to the target medical patient at the g-th collection time point is obtained. g Where Q′, W′, Y′, and P′ represent the standard distance from the bed, standard time spent away from the bed, standard distance from the danger zone, and standard stay duration for medical patients, respectively; and ι1, ι2, ι3, and ι4 represent the weighting factors for the distance from the bed, the time spent away from the bed, the distance from the danger zone, and the stay duration for medical patients, respectively.

[0016] Preferably, the analysis of the risk level of leaving the bed for the target medical patient at each collection time point is carried out as follows: the risk assessment coefficient of leaving the bed for the target medical patient at each collection time point is compared with the risk assessment coefficient range of leaving the bed for each risk level in the database. If the risk assessment coefficient of leaving the bed for the target medical patient at a certain collection time point is within the risk assessment coefficient range of leaving the bed for a certain risk level in the database, then the risk level of leaving the bed in the database is taken as the risk level of leaving the bed for the target medical patient at that collection time point. In this way, the risk level of leaving the bed for the target medical patient at each collection time point is analyzed.

[0017] Preferably, the analysis obtains the stability evaluation coefficients corresponding to the target medical patient at each collection time point. The specific analysis process is as follows: the body acceleration value, body tilt angle value, and body center of gravity height value corresponding to the target medical patient at each collection time point are respectively denoted as Z. g X g and C g Where g represents the number corresponding to each collection time point, g = 1, 2, ..., n, where n is any integer greater than 2. Substitute into the calculation formula. In the process, the stability evaluation coefficient λ corresponding to the target medical patient at the g-th collection time point is obtained. g Where Z′, X′, and C′ are the set standard body acceleration value, standard body tilt angle value, and standard body center of gravity height value corresponding to the medical patient, respectively, and υ1, υ2, and υ3 are the set weight factors corresponding to the medical patient's body acceleration value, body tilt angle value, and body center of gravity height value, respectively.

[0018] Preferably, the analysis obtains the symmetry evaluation coefficients corresponding to the target medical patients at each acquisition time point. The specific analysis process is as follows: the pressure balance corresponding to each side and the positional offset corresponding to each part of the target medical patient at each acquisition time point are respectively denoted as V. gh and F ghs Where h represents the number corresponding to each side, h = 1, 2, ..., t, s represents the number corresponding to each part, s = 1, 2, ..., d, t is any integer greater than 2, d is any integer greater than 2, and so on. Substitute these values ​​into the calculation formula. In the process, the stability evaluation coefficient γ corresponding to the target medical patient at the g-th collection time point is obtained. g Where V′ and F′ are the standard pressure balance corresponding to the side of the medical patient and the standard position offset corresponding to the location, respectively, and ψ1 and ψ2 are the weighting factors corresponding to the pressure balance of the side of the medical patient and the weighting factors corresponding to the position offset, respectively.

[0019] Preferably, the analysis obtains the comprehensive standing assessment coefficients corresponding to the target medical patients at each collection time point. The specific analysis process is as follows: Substitute the stability assessment coefficients and symmetry assessment coefficients corresponding to the target medical patients at each collection time point into the calculation formula. In the process, the comprehensive standing assessment coefficient φ corresponding to the target medical patient at the g-th collection time point is obtained. g , where θ1 and θ2 are the weighting factors corresponding to the stability evaluation coefficient and the symmetry evaluation coefficient of the set medical patients, respectively, and e represents the natural constant.

[0020] Preferably, the analysis of the fall risk level corresponding to the target medical patient at each collection time point is carried out as follows: The comprehensive standing assessment coefficient corresponding to the target medical patient at each collection time point is compared with the comprehensive standing assessment coefficient range corresponding to each fall risk level in the database. If the comprehensive standing assessment coefficient corresponding to the target medical patient at a certain collection time point is within the comprehensive standing assessment coefficient range corresponding to a certain fall risk level in the database, then the fall risk level in the database is taken as the fall risk level corresponding to the target medical patient at that collection time point. In this way, the fall risk level corresponding to the target medical patient at each collection time point is analyzed.

