Sedentariness risk grading early warning method, device and equipment and storage medium
Patent Information
- Application Number
- CN202311847764.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-28
- Publication Date
- 2025-07-01
Smart Images
Figure CN120236746A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of risk detection, and in particular, to a sedentary risk classification and early warning method, device, equipment, and storage medium. Background Art
[0002] Currently, the sedentary detection method usually obtains the height of a person's head using depth data, and determines whether the target enters the sedentary and lying-down determination process by comparing the height threshold of the head. After determining that the person is in a sedentary and lying-down state, a continuous holding stage determination is performed, and the continuous time is accumulated. When the continuous time exceeds the time threshold, an alarm signal is sent.
[0003] However, the above-mentioned sedentary detection scheme based on height and time accumulation cannot identify the risk levels of different sedentary actions, resulting in a low accuracy rate of sedentary risk classification and early warning, and further leading to low nursing efficiency. Summary of the Invention
[0004] The main objective of the present invention is to provide a sedentary risk classification and early warning method, device, equipment, and storage medium, aiming to solve the technical problem that the related technology cannot identify the risk levels of different sedentary actions, resulting in a low accuracy rate of sedentary risk classification and early warning, and further leading to low nursing efficiency.
[0005] To achieve the above objective, the present invention provides a sedentary risk classification and early warning method, and the sedentary risk classification and early warning method includes:
[0006] Detect the cumulative sitting duration of the target object in the sitting state, and detect whether the target object is in a sedentary state according to the cumulative sitting duration, where the sedentary state is a state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration;
[0007] When it is detected that the target object is in a sedentary state, obtain the physical sign information and limb information of the target object;
[0008] Analyze the physical sign stability degree of the target object according to the physical sign information, and analyze the limb activity state of the target object according to the limb information;
[0009] Determine the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state;
[0010] Generate an early warning strategy according to the sedentary risk level, and send an early warning information to the corresponding background device based on the early warning strategy, and / or control a preset alarm device to give an early warning based on the early warning strategy.
[0011] Optionally, the step of, when it is detected that the target object is in a sedentary state, obtaining the physical sign information and limb information of the target object includes:
[0012] When it is detected that the target object is in a sedentary state, receive the echo signal corresponding to the detection signal, where the detection signal is emitted by a radar detection device, and the echo signal is the signal generated by reflection when the detection signal propagates to the target object;
[0013] Perform feature analysis on the echo signal, and obtain the physical sign information and limb information of the target object according to the analysis result.
[0014] Optionally, the performing feature analysis on the echo signal and obtaining the physical sign information and limb information of the target object according to the analysis result includes:
[0015] Compare and analyze the detection signal and the echo signal to obtain the signal change information of the echo signal compared to the detection signal, where the signal change information includes phase change information and / or frequency change information;
[0016] Capture the chest movement information and limb movement information of the target object according to the signal change characteristics;
[0017] Obtain the physical sign information of the target object according to the chest movement information, and obtain the limb information of the target object according to the limb movement information.
[0018] Optionally, the analyzing the physical sign stability degree of the target object according to the physical sign information and analyzing the limb activity state of the target object according to the limb information includes:
[0019] Match the physical sign information with abnormal physical sign information, and analyze the physical sign stability degree of the target object according to whether the physical sign information matches the abnormal physical sign information;
[0020] Obtain the limb speed of the target object from the limb information, and analyze the limb activity state of the target object according to whether the limb speed is greater than a preset speed threshold.
[0021] Optionally, the determining the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state includes:
[0022] When the physical sign stability degree is unstable, determine that the sedentary risk level of the target object is the first risk level;
[0023] When the physical sign stability degree is stable and the limb activity state is an inactive state, determine that the sedentary risk level of the target object is the second risk level, and the second risk level is lower than the first risk level;
[0024] When the physical sign stability level is stable and the limb activity state is an active state, it is determined that the sedentary risk level of the target object is the third risk level, and the third risk level is lower than the second risk level.
