Health and safety monitoring method and device for medical care robot and electronic equipment

Abstract

The invention provides a health and safety monitoring method and device for a medical care robot and electronic equipment, and belongs to the technical field of health and safety monitoring. The method comprises the following steps: monitoring a target person based on a first radar to obtain first point cloud data; determining the state of the target person based on the first point cloud data; controlling the robot based on the state of the target person; wherein the first radar is used for monitoring the state of a target person in the target area, and the first point cloud data is the state data of the target person. According to the health safety monitoring method and device for the medical care robot and the electronic equipment, the stability and reliability of safety monitoring can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of health and safety monitoring, and more specifically, to a health and safety monitoring method and device, and electronic equipment for a medical robot. Background Art

[0002] In today's society, the trend of population aging is obvious. At the same time, people pay more and more attention to health and safety. The health and safety monitoring of individuals, especially the elderly, patients and other vulnerable groups, has become a key issue that needs to be solved urgently.

[0003] Traditional monitoring methods have obvious shortcomings, and there are limitations in the process of health and safety monitoring of monitored personnel by medical robots. Medical robots can obtain three-dimensional point cloud data through radar to determine the status of target personnel, but once the target personnel exceeds the monitoring range or makes abnormal movements, false alarms are likely to occur, and the monitoring effect will be affected, resulting in low accuracy and reliability of monitoring of target personnel.

[0004] Therefore, an accurate and reliable method for health safety monitoring. Summary of the invention

[0005] The purpose of the present disclosure is to provide a health and safety monitoring method and device, and electronic equipment for a medical robot to improve the accuracy and reliability of safety monitoring.

[0006] A first aspect of the embodiments of the present disclosure provides a health and safety monitoring method for a medical robot, comprising: The target person is monitored based on the first radar to obtain first point cloud data; Determine the status of the target person based on the first point cloud data; Control the robot based on the target person's status; The first radar is used to monitor the status of a target person in a target area, and the first point cloud data is the status data of the target person.

[0007] A second aspect of the disclosed embodiments provides a health and safety monitoring device for a medical robot, comprising: A monitoring module, used for monitoring the target person based on the first radar to obtain first point cloud data; A state determination module, used to determine the state of the target person based on the first point cloud data; A control module for controlling the robot based on the state of the target person; The first radar is used to monitor the status of a target person in a target area, and the first point cloud data is the status data of the target person.

[0008] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned health and safety monitoring method for a medical robot are implemented.

[0009] The health and safety monitoring method and device for a medical robot and the electronic device provided by the embodiments of the present disclosure have the following beneficial effects: The present disclosure monitors the target person through the first radar, can obtain the point cloud data of the target person in real time, and can continuously and accurately collect data without interfering with the natural behavior of the target person. The present disclosure accurately identifies the status of the target person through the first point cloud data, and performs different actions according to the status of the target person, thereby improving the accuracy and reliability of safety monitoring. The present disclosure processes the data obtained by the first radar monitoring through the random forest algorithm to obtain multiple recognition results, and determines whether to turn on the second radar to monitor the target person based on the difference between the multiple recognition results, so that the present disclosure turns on the second radar when a single radar cannot monitor the target person, thereby improving the accuracy and reliability of health and safety monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a health and safety monitoring method for a medical robot provided in an embodiment of the present disclosure; Figure 2 A structural block diagram of a health and safety monitoring device for a medical robot provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0013] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A flow chart of a health and safety monitoring method for a medical robot provided in an embodiment of the present disclosure, the method comprising: S101: Monitor a target person based on a first radar to obtain first point cloud data.

[0015] In this embodiment, the first radar can be a fixed-position radar, which can monitor the status of a target person within a preset area (i.e., the target area). The status may be whether the target person falls down. The installation location may be a wall or ceiling of the target area or other location that is conducive to data collection.

[0016] Target personnel refer to the monitored persons, such as elderly people in nursing homes or patients undergoing sports rehabilitation in hospitals, who need to pay close attention to their physical condition and whether they have fallen or not. The target area can be a living room in a nursing home or a ward.

[0017] The first point cloud data refers to the data obtained by monitoring the target person through the first radar, which may include the position information and action information of the target person in space. This collection of points is called the first point cloud data. The first point cloud data can intuitively present the shape and position distribution of the target person in space.

[0018] It should be noted that the first point cloud data specifically refers to the point cloud data of the target person. In the actual application process, when the first radar is used to monitor the target person, the data obtained includes the target person and the environment around the target person. Therefore, before obtaining the first point cloud data, the background point cloud data can be eliminated. For example, by collecting and modeling the background point cloud data when there is no human activity, in the subsequent monitoring process, the point cloud data obtained in real time is compared with the background model, the background point cloud is subtracted, and only the point cloud data that may belong to the moving target (such as the human body) is retained. The background model can be updated regularly to adapt to changes in the environment, such as the movement of furniture.