[0021] In a second aspect, the present invention provides a fall warning and anti-fall protective vest, comprising a vest body, four symmetrical pull tabs arranged on both sides of the front of the vest body, an alarm device arranged on one side of the front of the vest body, a zipper arranged at the junction of the front and back of the vest body, a pull tab arranged on the upper side of the back of the vest body, and a sensing cloth block arranged at the center of the back of the vest body, with two symmetrical bed-leaving sensors and two symmetrical standing sensors arranged inside the sensing cloth block.

[0022] The beneficial effects of this invention are as follows: 1. This invention provides a fall warning and anti-fall protective vest and its detection method. Through personalized protection, it provides the most suitable protective vest to achieve personalized protection and prevention measures. At the same time, through real-time monitoring, multi-dimensional assessment, alarm prompts and prevention measures, it effectively improves the safety and comfort of patients, reduces the burden on medical staff, and improves the efficiency and quality of medical care.

[0023] 2. In this embodiment of the invention, the patient's status and posture changes are monitored in real time, and the patient's status of getting out of bed or unstable posture is detected in time, thereby reducing the risk of falls. By giving early warning of the risk of falls, medical staff can take corresponding preventive measures, such as adjusting the patient's posture and providing assistive devices, thereby effectively reducing the risk of falls and protecting the patient's safety.

[0024] 3. In this embodiment of the invention, by collecting various information such as bed-standing data, stability data, and symmetry data, the patient's standing status and fall risk are comprehensively assessed, improving the comprehensiveness and accuracy of the assessment. Based on the analyzed bed-standing risk level and fall risk level, timely alerts are issued to medical staff, enabling them to take appropriate measures to prevent patient falls and reduce the occurrence of accidents.

[0025] 4. In this embodiment of the invention, by acquiring and analyzing the physical indicators of the target medical patient, a personalized protective vest is provided to best adapt to the patient's physical characteristics and provide more effective protection. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating the implementation steps of the method of the present invention.

[0028] Figure 2 This is a schematic diagram of the front of the protective vest of the present invention.

[0029] Figure 3 This is a schematic diagram of the back of the protective vest of the present invention.

[0030] Figure 2 and Figure 3 The components include: 1. Protective vest body; 2. Alarm; 3. Handle strip; 4. Zipper; 5. Sensor cloth; 6. Bed exit sensor; 7. Standing sensor. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Examples of embodiments of the present invention Figure 1 As shown, a method for detecting a fall-prevention and anti-fall protective vest includes:

[0033] Step 1: Finding the most suitable protective vest: Obtain the target medical patient's body measurements, including chest circumference, waist circumference, and shoulder width. Then, analyze the most suitable protective vest for the target medical patient, provide the most suitable protective vest for the target medical patient, and ensure that the vest is worn correctly.

[0034] It should be noted that during medical examinations or physical examinations, medical personnel will measure the patient's body measurements, including chest circumference, waist circumference, and shoulder width, in order to obtain the target medical patient's chest circumference, waist circumference, and shoulder width.

[0035] In a specific embodiment, the analysis of the most suitable protective vest for the target medical patient is carried out as follows: the chest circumference, waist circumference, and shoulder width of the target medical patient are compared with the corresponding chest circumference range, waist circumference range, and shoulder width range of each protective vest in the database. If the chest circumference, waist circumference, and shoulder width of the target medical patient are located within the corresponding chest circumference range, waist circumference range, and shoulder width range of a certain protective vest in the database, then the protective vest in the database is taken as the most suitable protective vest for the target medical patient.

[0036] In this embodiment of the invention, by acquiring and analyzing the physical indicators of the target medical patient, a personalized protective vest is provided to best adapt to the patient's physical characteristics and provide more effective protection.