[0025] Optionally, the detecting the cumulative sitting duration of the target object in the sitting state and detecting whether the target object is in a sedentary state according to the cumulative sitting duration includes:
[0026] Obtain the position information and height information of the target object;
[0027] Detect the maximum position fluctuation value of the target object within a preset time period according to the position information;
[0028] Count the number of target frames in which the height value of the target object is greater than a preset sitting height threshold within the preset time period according to the height information;
[0029] When the maximum position fluctuation value is less than a preset position fluctuation threshold and the number of target frames is less than a preset number of frames, it is determined that the target object is in a sitting state;
[0030] Count the cumulative sitting duration of the target object in the sitting state, and detect whether the target object is in a sedentary state according to the cumulative sitting duration.
[0031] Optionally, the obtaining the position information and height information of the target object includes:
[0032] Transmit a detection signal to the target object through a radar detection device, and receive the echo signal generated by reflection when the detection signal propagates to the target object;
[0033] Compare the detection signal with the echo information, and obtain the position information and height information of the target object according to the comparison result.
[0034] In addition, to achieve the above object, the present invention also proposes a sedentary risk classification and early warning device, and the sedentary risk classification and early warning device includes:
[0035] A detection module, configured to detect the cumulative sitting duration of the target object in the sitting state, and detect whether the target object is in a sedentary state according to the cumulative sitting duration, where the sedentary state is a state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration;
[0036] An obtaining module, configured to obtain the physical sign information and limb information of the target object when it is detected that the target object is in a sedentary state;
[0037] An analysis module, configured to analyze the physical sign stability of the target object according to the physical sign information, and analyze the limb movement state of the target object according to the limb information;
[0038] A determination module, configured to determine the sedentary risk level of the target object according to the physical sign stability and the limb movement state;
[0039] An early warning module, configured to generate an early warning strategy according to the sedentary risk level, and send an early warning message to the corresponding background device based on the early warning strategy, and / or control a preset alarm device to give an alarm based on the early warning strategy.
[0040] In addition, to achieve the above object, the present invention further provides a sedentary risk classification and early warning device, where the sedentary risk classification and early warning device includes a memory, a processor, and a sedentary risk classification and early warning program stored on the memory and executable on the processor. The sedentary risk classification and early warning program is configured to implement the sedentary risk classification and early warning method as described above.
[0041] In addition, to achieve the above object, the present invention further provides a storage medium, where a sedentary risk classification and early warning program is stored on the storage medium. When the sedentary risk classification and early warning program is executed by a processor, it implements the sedentary risk classification and early warning method as described above.
[0042] In the present invention, the cumulative sitting duration of the target object in the sitting state is detected, and whether the target object is in a sedentary state is detected according to the cumulative sitting duration. Wherein, the sedentary state is a state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration. When it is detected that the target object is in a sedentary state, the physical sign information and limb information of the target object are obtained, the physical sign stability of the target object is detected according to the physical sign information, and the limb movement state of the target object is detected according to the limb information. The sedentary risk level of the target object is determined according to the physical sign stability and the limb movement state, an early warning strategy is generated according to the sedentary risk level, and an early warning message is sent to the corresponding background device based on the early warning strategy and / or a preset alarm device is controlled to give an alarm based on the early warning strategy; Since the present invention introduces limb speed and physical sign information for sedentary risk detection, it can achieve more accurate sedentary detection, and then can achieve the risk level division of different sedentary actions, improve the accuracy of sedentary risk classification and early warning, and because the present invention generates a corresponding early warning strategy according to the sedentary risk level for early warning, it can facilitate users to make independent responses to different risk levels, and then improve the nursing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] Figure 1 It is a schematic structural diagram of a sedentary risk classification and early warning device in the hardware operating environment related to the embodiment solution of the present invention;
[0044] Figure 2 It is a schematic flowchart of the first embodiment of the sedentary risk classification and early warning method of the present invention;
[0045] Figure 3 It is a schematic layout diagram of the radar detection equipment for an embodiment of the sedentary risk classification and early warning method of the present invention;
[0046] Figure 4 It is a schematic flowchart of the second embodiment of the sedentary risk classification and early warning method of the present invention;
[0047] Figure 5 It is a schematic flowchart of the third embodiment of the sedentary risk classification and early warning method of the present invention;
[0048] Figure 6 It is a schematic flowchart of the fourth embodiment of the sedentary risk classification and early warning method of the present invention;
[0049] Figure 7 It is a schematic diagram of the radar detection equipment module for an embodiment of the sedentary risk classification and early warning method of the present invention;
[0050] Figure 8 It is a schematic logic diagram of the status detection module for an embodiment of the sedentary risk classification and early warning method of the present invention;
[0051] Figure 9 It is a schematic logic diagram of the physical sign detection module for an embodiment of the sedentary risk classification and early warning method of the present invention;
[0052] Figure 10 It is a schematic logic diagram of the alarm module for an embodiment of the sedentary risk classification and early warning method of the present invention;
[0053] Figure 11 It is a structural block diagram of the first embodiment of the sedentary risk classification and early warning device of the present invention.