[0019] Or use a target tracking algorithm, for example, to track the identified human target. In consecutive frames, the motion trajectory of the same human target is determined by matching the features and position information of the point cloud data. If it is found that the motion trajectory of a certain point cloud data does not conform to the law of human motion, or loses association with the previously tracked human target, it can be determined to be the motion data of other objects and excluded. This article will not go into details about how to eliminate the environmental point cloud data and finally obtain the first point cloud data.

[0020] S102: Determine the status of the target person based on the first point cloud data.

[0021] In this embodiment, as can be seen from the above content, the first point cloud data can reflect the shape and position distribution of the target person in space. The status can be whether it has fallen or other preset abnormal behaviors. Falling is used as an example for explanation in this application, and it should also be understood that this application is not limited to monitoring falling behavior.

[0022] In this embodiment, the first point cloud data can be input into the target neural network model, and the state of the target person, such as whether he has fallen, can be obtained according to the target neural network model.

[0023] It should be noted that the target neural network model should be trained with a large amount of point cloud data and its corresponding personnel status information data.

[0024] S103: Control the robot based on the state of the target person; wherein the first radar is used to monitor the state of the target person in the target area, and the first point cloud data is the state data of the target person.

[0025] In this embodiment, different control strategies or control methods can be set according to the status of the target person. For example, when the status of the target person is monitored to be abnormal, the robot can be controlled to sound and light alarms and send the information of the person falling to related equipment. The related equipment can be the mobile phone of a nursing staff in a nursing home or the mobile phone of a nurse in a hospital or the mobile phone of a family member. When it is monitored that the status of the target person is frequently in a pre-abnormal state, no alarm is required, and only the situation is sent to the relevant equipment. The robot in this application can also obtain the health information of the target person through the health monitoring equipment worn by the target person, such as monitoring the heart rate through a smart bracelet worn, and the health data obtained from the monitoring can also be sent to the relevant equipment together.

[0026] The pre-abnormal state refers to a state that is about to be determined as an abnormal state, for example, a state in which the target person is tripped but immediately stabilizes before falling, or an abnormal state caused by the target person's physical condition.

[0027] From the above, it can be concluded that the present disclosure monitors the target person through the first radar, can obtain the point cloud data of the target person in real time, and can continuously and accurately collect data without interfering with the natural behavior of the target person. The present disclosure accurately identifies the state of the target person through the first point cloud data, and performs different actions according to the state of the target person, thereby improving the accuracy and reliability of security monitoring.

[0028] In one embodiment of the present disclosure, a health and safety monitoring method for a medical robot further includes: Making a decision on the first point cloud data based on the first random forest model to obtain differences of multiple recognition results; In response to the difference between the multiple recognition results being greater than or equal to the first difference, monitoring the target person based on a second radar to obtain second point cloud data, where the second radar is a built-in radar of the robot; Determine target point cloud data based on the first point cloud data and the second point cloud data; The status of the target person is determined based on the target point cloud data.

[0029] In this embodiment, the first random forest model is a model based on the random forest algorithm. The random forest is an integrated learning method, which consists of multiple decision trees. A decision is made by integrating the results of multiple decision trees. The first random forest model here is used to analyze and process the first point cloud data to identify the state of the target person. The decision trees in the random forest algorithm are relatively independent.

[0030] The first random forest model is obtained by training with a large amount of point cloud data and its corresponding personnel status information data. It should be noted that the data set used by the first random forest model and the above-mentioned first neural network model can be the same, that is, the data can be the same, but the training process of the first neural network model is different from the training process of the first random forest model, and the output is also different. The first neural network model outputs a single recognition result, and the present application takes into account that multiple recognition results can be output through each decision tree in the first random forest model.

[0031] The difference can be determined based on the similarity of multiple recognition results, or can also be determined based on whether the multiple recognition results are consistent. The similarity can be calculated using the cosine similarity formula or the Pearson correlation coefficient formula, and the difference can be obtained by subtracting 1 from the similarity value, or a mapping table between similarity and difference can be pre-set.

[0032] Or, more simply, since the essence of the random forest algorithm model is that each decision tree votes, the degree of difference can be determined to determine whether the voting results are consistent. For example, if the voting results of all decision trees are exactly the same, that is, they all believe that the target person is in a certain state (for example, they all believe that the target person is walking normally), then the degree of difference can be determined to be 0, because there is no different judgment at this time, and the consistency of the results is the highest.

[0033] If the voting results of some decision trees are different from those of other decision trees, the difference is greater than 0. The specific value of the difference can be determined based on the ratio of the number of different voting results to the total number of voting results. For example, assuming that there are 100 decision trees in the first random forest model, 80 of which believe that the target person is in state A, and 20 believe that the target person is in state B, then the difference can be determined as 0.2 (that is, the ratio of the number of voting results other than the data with the most votes to the total number of voting results) to represent the difference between these recognition results.