[0037] Step 2: Acquisition of bed exit data: After the target medical patient puts on the protective vest, the system monitors when the target medical patient leaves the bed. Several data collection points are set at each time point when the target medical patient leaves the bed, and the corresponding bed exit data is collected at each time point. The bed exit data includes the distance from the bed, the duration of bed exit, the distance between each danger zone and the duration of stay. The risk assessment coefficient of bed exit for the target medical patient at each data collection time point is obtained by analysis.

[0038] It should be noted that the bed-leaning sensors installed on the protective vest collect data on the target medical patient's distance from and duration of bed-leaning. These sensors also monitor the patient's time spent and distance from others within hazardous areas. Based on the monitored data, the distance and duration of stay corresponding to each hazardous area are recorded.

[0039] In a specific embodiment, the analysis yields the risk assessment coefficient for the target medical patient at each collection time point. Specifically, the analysis identifies the following: the distance from the bed, the duration of time away from the bed, the distance between each risk area, and the duration of stay for the target medical patient at each collection time point, denoted as Q. g W g Y gi and P gi Where g represents the number corresponding to each collection time point, g = 1, 2, ..., n, i represents the number corresponding to each danger zone, i = 1, 2, ..., u, n is any integer greater than 2, u is any integer greater than 2, and so on, into the calculation formula.

[0040] In the process, the risk assessment coefficient κ for leaving the bed corresponding to the target medical patient at the g-th collection time point is obtained. g Where Q′, W′, Y′, and P′ represent the standard distance from the bed, standard time spent away from the bed, standard distance from the danger zone, and standard stay duration for medical patients, respectively; and ι1, ι2, ι3, and ι4 represent the weighting factors for the distance from the bed, the time spent away from the bed, the distance from the danger zone, and the stay duration for medical patients, respectively.

[0041] It should be noted that ι1, ι2, ι3, and ι4 are all greater than 0 and less than 1.

[0042] It should also be noted that the standard distance from the bed, standard time spent away from the bed, standard distance from the patient in hazardous areas, and standard stay duration are determined based on the actual situation of the medical institution, professional knowledge, and data analysis. For example, for patients with milder conditions, the standard distance from the bed and standard time spent away from the bed can be relatively larger, while for patients with more severe conditions or requiring special monitoring, the standard distance from the bed and standard time spent away from the bed can be relatively smaller. For hazardous areas, the standard distance from the patient in hazardous areas and the standard stay duration can be determined based on the specific facilities and environmental conditions.

[0043] Step 3: Analysis of Bed Exit Risk Level: Based on the bed exit risk assessment coefficient corresponding to the target medical patients at each collection time point, the bed exit risk level corresponding to the target medical patients at each collection time point is analyzed, and alarm prompts are issued for the target medical patients at each collection time point according to the corresponding bed exit risk level.

[0044] In a specific embodiment, the analysis of the risk level of leaving the bed for the target medical patient at each collection time point is carried out as follows: the risk assessment coefficient of leaving the bed for the target medical patient at each collection time point is compared with the risk assessment coefficient range of leaving the bed for each risk level in the database. If the risk assessment coefficient of leaving the bed for the target medical patient at a certain collection time point is within the risk assessment coefficient range of leaving the bed for a certain risk level in the database, then the risk level of leaving the bed in the database is taken as the risk level of leaving the bed for the target medical patient at that collection time point. In this way, the risk level of leaving the bed for the target medical patient at each collection time point is analyzed.

[0045] It should be noted that the danger levels for leaving the bed include Level 1 alarm, Level 2 alarm, and Level 3 alarm.

[0046] It should also be noted that when a patient leaves the bed, a yellow light and a level one alarm are activated (with a voice reminder to the patient to return to bed). After 30 seconds, the family member receives a level two alarm via their wristband indicating that the patient has left the bed. If the family member continues to fail to address the issue, after one minute, the nurses' station receives a level three alarm reminding them that the patient needs attention.