[0054] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiment
[0055] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
[0056] Refer to Figure 1 , Figure 1 It is a schematic structural diagram of the sedentary risk classification and early warning device for the hardware operating environment involved in the embodiment solution of the present invention.
[0057] Such as Figure 1As shown in the figure, the sedentary risk grading and early warning device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface. For the wired interface of the user interface 1003, it may be a USB interface in the present invention. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed Random Access Memory (RAM) or a stable memory (Non-volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0058] Those skilled in the art can understand that Figure 1 the structure shown in does not constitute a limitation on the sedentary risk grading and early warning device, and it may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.
[0059] As Figure 1 shown, the memory 1005 regarded as a computer storage medium may include an operating system, a network communication module, a user interface module, and a sedentary risk grading and early warning program.
[0060] In Figure 1 the sedentary risk grading and early warning device shown, the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server; the user interface 1003 is mainly used to connect to the user device; the sedentary risk grading and early warning device calls the sedentary risk grading and early warning program stored in the memory 1005 through the processor 1001 and executes the sedentary risk grading and early warning method provided by the embodiments of the present invention.
[0061] Based on the above hardware structure, an embodiment of the sedentary risk grading and early warning method of the present invention is proposed.
[0062] Referring to Figure 2 , Figure 2 which is a schematic flowchart of the first embodiment of the sedentary risk grading and early warning method of the present invention, the first embodiment of the sedentary risk grading and early warning method of the present invention is proposed.
[0063] Step S10: Detect the cumulative sitting duration of the target object in the sitting state, and detect whether the target object is in a sedentary state according to the cumulative sitting duration, where the sedentary state is the state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration.
[0064] It should be understood that the execution subject of this embodiment can be a sedentary risk classification and warning device with data processing, network communication, program running, and detection functions. Among them, the sedentary risk classification and warning device includes, but is not limited to, radar detection devices, infrared sensing devices, visual detection devices, etc., or other electronic devices that can achieve the same or similar functions. This embodiment does not limit this.
[0065] It can be understood that the target object can be an object that needs to be classified and warned about the sedentary risk. The sedentary state can be the state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration. Among them, detecting the cumulative sitting duration of the target object in the sitting state can be to detect whether the target object is in the sitting state according to the height information of the target object, and count the cumulative sitting duration of the target object in the sitting state. Specifically, detecting whether the target object is in the sitting state according to the height information of the target object can be to determine that the target object is in the sitting state when the height information of the target object is always lower than the preset sitting height threshold within a preset time period; on the contrary, it is determined that the target object is not in the sitting state. Among them, the preset time period and the preset sitting height threshold can be set in advance, and the preset sitting height threshold can also be adjusted in real time according to the height of the chair when the target object sits down. This embodiment does not limit this;
[0066] Of course, in order to detect the sitting state more accurately, it is also possible to detect whether the target object is in the sitting state according to the position information and height information of the target object, and count the cumulative sitting duration of the target object in the sitting state. This embodiment does not limit this. Among them, the position information can be used to represent the location of the target object, and the height information can be used to represent the height value of the target object. Specifically, detecting whether the target object is in the sitting state according to the position information and height information can be to match the position information with the preset sitting position information, and match the height information with the preset sitting height information. When the position information matches the preset sitting position information successfully and the height information matches the preset sitting height information successfully, it is determined that the target object is in the sitting state; when the position information fails to match the preset sitting position information or the height information fails to match the preset sitting height information, it is determined that the target object is not in the sitting state. Among them, the preset sitting position information and the preset sitting height can be set in advance.