[0034] If the difference is greater than or equal to the first difference, it means that the first radar cannot effectively monitor the target person. The first difference can be set according to experience or the requirements of the user.

[0035] Considering that the first radar is a fixed radar, its monitoring range is limited and is only limited to monitoring the target personnel in the target area. In nursing homes, hospitalization and other actual application scenarios, the first radar may be unable to effectively identify the status of the target personnel due to wall obstructions or other reasons. Therefore, the second radar can be activated when the first radar cannot effectively monitor the target personnel.

[0036] The second radar is the robot's built-in radar, that is, the second radar can be mobile. Considering that the radar has high power and generates severe heat, the second radar can be disabled when the first radar can effectively monitor the target person. The robot can follow or rotate the target person in a certain area. However, it should be noted that the monitoring area of ​​the robot's built-in second radar should overlap with the monitoring area of ​​the first radar (i.e. the target area) to better monitor the target person.

[0037] The second point cloud data is essentially the same as the first point cloud data, and the difference lies in the different monitoring radar positions. At this time, since the first point cloud data and the second point cloud data exist at the same time, the two data should be weighted or otherwise fused to obtain a final point cloud data, i.e., the target point cloud data, and the state of the target person is determined based on the target point cloud data, which can also be determined using the first neural network model, because the target point cloud data and the first point cloud data are essentially point cloud data, and the training data of the first neural network model is the point cloud data and the corresponding state.

[0038] It can be concluded from the above that the present disclosure makes decisions on the first point cloud data through the first random forest model, which can fully utilize the ensemble learning advantages of the random forest algorithm, combine the output results of multiple decision trees, and effectively reduce the recognition errors that may be caused by a single decision tree. When the difference between multiple recognition results is large, the second radar is enabled for supplementary monitoring, so that the present embodiment can automatically respond to special monitoring environments, such as wall obstructions, etc., thereby improving the accuracy and robustness of security monitoring.

[0039] In one embodiment of the present disclosure, determining target point cloud data based on first point cloud data and second point cloud data includes: Making decisions on the state feature points in the first point cloud data based on the second random forest model to obtain a plurality of state feature point sets, wherein each state feature point set includes a plurality of state feature points; Determine the expanded basic points based on multiple state feature point sets; Determine the extended basic data based on the extended basic point; Using the expanded basic data or data obtained by expanding the expanded basic data as third point cloud data; The third point cloud data and the second point cloud data are weightedly calculated to obtain target point cloud data.

[0040] In this embodiment, the second random forest model is trained by a data set consisting of a large amount of point cloud data and its corresponding state feature point data. State feature points refer to key data points that can reflect the state of the target person. These points contain important information related to the state of the target person. For example, for monitoring whether a fall occurs, the state feature points can be the human head, feet, human joints, center of gravity, etc. These points are also points used for data expansion or points to be expanded later.

[0041] Based on the above description, it can be known that there are multiple state feature points, and the set composed of all state feature points is the state feature point set. Each decision tree in the random forest can output a state feature point set. It is possible to determine which state feature points are relatively accurate and which state feature points are relatively fuzzy by analyzing whether the state feature points in the state feature point set output by each decision tree are consistent. For example, for a random forest composed of 100 decision trees, 50 of its output results are [A1, A2, A3, A4], 30 are [A1, A2, A3, A5], and 20 are [A1, A2, A6, A5], where A1, A2, A3, A4, A5, and A6 are the identified state points. The preset threshold is 75, and the same data greater than 75 are the expanded basic points. In the above results, A1, A2, and A3 are the expanded basic points. That is, when the number of state feature points in all state feature point sets is greater than the first preset number, the state feature point is defined as an expanded basic point.

[0042] The extended basic data is data determined based on the extended basic points. For example, if the extended basic point is the head of the target person, the extended basic data should be the point cloud data around the extended basic point. The extended basic data can be the point cloud data of the head and neck. This is because for the same person, the position and coordinates of the head are determined, and the position and coordinates of the neck are also basically fixed. The extended basic data is the point cloud data around the extended basic point. It should be noted that the extended basic data corresponding to each extended basic point should be different. The extended rules can be pre-trained and set according to the various physical indicators of the target person. The above steps are all performed on the first point cloud data obtained by the first radar monitoring.

[0043] Considering that when the number of expanded basic points is small, the reliability of the expanded point cloud data will be correspondingly reduced, the expanded basic data or the data obtained by expanding the expanded basic data can be used as the third point cloud data in this application, and the third point cloud data and the second point cloud data are weighted.

[0044] Specifically, in one embodiment of the present disclosure, the expanded basic data or the data obtained by expanding the expanded basic data is used as the third point cloud data, including: In response to the number of the expanded basic points being less than the total number of the state feature points of the preset ratio, using the expanded basic data as the third point cloud data; In response to the number of the expanded basic points being greater than or equal to the total number of the state feature points of a preset ratio, expanding the data of the area to be expanded based on the second point cloud data and the expanded basic points to obtain expanded data; The third point cloud data is determined based on the expanded basic data and the expanded data.