[0047] This invention provides real-time monitoring of the patient's status and posture changes, enabling timely detection of patients getting out of bed or experiencing postural instability, thereby reducing the risk of falls. By providing early warnings of fall risks, medical staff can take corresponding preventative measures, such as adjusting the patient's posture or providing assistive devices, thus effectively reducing the risk of falls and protecting the patient's safety.

[0048] Step 4: Acquisition of stability and symmetry data: Acquire stability and symmetry data for the target medical patient at each collection time point. Stability data includes body acceleration, body tilt angle, and body center of gravity height. Symmetry data includes pressure balance on each side and positional offset at each location. Analyze these data to obtain the stability and symmetry evaluation coefficients for the target medical patient at each collection time point.

[0049] It should be noted that the standing sensor installed inside the safety vest obtains the patient's body acceleration value, body tilt angle value, and body center of gravity height value through the standing sensor device.

[0050] It should also be noted that a standing sensor is installed inside the safety vest to obtain the pressure balance of each side and the positional offset of each part.

[0051] In a specific embodiment, the analysis yields the stability evaluation coefficients corresponding to the target medical patient at each data collection time point. The specific analysis process is as follows: The body acceleration value, body tilt angle value, and body center of gravity height value corresponding to the target medical patient at each data collection time point are respectively denoted as Z. g X g and C g Where g represents the number corresponding to each collection time point, g = 1, 2, ..., n, where n is any integer greater than 2. Substitute into the calculation formula. In the process, the stability evaluation coefficient λ corresponding to the target medical patient at the g-th collection time point is obtained. g Where Z′, X′, and C′ are the set standard body acceleration value, standard body tilt angle value, and standard body center of gravity height value corresponding to the medical patient, respectively, and υ1, υ2, and υ3 are the set weight factors corresponding to the medical patient's body acceleration value, body tilt angle value, and body center of gravity height value, respectively.

[0052] It should be noted that υ1, υ2, and υ3 are all greater than 0 and less than 1.

[0053] It should also be noted that, based on the patient's physical condition and stability requirements, standard body acceleration values, standard body tilt angle values, and standard body center of gravity height values ​​are determined. These standard values ​​can be determined based on relevant research, expert opinions, or practical experience. Weighting factors for body acceleration values, body tilt angle values, and body center of gravity height values ​​are determined according to the degree of influence of each parameter on stability assessment. These weighting factors can be set based on actual needs and experience; for example, if the body tilt angle has a greater impact on stability assessment, a larger weighting factor can be assigned.

[0054] In another specific embodiment, the analysis obtains the symmetry evaluation coefficients corresponding to the target medical patients at each acquisition time point. The specific analysis process is as follows: the pressure balance corresponding to each side and the positional offset corresponding to each part of the target medical patient at each acquisition time point are respectively denoted as V. gh and F ghs Where h represents the number corresponding to each side, h = 1, 2, ..., t, s represents the number corresponding to each part, s = 1, 2, ..., d, t is any integer greater than 2, d is any integer greater than 2, and so on. Substitute these values ​​into the calculation formula. In the process, the stability evaluation coefficient γ corresponding to the target medical patient at the g-th collection time point is obtained. g Where V′ and F′ are the standard pressure balance corresponding to the side of the medical patient and the standard position offset corresponding to the location, respectively, and ψ1 and ψ2 are the weighting factors corresponding to the pressure balance of the side of the medical patient and the weighting factors corresponding to the position offset, respectively.

[0055] It should be noted that standard pressure balance and positional offset are determined based on the patient's physical condition and stability requirements. Pressure balance is determined by measuring the pressure distribution on each side, and positional offset is determined by measuring the deviation of each part of the body from a reference position. These standard values ​​can be determined based on relevant research, expert opinions, or practical experience. Weighting factors for pressure balance and positional offset are determined according to the degree of influence of each parameter on stability assessment. These weighting factors are set based on actual needs and experience; for example, if pressure balance has a greater impact on stability assessment, a larger weighting factor can be assigned.