[0067] It can be understood that detecting whether the target object is in a sedentary state based on the cumulative sitting duration can be to determine that the target object is in a sedentary state when the cumulative sitting duration is greater than a preset duration; otherwise, it is determined that the target object is not in a sedentary state, where the preset duration can be set in advance.
[0068] Step S20: When it is detected that the target object is in a sedentary state, obtain the physical sign information and limb information of the target object.
[0069] It should be noted that the physical sign information includes but is not limited to information such as the target object's respiration and heart rate, and the limb information includes but is not limited to information such as limb position and limb speed. Obtaining the physical sign information and limb information of the target object can be to detect the target object, obtain the detection information of the target object, and obtain the physical sign information and limb information of the target object according to the detection information.
[0070] In a specific implementation, in order to more accurately obtain the physical sign information and limb information of the target object, the sedentary risk grading and warning device can be a radar detection device. The radar detection device has functions of data processing, network communication, program operation, and detection. The radar detection device obtains the physical sign information and limb information of the target object by transmitting a detection signal to the target object and receiving the radar echo signal reflected when the detection signal propagates to the target object, detects the physical sign stability degree of the target object according to the physical sign information, and detects the limb activity state of the target object according to the limb information, and determines the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state.
[0071] Of course, in order to implement sedentary risk warning, the radar detection device can be a radar base station integrated with a buzzer alarm. The radar base station has functions of data processing, network communication, program operation, and detection. The radar base station can determine the sedentary risk level of the target object according to the physical sign information and limb information of the target object, and the buzzer alarm can perform buzzer warning according to the sedentary risk level of the target object. The present invention can be applied to scenarios such as nursing homes for sedentary risk grading and warning. In this scenario, the buzzer alarm performing buzzer warning can prompt the nursing staff to conduct on-site verification and improve the nursing efficiency.
[0072] For ease of understanding, reference Figure 3 is made for illustration, but the present invention is not limited thereto. Figure 3 FIG. is a layout schematic diagram of the radar detection device according to an embodiment of the sedentary risk grading and warning method of the present invention. In the figure, the radar detection device is installed on the wall, and it is required that there is no occlusion and the field of view can effectively cover the indoor scene.
[0073] Step S30: Analyze the physical sign stability degree of the target object according to the physical sign information, and analyze the limb activity state of the target object according to the limb information.
[0074] It should be understood that detecting the physical sign stability degree of the target object according to the physical sign information can be to detect abnormal physical signs of the target object based on the physical sign information to obtain the physical sign stability degree of the target object. Among them, the abnormal physical sign detection includes but is not limited to respiratory rate detection, heart rate detection, apnea detection, heart rate abnormality detection, and heart rate attenuation detection, etc. In specific implementation, for example, the radar detection device can include a millimeter-wave radar, and the millimeter-wave radar can be used to detect physical sign information such as the respiration and heart rate of the target object. The specific applications include abnormal physical sign detections such as respiratory rate detection, heart rate detection, apnea detection, heart rate abnormality detection, and heart rate attenuation detection.
[0075] It can be understood that the limb activity state can include an active state and an inactive state. Detecting the limb activity state of the target object according to the limb information can be to determine that the limb activity state of the target object is the active state when there is a limb speed of the target object; and to determine that the limb activity state of the target object is the inactive state when there is no limb speed of the target object.