[0045] In this embodiment, when the number of expanded basic points is less than the total number of state feature points of the preset ratio, it means that even if the data is expanded, the reliability of the point cloud data obtained will be affected to a certain extent, so the data is no longer expanded at this time, and only the data around the expanded basic points (expanded basic data) are used as the third point cloud data and then weighted calculated with the second point cloud data. For example, the expanded basic point is only the head of the target person, and the expanded basic data is the point cloud data of the head and neck of the target person. At this time, the third point cloud data is only the point cloud data of the head and neck of the target person, and the point cloud data of other parts are not expanded, nor weighted calculated.

[0046] When the number of expanded basic points is greater than or equal to the total number of state feature points in a preset ratio, it means that the point cloud data of the area to be expanded can still be considered based on the expanded basic points determined in the first point cloud data, and the expanded data can be used as the third point cloud data and weighted calculated on the second point cloud data.

[0047] Similarly, the second point cloud data also has the same state feature points as the first point cloud data. The relative positions of the state feature points in the second point cloud data can be used as the expansion basis and expansion logic in the first point cloud data, that is, the relative position relationship of each state feature point is obtained through the second point cloud data, and then the points to be expanded in the area to be expanded are determined based on the relative position relationship and the expansion base points in the first point cloud data. The points to be expanded refer to the state feature points other than the expansion base points, and then the data around the points to be expanded are obtained based on the points to be expanded, in the same way as the above-mentioned method of determining the expansion base data based on the expansion base points. For example, if the point to be expanded is the head, then the area to be expanded is the head and neck area.

[0048] The third point cloud data can be obtained through the above process, that is, the third point cloud data has two situations. If the number of extended basic points is less than the total number of state feature points in the preset ratio, the third point cloud data is the extended basic data. If the number of extended basic points is greater than or equal to the total number of state feature points in the preset ratio, the third point cloud data is the expanded complete point cloud data.

[0049] It can be concluded from the above that the present disclosure determines the extended basic points by analyzing the state feature point sets output by multiple decision trees, which represent reliable and representative state features. Furthermore, based on the extended basic data determined by the extended basic points, the third point cloud data is determined, and weighted calculation is performed on the second point cloud data so that the target point cloud data can comprehensively reflect the monitoring results of the two radars, thereby improving the accuracy and reliability of safety monitoring.

[0050] In one embodiment of the present disclosure, weighted calculation is performed on the third point cloud data and the second point cloud data to obtain target point cloud data, including: In response to the third point cloud data being the expanded basic data, determining a first weight adjustment step length based on an importance level of the expanded basic data; Alternatively, in response to the third point cloud data being data expanded by expanding the basic data, determining the first weight adjustment step length based on the number of expanded basic points; Adjusting the first weight reference value based on the first weight adjustment step length to obtain a first weight; adjusting the second weight reference value based on the first weight adjustment step length to obtain a second weight; Perform weighted calculation based on the first weight, the second weight, the third point cloud data, and the second point cloud data to obtain target point cloud data; The first weight is the weight corresponding to the third point cloud data, the second weight is the weight corresponding to the second point cloud data, and the first weight reference value and the second weight reference value are adjusted in different directions.

[0051] In this embodiment, considering that there are two situations of the third point cloud data, different control strategies are adopted for the above two situations in this embodiment. When the third point cloud data is extended basic data, that is, incomplete data, the first weight adjustment step can be determined according to the importance level of the extended basic data. The importance level of the extended basic data refers to the importance level when performing fall monitoring (abnormal monitoring). The importance level of the extended basic data can be equal to the importance level of the extended basic points. The importance level of the extended basic points can be preset. For example, in the process of fall monitoring, the importance level of the head is relatively large, which can be 4, then its corresponding importance level should be the highest, and the importance level of the feet is relatively low, which can be 2, then its corresponding importance level can be adaptively reduced.

[0052] The first weight adjustment step length may be determined according to a mapping table of the importance levels of the extended basic data and the corresponding first weight adjustment step lengths, as shown in Table 1: Table 1: Mapping table of importance levels of expanded basic data and their corresponding first weight adjustment steps

[0053] It should be noted that the data in the above table have positive and negative signs. In the subsequent adjustment process, the above positive and negative signs should be used for calculation. A positive number indicates that the subsequent adjustment of the first weight reference value is to increase, and a negative sign indicates that the subsequent adjustment of the first weight reference value is to decrease. This is because when the extended basic data is accurate data that has been determined by the second random forest model, and the greater the importance level of the extended basic data, the weight should be appropriately increased during the weighted calculation process. On the contrary, as the importance level decreases, the corresponding weight during the weighted calculation should also be appropriately reduced. The first weight reference value is the weight corresponding to the third point cloud data, and the second weight reference value is the weight corresponding to the second point cloud data. The sum of the first weight and the second weight obtained by the final adjustment should be 1, so the adjustment directions of the first weight reference value and the second weight reference value are different.