[0056] Step 5: Obtaining the comprehensive standing assessment coefficient: Based on the stability assessment coefficient and symmetry assessment coefficient of the target medical patient at each collection time point, the comprehensive standing assessment coefficient of the target medical patient at each collection time point is obtained.

[0057] In a specific embodiment, the analysis yields the comprehensive standing assessment coefficients for the target medical patients at each collection time point. The specific analysis process is as follows: The stability assessment coefficients and symmetry assessment coefficients for the target medical patients at each collection time point are substituted into the calculation formula. In the process, the comprehensive standing assessment coefficient φ corresponding to the target medical patient at the g-th collection time point is obtained. g , where θ1 and θ2 are the weighting factors corresponding to the stability evaluation coefficient and the symmetry evaluation coefficient of the set medical patients, respectively, and e represents the natural constant.

[0058] It should be noted that the weighting factors for the stability and symmetry evaluation coefficients are determined based on their relative importance to the standing assessment. These weighting factors can be set according to actual needs and experience; for example, if stability has a greater impact on the standing assessment, a larger weighting factor can be assigned.

[0059] Step Six: Analysis of Fall Risk Levels: Based on the comprehensive standing assessment coefficients of the target medical patients at each data collection time point, the fall risk levels of the target medical patients at each data collection time point are analyzed, and alarm prompts are issued to the target medical patients at each data collection time point according to their corresponding fall risk levels.

[0060] In a specific embodiment, the analysis of the fall risk level corresponding to the target medical patient at each collection time point is carried out as follows: The comprehensive standing assessment coefficient corresponding to the target medical patient at each collection time point is compared with the comprehensive standing assessment coefficient range corresponding to each fall risk level in the database. If the comprehensive standing assessment coefficient corresponding to the target medical patient at a certain collection time point is within the comprehensive standing assessment coefficient range corresponding to a certain fall risk level in the database, then the fall risk level in the database is taken as the fall risk level corresponding to the target medical patient at that collection time point. In this way, the fall risk level corresponding to the target medical patient at each collection time point is analyzed.

[0061] In this embodiment of the invention, by collecting various information such as bed-leaning data, stability data, and symmetry data, the patient's standing status and fall risk are comprehensively assessed, improving the comprehensiveness and accuracy of the assessment. Based on the analyzed bed-leaning risk level and fall risk level, timely alerts are issued to medical staff, enabling them to take appropriate measures to prevent patient falls and reduce the occurrence of accidents.

[0062] Examples of embodiments of the present invention Figure 2 and Figure 3 As shown, a fall warning and anti-fall protective vest includes a vest body 1. Four handle strips 3 are symmetrically arranged on both sides of the front of the vest body 1. An alarm 2 is arranged on one side of the front of the vest body 1. A zipper 4 is arranged at the junction of the front and back of the vest body 1. A handle strip 3 is arranged on the upper side of the back of the vest body 1. A sensing cloth 5 is arranged at the center of the back of the vest body 1. Two symmetrical bed-leaving sensors 6 and two symmetrical standing sensors 7 are arranged inside the sensing cloth 5.

[0063] It should be noted that the protective vest is made of ACF biomimetic cartilage material, which can absorb 90% of the impact energy. The vest protects areas such as the spinal cord, shoulders, ribs, chest and abdomen, waist, and hip joints, which are prone to injury from falls. The protective vest is equipped with an airbag.

[0064] This invention provides a fall warning and anti-fall protective vest and its detection method. By providing personalized protection and the most suitable protective vest, personalized protection and prevention measures can be achieved. At the same time, through real-time monitoring, multi-dimensional assessment, alarm prompts and preventive measures, the safety and comfort of patients can be effectively improved, the burden on medical staff can be reduced, and the efficiency and quality of medical care can be improved.