[0076] Step S40: Determine the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state.
[0077] It should be understood that determining the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state can be to use the physical sign stability degree and the limb activity state as risk reference information, look up the sedentary risk level corresponding to the risk reference information in the preset risk table, and use the sedentary risk level corresponding to the risk reference information as the sedentary risk level of the target object. Among them, the preset risk table includes the corresponding relationship between the risk reference information and the sedentary risk level, and the corresponding relationship between the risk reference information and the sedentary risk level can be set in advance.
[0078] Of course, in order to more accurately classify the sedentary risk of the target object, it is also possible to classify the sedentary risk of the target object according to whether the physical sign stability degree is stable and whether the limb activity state is the active state. This embodiment does not limit this.
[0079] Step S50: Generate an early warning strategy according to the sedentary risk level, and send an early warning message to the corresponding background device based on the early warning strategy, and / or control a preset alarm device to give an early warning based on the early warning strategy.
[0080] It should be understood that in order to perform sedentary risk early warning in a timely manner, in this embodiment, an early warning strategy is also generated according to the sedentary risk level, and an early warning message is sent to the corresponding background device based on the early warning strategy and / or a preset alarm device is controlled to give an early warning based on the early warning strategy.
[0081] It can be understood that, in order to facilitate the user to understand the sedentary risk level through the background device, in this embodiment, a warning message is also sent to the corresponding background device based on the warning strategy. After the user obtains the warning message through the background device, the user can make independent responses for different risk levels, thereby improving the care efficiency. Of course, in this embodiment, the preset alarm device can also be directly controlled based on the warning strategy to give a warning to remind the on-site staff to check, so as to improve the care efficiency. For example, the radar detection device can be a radar base station integrated with a buzzer alarm, and the radar detection device can control the buzzer alarm to give a buzzer warning based on the warning strategy. This embodiment does not limit this.
[0082] In this embodiment, the cumulative sitting duration of the target object in the sitting state is detected, and whether the target object is in a sedentary state is detected according to the cumulative sitting duration. Wherein, the sedentary state is the state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration. When it is detected that the target object is in a sedentary state, the physical sign information and limb information of the target object are obtained, the physical sign stability degree of the target object is detected according to the physical sign information, and the limb activity state of the target object is detected according to the limb information. The sedentary risk level of the target object is determined according to the physical sign stability degree and the limb activity state, a warning strategy is generated according to the sedentary risk level, and a warning message is sent to the corresponding background device based on the warning strategy and / or a preset alarm device is controlled to give a warning based on the warning strategy; Since this embodiment introduces limb speed and physical sign information for sedentary risk detection, more accurate sedentary detection can be realized, and then the risk level division of different sedentary actions can be realized, improving the accuracy of sedentary risk classification and warning. And because the present invention generates a corresponding warning strategy according to the sedentary risk level for warning, it is convenient for the user to make independent responses for different risk levels, and then the care efficiency can be improved.
[0083] Refer to Figure 4 , Figure 4 is a schematic flowchart of the second embodiment of the sedentary risk classification and warning method of the present invention. Based on the above Figure 2 shown in the first embodiment, the second embodiment of the sedentary risk classification and warning method of the present invention is proposed.
[0084] In the second embodiment, the step S20 includes:
[0085] Step S201: When it is detected that the target object is in a sedentary state, receive the echo signal corresponding to the detection signal, where the detection signal is emitted by a radar detection device, and the echo signal is a signal reflected when the detection signal propagates to the target object.
[0086] It should be understood that in this embodiment, the sedentary risk grading and warning device can be a radar detection device. The radar detection device detects whether the target object is in a sedentary state by continuously transmitting detection signals to the target object. Specifically, when it detects that the target object is in a sedentary state, it receives the echo signal generated by the reflection when the detection signal propagates to the target object to obtain the physical signs information and limb information of the target object; since the radar detection device detects and obtains the physical signs information and limb information of the target object by analyzing the changes in the echo signal reflected by the target object, it can obtain the physical signs information and limb information of the target object more accurately.