[0054] When the third point cloud data is data obtained by expanding the basic data, the first weight adjustment step should be determined based on the number of expanded basic points. This is because the more the number of expanded basic points, the higher the reliability of the third point cloud data obtained by the expansion. The first weight adjustment step can be determined based on the first formula. First formula: ,in, is the first weight adjustment step size, is the basic step length, is the proportionality coefficient, To expand the number of basic points, the basic step size can be determined based on experience or usage preference, for example, it can be 0.05, and the proportional coefficient can be adjusted according to actual conditions, for example, it can be 0.01.

[0055] As the number of expanded basic points increases, the first weight adjustment step size also increases, and the adjustment direction of the first weight reference value should be to increase, that is, as the number of expanded basic points increases, the reliability of the third point cloud data increases, and its proportion weight in the weighted calculation process should be larger, and the reference value of the second weight should be reduced accordingly with the same step size. However, it should be noted that since the major premise of the above steps is that the first radar monitoring is inaccurate, the second radar is turned on, and even after the expansion of the third point cloud data, its reliability is less than the reliability of the second point cloud data. Therefore, when setting the above weight step size and reference value, it should be ensured that the first weight is less than the second weight. The first weight reference value and the second weight reference value can be determined based on experience.

[0056] From the above, it can be concluded that the present disclosure adopts different weight adjustment strategies according to different situations of the third point cloud data, thereby ensuring the rationality and accuracy of the weights in the weighted calculation process. When the third point cloud data is expanded basic data, the weight adjustment step is determined by the importance level of the expanded basic data. The method takes into account the differences in importance of different parts in monitoring, so that the weighted results are more in line with actual needs. When the third point cloud data is expanded data, the weight adjustment step is based on the number of expanded basic points, reflecting the positive impact of data expansion on improving data reliability, thereby ensuring the reasonable distribution of weights in weighted calculations.

[0057] In one embodiment of the present disclosure, determining the state of a target person based on target point cloud data includes: In response to the presence of an abnormal feature and / or a pre-abnormal feature in the first point cloud data, increasing the monitoring frequency of the first radar based on the first frequency step size; In response to the presence of abnormal features and / or pre-abnormal features in the target point cloud data, increasing the monitoring frequency of the first radar and the monitoring frequency of the second radar based on the first frequency step size; The status of the target person is determined based on the adjacent frame data in the target point cloud data.

[0058] In this embodiment, the abnormal feature refers to the abnormal behavior of the target person, such as the feature reflected in the point cloud data when falling, for example, the height difference between the head and the feet in the point cloud data is less than a certain threshold, or the arm support action feature, etc., which can be set based on a large amount of falling behavior data. The pre-abnormal feature refers to the feature that is about to be determined as an abnormal feature, corresponding to the pre-abnormal state in S103. The pre-abnormal feature is the data feature for determining the pre-abnormal state, such as the data feature in the point cloud data of the target person being tripped but immediately stabilizing before falling. Whether the abnormal feature or the pre-abnormal feature appears in the target point cloud data can be identified by the second neural network model, and the second neural network model is a model obtained by training a large amount of normal point cloud data, point cloud data with abnormal features, and point cloud data with pre-abnormal features.

[0059] If abnormal features and / or pre-abnormal features appear in the target point cloud data, the monitoring frequency of the currently enabled radar should be increased by the same step size, and the target personnel should be monitored more closely based on the adjusted monitoring frequency. It should be noted that the initial monitoring frequencies of the first radar and the second radar should be the same, and the first frequency step size should also be the same, because only in this way can the data of the two radars be effectively expanded and weighted. The first frequency step size can be determined according to the actual scenario or experiment.

[0060] In this embodiment, the status of the target person can be determined based on the data of adjacent frames in the target point cloud data. This is because the target person's action features such as squatting, tying shoelaces, and going to the toilet are easily judged as abnormal features or pre-abnormal features, so whether an abnormality or pre-abnormality has actually occurred can be judged according to the following method.

[0061] In one embodiment of the present disclosure, determining the state of a target person based on adjacent frame data in the target point cloud data includes: In response to the similarities of a first number of consecutive adjacent frame data in the target point cloud data being less than a first similarity, and an abnormal feature appearing in the target point cloud data, the state of the target person is an abnormal state; In response to the similarities of a first number of consecutive adjacent frame data in the target point cloud data being less than a first similarity, and pre-abnormal features appearing in the target point cloud data, and no abnormal features appearing, the state of the target person is a pre-abnormal state.