[0065] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for detecting a fall-prevention and anti-fall protective vest, characterized in that, include: Step 1: The most suitable protective vest to wear: Obtain the body indicators of the target medical patient, including chest circumference, waist circumference and shoulder width, and then analyze the most suitable protective vest for the target medical patient, provide the most suitable protective vest for the target medical patient, and ensure that the vest is worn correctly. Step 2: Acquisition of bed exit data: After the target medical patient puts on the protective vest, the system monitors when the target medical patient leaves the bed. Several data collection points are set at the time when the target medical patient leaves the bed, and the bed exit data corresponding to the target medical patient is collected at each data collection point. The bed exit data includes the distance from the bed, the duration of the bed exit, the distance and duration of stay in each danger zone, and the analysis yields the bed exit risk assessment coefficient corresponding to the target medical patient at each data collection point. Step 3: Analysis of the risk level of leaving the bed: Based on the risk assessment coefficient of leaving the bed for the target medical patients at each collection time point, the risk level of leaving the bed for the target medical patients at each collection time point is analyzed, and alarm prompts are issued for the target medical patients at each collection time point according to the corresponding risk level of leaving the bed. Step 4: Acquisition of stability and symmetry data: Acquire stability and symmetry data for the target medical patient at each collection time point. Stability data includes body acceleration value, body tilt angle value, and body center of gravity height value. Symmetry data includes pressure balance corresponding to each side and positional offset corresponding to each part. Analyze to obtain the stability assessment coefficient and symmetry assessment coefficient for the target medical patient at each collection time point. The analysis yielded stability assessment coefficients for the target medical patients at each data collection time point. The specific analysis process is as follows: The body acceleration value, body tilt angle value, and body center of gravity height value of the target medical patient at each data collection time point are respectively recorded as follows: , and ,in, This indicates the number corresponding to each data collection time point. Let n be any integer greater than 2, and substitute it into the calculation formula. In the middle, we obtained the first Stability assessment coefficients for target medical patients at each data collection time point ,in, , , These are the standard body acceleration value, standard body tilt angle value, and standard body center of gravity height value corresponding to the set medical patients. , , These are the weighting factors corresponding to the set medical patient's body acceleration value, body tilt angle value, and body center of gravity height value, respectively. The analysis yielded symmetry evaluation coefficients for the target medical patients at each data collection time point. The specific analysis process is as follows: The pressure balance corresponding to each side and the positional offset corresponding to each part of the target medical patient at each data collection time point are respectively denoted as follows: and ,in, This indicates the number corresponding to each side. , This indicates the corresponding number for each part. Let t be any integer greater than 2 and d be any integer greater than 2. Substitute them into the calculation formula. In the middle, we obtained the first Stability assessment coefficients for target medical patients at each data collection time point ,in, , These are the standard pressure balance corresponding to the side of the medical patient and the standard position offset corresponding to the location, respectively. , These are the weighting factors corresponding to the lateral pressure balance of the medical patient and the weighting factors corresponding to the location offset, respectively. Step 5: Obtaining the comprehensive standing assessment coefficient: Based on the stability assessment coefficient and symmetry assessment coefficient of the target medical patient at each collection time point, the comprehensive standing assessment coefficient of the target medical patient at each collection time point is obtained by analysis. The analysis yielded the comprehensive standing assessment coefficients for the target medical patients at each data collection time point. The specific analysis process is as follows: The stability assessment coefficient and symmetry assessment coefficient corresponding to the target medical patients at each collection time point were substituted into the calculation formula. In the middle, we obtained the first The comprehensive standing assessment coefficient of the target medical patient at each data collection time point ,in, , These are the weighting factors for the stability assessment coefficient and the symmetry assessment coefficient corresponding to the set medical patients, respectively, where e represents the natural constant; Step Six: Analysis of Fall Risk Levels: Based on the comprehensive standing assessment coefficients of the target medical patients at each data collection time point, the fall risk levels of the target medical patients at each data collection time point are analyzed, and alarm prompts are issued to the target medical patients at each data collection time point according to their corresponding fall risk levels.