[0087] For ease of understanding, the following is an example, but it does not limit the present invention. In one example, the radar detection device can be a millimeter-wave radar. The millimeter-wave radar can be used to continuously transmit millimeter-wave signals with a preset frequency to the target object. Among them, the preset frequency can be set in advance. The transmitted millimeter-wave signals will propagate to the surface of the target object and be reflected by the surface of the target object. The millimeter-wave radar will receive the signals reflected back from the surface of the target object. The millimeter-wave radar obtains the physical signs information and limb information of the target object by analyzing the signals reflected back from the surface of the target object.
[0088] Step S202: Perform feature analysis on the echo signal, and obtain the physical signs information and limb information of the target object according to the analysis result.
[0089] It can be understood that performing feature analysis on the echo signal and obtaining the physical signs information and limb information of the target object according to the analysis result can be to compare and analyze the detection signal and the echo signal, obtain the signal change information of the echo signal compared with the detection signal, and obtain the physical signs information and limb information of the target object according to the signal change information.
[0090] In this embodiment, the physical signs information and limb information of the target object are detected and obtained by analyzing the changes in the echo signal reflected by the target object, so that the physical signs information and limb information of the target object can be obtained more accurately.
[0091] Further, the step S202 includes: comparing and analyzing the detection signal and the echo signal to obtain the signal change information of the echo signal compared with the detection signal, where the signal change information includes phase change information and / or frequency change information; capturing the chest movement information and limb movement information of the target object according to the signal change characteristics; obtaining the physical signs information of the target object according to the chest movement information, and obtaining the limb information of the target object according to the limb movement information.
[0092] It should be understood that, in order to further improve the accuracy of the physical sign information and limb information, in this embodiment, the received echo signal is analyzed, and the physical sign information and limb information of the target object are captured through the change information such as the phase and frequency of the echo signal compared with the detection signal. This is because the breathing and heart rate of the target object will cause the chest and heart movements (i.e., chest movements) of the target object, resulting in changes in the phase and frequency of the echo signal reflected back by the target object compared with the detection signal. Therefore, in this embodiment, the chest movement information of the target object can be captured according to the signal change characteristics, and the physical sign information of the target object can be obtained according to the chest movement information. Among them, the chest movement information includes, but is not limited to, information such as the chest movement amplitude and frequency of the target object, and the physical sign information includes, but is not limited to, information such as the breathing and heart rate of the target object; the limb movement of the target object will also cause changes in the phase and frequency of the echo signal reflected back by the target object compared with the detection signal. Therefore, in this embodiment, the limb movement information of the target object can also be captured according to the signal change characteristics, and the limb information of the target object can be obtained according to the limb movement information. Among them, the limb movement information includes, but is not limited to, information such as the limb movement direction and speed, and the limb information includes, but is not limited to, information such as the limb position and limb speed.
[0093] Refer to Figure 5 , Figure 5 FIG. is a schematic flowchart of the third embodiment of the sedentary risk classification and early warning method of the present invention. Based on the above embodiments, the third embodiment of the sedentary risk classification and early warning method of the present invention is proposed.
[0094] In the third embodiment, the step S10 includes:
[0095] Step S101: Obtain the position information and height information of the target object.
[0096] It should be understood that in this embodiment, whether the target object is in a sitting state is jointly detected by the maximum position fluctuation value of the target object within a preset time period and the number of target frames in which the height value of the target object is greater than the preset sitting height threshold within a preset time period, so as to further improve the accuracy of the sitting state detection, and further improve the accuracy of the sedentary risk detection.
[0097] It should be noted that the position information can be used to represent the location of the target object, and the height information can be used to represent the height value of the target object. Obtaining the position information and height information of the target object can be to detect the target object, obtain the detection information of the target object, and obtain the position information and height information of the target object according to the detection information.