[0062] In this embodiment, considering that squatting or shoe tying is a relatively slow process, the posture changes in the multi-frame data are relatively gentle and have a certain degree of continuity; while falling often happens suddenly, and the posture changes in the continuous multi-frames are more drastic and sudden. By analyzing the similarity of the point cloud data in the multi-frame data, the two situations can be distinguished more accurately.

[0063] The similarity of two point cloud data can be calculated based on the Hausdorff distance method. The principle is to calculate the maximum value of the two-way closest distance between all points in the two frames of point cloud, which measures the maximum distance from a point in one point cloud set to another point cloud set, and can reflect the overall difference between the two frames of point cloud. It will not be repeated in this application. The first quantity and the first similarity can be determined based on experience and actual usage scenarios. When both abnormal features and pre-abnormal features appear in the target point cloud data, the abnormal features shall prevail, because the pre-abnormal features generally appear before the abnormal state features.

[0064] In one embodiment of the present disclosure, controlling a robot based on the state of a target person includes: In response to the target person being in an abnormal state, controlling the robot to issue a first-level alarm; In response to the frequency of the target person appearing in the pre-abnormal state exceeding the first frequency, the robot is controlled to issue a secondary alarm.

[0065] In this embodiment, similar to the explanation of S103, different control modes can be set according to the different states of the target personnel. When an abnormal state occurs, the robot is controlled to give an audible and visual alarm and send the information of the personnel falling to the relevant equipment, which is a first-level alarm. When the target personnel is monitored to be in a pre-abnormal state frequently (that is, the frequency of the target personnel in the pre-abnormal state exceeds the first frequency), no alarm may be given, and only the situation may be sent to the relevant equipment, which is a second-level alarm, and the first frequency is a preset threshold. The relevant personnel can take different processing methods according to the received alarm information. For example, in the case of a second-level alarm, timely review and observation are made to see whether there are foreign objects that are easy to trip people at the location where the pre-abnormal state occurs.

[0066] From the above, it can be concluded that when abnormal features or pre-abnormal features appear in the first point cloud data, the present disclosure can automatically increase the monitoring frequency of the first radar based on the first frequency step, which can ensure that when potential risks occur, the state changes of the target person are captured more quickly. If abnormal features or pre-abnormal features also appear in the target point cloud data, this embodiment further increases the monitoring frequency of the first radar and the second radar, thereby achieving all-round and closer monitoring of the target person, and improving the accuracy and reliability of safety monitoring. By analyzing the similarity of multiple consecutive frames in the target point cloud data, the present disclosure can more accurately judge the state of the target person, which helps to distinguish normal behaviors such as squatting and tying shoelaces from abnormal behaviors such as falling, and further improves the judgment accuracy of safety monitoring.

[0067] In one embodiment of the present disclosure, a health and safety monitoring method for a medical robot further includes: In response to the frequency of the target person appearing in a pre-abnormal state exceeding the first frequency, the initial monitoring frequency of the first radar is increased by the first compensation frequency as the monitoring frequency of the first radar, and the initial monitoring frequency of the second radar is increased by the first compensation frequency as the monitoring frequency of the second radar.

[0068] In this embodiment, considering that when a person is about to fall frequently, the initial monitoring frequency preset by the first radar and the second radar should be appropriately increased to monitor the status information of the target person more frequently. The first compensation frequency is a preset frequency, which can be determined based on experience.

[0069] Secondly, it is also taken into consideration that due to the increase in the initial frequency of the radar, when the second neural network model detects the emergence of abnormal features and / or pre-abnormal features, it will still increase the monitoring frequency of the first radar based on the first frequency step. However, at this time, due to the increase in frequency, the number of frames will become more intensive, and when judging the similarity between adjacent frames, it will be more likely to be judged as normal behavior, which will lead to misjudgment. Therefore, the similarity threshold and the first number should be set more strictly.

[0070] For example, in one embodiment of the present disclosure, the first similarity adjustment step is determined based on the second formula, and the first similarity is reduced based on the first similarity adjustment step; A first quantity adjustment step is determined based on the third formula, and the first quantity is increased based on the first quantity adjustment step.

[0071] In this embodiment, the second formula can be ,in, Adjust the step size for the first similarity, is the first adjustment coefficient, which is used to control the sensitivity of the first similarity adjustment and needs to be determined according to actual experiments and scenarios. is the first compensation frequency, The system presets the maximum monitoring frequency, which serves as a reference value to normalize the first compensation frequency to the range of 0-1 to ensure the rationality and consistency of the adjustment step.

[0072] The third formula can be ,in, Adjust the step size for the first quantity, is the second adjustment coefficient, which is used to adjust the amplitude of the first quantity adjustment, and its value also needs to be set according to actual conditions.