2. The method for detecting a fall warning and anti-fall protective vest as described in claim 1, characterized in that, The analysis of the most suitable protective vest for the target medical patient is as follows: The chest circumference, waist circumference, and shoulder width of the target medical patient are compared with the corresponding chest circumference, waist circumference, and shoulder width ranges of each protective vest in the database. If the chest circumference, waist circumference, and shoulder width of the target medical patient are within the corresponding chest circumference, waist circumference, and shoulder width ranges of a certain protective vest in the database, then that protective vest in the database is taken as the most suitable protective vest for the target medical patient.

3. The method for detecting a fall warning and anti-fall protective vest as described in claim 1, characterized in that, The analysis yielded the risk assessment coefficients for bed exit for the target medical patients at each data collection time point. Specifically, the analysis revealed: The distance from the bed, duration of time away from the bed, distance from each danger zone, and duration of stay for the target medical patient at each collection time point are recorded as follows: , , and ,in, This indicates the number corresponding to each data collection time point. , This indicates the number corresponding to each hazardous area. Let n be any integer greater than 2, and u be any integer greater than 2. Substitute them into the calculation formula. In the middle, we obtained the first Risk assessment coefficient for the target medical patient at each data collection time point. ,in, , , , These are the standard distance from the bed, standard time spent away from the bed, standard distance from the patient in the danger zone, and standard stay duration, respectively. , , , These are the weighting factors corresponding to the distance of medical patients from the bed, the duration of time spent away from the bed, the distance from the danger zone, and the duration of stay, respectively.

4. The method for detecting a fall warning and anti-fall protective vest as described in claim 3, characterized in that, The analysis of the risk level of bed exit for target medical patients at each data collection time point is as follows: The risk assessment coefficients for leaving the bed corresponding to the target medical patient at each collection time point are compared with the risk assessment coefficient ranges for each risk level leaving the bed in the database. If the risk assessment coefficient for leaving the bed corresponding to the target medical patient at a certain collection time point is within the risk assessment coefficient range for a certain risk level leaving the bed in the database, then the risk level leaving the bed in the database is taken as the risk level leaving the bed corresponding to the target medical patient at that collection time point. In this way, the risk level leaving the bed corresponding to the target medical patient at each collection time point is analyzed.

5. The method for detecting a fall warning and anti-fall protective vest as described in claim 1, characterized in that, The analysis of the fall risk level of the target medical patients at each data collection time point is as follows: The comprehensive standing assessment coefficients corresponding to the target medical patients at each collection time point are compared with the comprehensive standing assessment coefficient ranges corresponding to each fall risk level in the database. If the comprehensive standing assessment coefficient of the target medical patient at a certain collection time point is within the comprehensive standing assessment coefficient range corresponding to a certain fall risk level in the database, then the fall risk level in the database is used as the fall risk level corresponding to the target medical patient at that collection time point. In this way, the fall risk levels corresponding to the target medical patients at each collection time point are analyzed.

6. The fall warning and anti-fall protective vest used in the fall warning and anti-fall protective vest detection method as described in claim 1, comprising a protective vest body 1, characterized in that, The protective vest body 1 has four symmetrical pull strips 3 on both sides of the front. The protective vest body 1 has an alarm 2 on one side of the front. The protective vest body 1 has a zipper 4 at the junction of the front and back. The protective vest body 1 has a pull strip 3 on the upper side of the back. The protective vest body 1 has a sensor cloth 5 at the center of the back. The sensor cloth 5 has two symmetrical bed-leaving sensors 6 and two symmetrical standing sensors 7 inside.

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