[0073] It can be concluded from the above that when the frequency of the target person's pre-abnormal state exceeds the preset first frequency, the present disclosure can automatically increase the initial monitoring frequency of the first radar and the second radar at the first compensation frequency, thereby improving the timeliness and accuracy of safety monitoring. This embodiment also takes into account that after the initial frequency of the radar is increased, the similarity judgment between adjacent frames may be affected, and adopts a more stringent similarity threshold and first quantity setting, ensuring that even when the monitoring frequency is increased, the behavior of the target person can still be accurately judged, avoiding the problem of misjudgment caused by dense frames, and improving the accuracy and reliability of safety monitoring.

[0074] Corresponding to the health and safety monitoring method for a medical robot in the above embodiment, Figure 2 This is a structural block diagram of a health and safety monitoring device for a medical robot provided in one embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The health and safety monitoring device 20 for a medical robot includes: a monitoring module 21, a state determination module 22 and a control module 23.

[0075] The monitoring module 21 is used to monitor the target person based on the first radar to obtain the first point cloud data; A state determination module 22, used to determine the state of the target person based on the first point cloud data; A control module 23, used to control the robot based on the state of the target person; The first radar is used to monitor the status of a target person in a target area, and the first point cloud data is the status data of the target person.

[0076] In one embodiment of the present disclosure, the health and safety monitoring device 20 for a medical robot further includes: a second radar monitoring module; A second radar monitoring module is used to make a decision on the first point cloud data based on the first random forest model to obtain the difference between multiple recognition results; In response to the difference between the multiple recognition results being greater than or equal to the first difference, monitoring the target person based on a second radar to obtain second point cloud data, where the second radar is a built-in radar of the robot; Determine target point cloud data based on the first point cloud data and the second point cloud data; The status of the target person is determined based on the target point cloud data.

[0077] In one embodiment of the present disclosure, the second radar monitoring module is specifically used to make decisions on the state feature points in the first point cloud data based on the second random forest model to obtain multiple state feature point sets, wherein each state feature point set includes multiple state feature points; Determine the expanded basic points based on multiple state feature point sets; Determine the extended basic data based on the extended basic point; Using the expanded basic data or data obtained by expanding the expanded basic data as third point cloud data; The third point cloud data and the second point cloud data are weightedly calculated to obtain target point cloud data.

[0078] In one embodiment of the present disclosure, the second radar monitoring module is further configured to: In response to the number of the expanded basic points being less than the total number of the state feature points of the preset ratio, using the expanded basic data as the third point cloud data; In response to the number of the expanded basic points being greater than or equal to the total number of the state feature points of a preset ratio, expanding the data of the area to be expanded based on the second point cloud data and the expanded basic points to obtain expanded data; The third point cloud data is determined based on the expanded basic data and the expanded data.

[0079] In one embodiment of the present disclosure, the second radar monitoring module is further configured to: In response to the third point cloud data being the expanded basic data, determining a first weight adjustment step length based on an importance level of the expanded basic data; Alternatively, in response to the third point cloud data being data expanded by expanding the basic data, determining the first weight adjustment step length based on the number of expanded basic points; Adjusting the first weight reference value based on the first weight adjustment step length to obtain a first weight; adjusting the second weight reference value based on the first weight adjustment step length to obtain a second weight; Perform weighted calculation based on the first weight, the second weight, the third point cloud data, and the second point cloud data to obtain target point cloud data; The first weight is the weight corresponding to the third point cloud data, the second weight is the weight corresponding to the second point cloud data, and the first weight reference value and the second weight reference value are adjusted in different directions.

[0080] In one embodiment of the present disclosure, the second radar monitoring module is further configured to: In response to the presence of an abnormal feature and / or a pre-abnormal feature in the first point cloud data, increasing the monitoring frequency of the first radar based on the first frequency step size; In response to the presence of abnormal features and / or pre-abnormal features in the target point cloud data, increasing the monitoring frequency of the first radar and the monitoring frequency of the second radar based on the first frequency step size; The status of the target person is determined based on the adjacent frame data in the target point cloud data.

[0081] In one embodiment of the present disclosure, the second radar monitoring module is further configured to: In response to the similarities of a first number of consecutive adjacent frame data in the target point cloud data being less than a first similarity, and an abnormal feature appearing in the target point cloud data, the state of the target person is an abnormal state; In response to the similarities of a first number of consecutive adjacent frame data in the target point cloud data being less than a first similarity, and pre-abnormal features appearing in the target point cloud data, and no abnormal features appearing, the state of the target person is a pre-abnormal state.

[0082] In one embodiment of the present disclosure, the control module is specifically configured to: In response to the target person being in an abnormal state, controlling the robot to issue a first-level alarm; In response to the frequency of the target person appearing in the pre-abnormal state exceeding the first frequency, the robot is controlled to issue a secondary alarm.

[0083] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0084] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0085] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0086] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0087] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the health and safety monitoring method for a medical robot provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0088] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0089] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0090] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0092] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0093] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A health and safety monitoring method for a medical robot, characterized in that: include: The target person is monitored based on the first radar to obtain first point cloud data; Determine the status of the target person based on the first point cloud data; controlling the robot based on the status of the target person; Wherein, the first radar is used to monitor the status of the target person in the target area, and the first point cloud data is the status data of the target person; The health and safety monitoring method for the medical robot also includes: Making a decision on the first point cloud data based on a first random forest model to obtain differences of multiple recognition results; In response to the difference between the multiple recognition results being greater than or equal to the first difference, monitoring the target person based on a second radar to obtain second point cloud data, where the second radar is a built-in radar of the robot; Determine target point cloud data based on the first point cloud data and the second point cloud data; The status of the target person is determined based on the target point cloud data.

2. The health and safety monitoring method for a medical robot according to claim 1, characterized in that: The determining target point cloud data based on the first point cloud data and the second point cloud data comprises: Making decisions on the state feature points in the first point cloud data based on a second random forest model to obtain a plurality of state feature point sets, wherein each state feature point set includes a plurality of state feature points; Determining an expanded basic point based on the multiple state feature point sets; Determining extended basic data based on the extended basic points; Using the expanded basic data or data obtained by expanding the expanded basic data as third point cloud data; The target point cloud data is obtained by performing weighted calculation on the third point cloud data and the second point cloud data.

3. The health and safety monitoring method for a medical robot according to claim 2, characterized in that: The step of using the expanded basic data or the data obtained by expanding the expanded basic data as the third point cloud data includes: In response to the number of the expanded basic points being less than the total number of state feature points of a preset ratio, using the expanded basic data as third point cloud data; In response to the number of the expanded basic points being greater than or equal to the total number of state feature points of a preset ratio, expanding the data of the area to be expanded based on the second point cloud data and the expanded basic points to obtain expanded data; Third point cloud data is determined based on the extended basic data and the extended data.

4. The health and safety monitoring method for a medical robot according to claim 3, characterized in that: The step of performing weighted calculation on the third point cloud data and the second point cloud data to obtain the target point cloud data comprises: In response to the third point cloud data being the extended basic data, determining a first weight adjustment step length based on an importance level of the extended basic data; Alternatively, in response to the third point cloud data being data obtained by expanding the expanded basic data, a first weight adjustment step is determined based on the number of the expanded basic points; Adjusting a first weight reference value based on the first weight adjustment step length to obtain a first weight; adjusting a second weight reference value based on the first weight adjustment step length to obtain a second weight; Perform weighted calculation based on the first weight, the second weight, the third point cloud data and the second point cloud data to obtain the target point cloud data; The first weight is a weight corresponding to the third point cloud data, the second weight is a weight corresponding to the second point cloud data, and the first weight reference value and the second weight reference value are adjusted in different directions.

5. The health and safety monitoring method for a medical robot according to claim 1, characterized in that: The determining the state of the target person based on the target point cloud data includes: In response to the presence of abnormal features and / or pre-abnormal features in the first point cloud data, increasing the monitoring frequency of the first radar based on a first frequency step size; In response to the presence of abnormal features and / or pre-abnormal features in the target point cloud data, increasing the monitoring frequency of the first radar and the monitoring frequency of the second radar based on a first frequency step size; The state of the target person is determined based on adjacent frame data in the target point cloud data.

6. The health and safety monitoring method for a medical robot according to claim 5, characterized in that: The determining the state of the target person based on adjacent frame data in the target point cloud data includes: In response to the similarities of a first number of consecutive adjacent frame data in the target point cloud data being less than a first similarity, and an abnormal feature appearing in the target point cloud data, the state of the target person is an abnormal state; In response to the similarities of a first number of consecutive adjacent frame data in the target point cloud data being less than a first similarity, and pre-abnormal features appearing in the target point cloud data, and no abnormal features appearing, the state of the target person is a pre-abnormal state.

7. The health and safety monitoring method for a medical robot according to claim 6, characterized in that: The controlling the robot based on the state of the target person comprises: In response to the target person being in an abnormal state, controlling the robot to issue a first-level alarm; In response to the frequency of the target person appearing in the pre-abnormal state exceeding the first frequency, the robot is controlled to issue a secondary alarm.

8. A health and safety monitoring device for a medical robot, characterized in that: include: A monitoring module, used for monitoring the target person based on the first radar to obtain first point cloud data; A status determination module, used to determine the status of the target person based on the first point cloud data; A control module, used for controlling the robot based on the state of the target person; Wherein, the first radar is used to monitor the status of the target person in the target area, and the first point cloud data is the status data of the target person; The health and safety monitoring device for the medical robot also includes: A second radar monitoring module, used for making a decision on the first point cloud data based on a first random forest model to obtain a difference between multiple recognition results; In response to the difference between the multiple recognition results being greater than or equal to the first difference, monitoring the target person based on a second radar to obtain second point cloud data, where the second radar is a built-in radar of the robot; Determine target point cloud data based on the first point cloud data and the second point cloud data; The status of the target person is determined based on the target point cloud data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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