[0098] Further, in order to more accurately obtain the position information and height information of the target object, step S101 includes: transmitting a detection signal to the target object through a radar detection device, and receiving the echo signal generated by reflection when the detection signal propagates to the target object; comparing the detection signal with the echo information, and obtaining the position information and height information of the target object according to the comparison result.
[0099] It can be understood that, in order to more accurately obtain the position information and height information of the target object, in this embodiment, the sedentary risk grading and warning device may be a radar detection device. The radar detection device has functions of data processing, network communication, program operation, and detection. The radar detection device obtains the position information and height information of the target object by transmitting a detection signal to the target object and receiving the radar echo signal generated by reflection when the detection signal propagates to the target object. Specifically, it may be to compare the detection signal with the echo information, and obtain the position information and height information of the target object according to the comparison result.
[0100] Step S102: Detect the maximum position fluctuation value of the target object within a preset time period according to the position information.
[0101] It should be understood that, in this embodiment, the maximum position fluctuation value of the target object within a preset time period is detected according to the position information. Among them, the preset time period can be set in advance, and the maximum position fluctuation value is used to measure whether the user moves significantly within the preset time period.
[0102] Step S103: Count the number of target frames in which the height value of the target object is greater than a preset sitting height threshold within the preset time period according to the height information.
[0103] It can be understood that the preset sitting height threshold can be set in advance, and the preset sitting height threshold can also be adjusted in real time according to the height of the chair when the target object sits down. This embodiment does not limit this. The number of target frames is used to measure whether the user stands for a long time within the preset time period.
[0104] Step S104: When the maximum position fluctuation value is less than a preset position fluctuation threshold and the number of target frames is less than a preset number of frames, it is determined that the target object is in a sitting state.
[0105] It should be understood that when the maximum position fluctuation value is less than a preset position fluctuation threshold and the number of target frames is less than a preset number of frames, it means that the user does not move significantly and does not stand for a long time within the preset time period. Therefore, it can be determined that the target object is in a sitting state.
[0106] When the maximum position fluctuation value is greater than or equal to the preset position fluctuation threshold, or the target frame number is greater than or equal to the preset frame number, it indicates that the user has made a large movement or stood for a long time within the preset time period. Therefore, it can be determined that the target object is not in a sitting state.
[0107] Step S105: Statistically calculate the cumulative sitting duration of the target object in the sitting state, and detect whether the target object is in a sedentary state based on the cumulative sitting duration.
[0108] It can be understood that detecting whether the target object is in a sedentary state based on the cumulative sitting duration can be that when the cumulative sitting duration is greater than the preset duration, it is determined that the target object is in a sedentary state; otherwise, it is determined that the target object is not in a sedentary state, where the preset duration can be set in advance.
[0109] In this embodiment, the maximum position fluctuation value of the target object within the preset time period and the target frame number of the height value of the target object within the preset time period that is greater than the preset sitting height threshold are jointly used to detect whether the target object is in a sitting state, thereby further improving the accuracy of sitting state detection and then improving the accuracy of sedentary risk detection.
[0110] In the third embodiment, the step S30 includes:
[0111] Step S301: Match the physical sign information with the abnormal physical sign information, and analyze the physical sign stability degree of the target object based on whether the physical sign information matches the abnormal physical sign information.
[0112] It should be understood that in order to more accurately judge the physical sign stability degree and limb activity state of the target object, in this embodiment, by matching the physical sign information with the abnormal physical sign information and judging whether the limb speed is greater than the preset speed threshold, the physical sign stability degree and limb activity state of the target object are respectively judged.
[0113] It should be noted that the abnormal physical sign information can be the physical sign information of the target object when in an abnormal state. For example, the physical sign information of the target object when in an abnormal state such as abnormal breathing, abnormal heartbeat, etc.
[0114] It can be understood that when the physical sign information matches the abnormal physical sign information successfully, it indicates that the target object is in an abnormal state. Therefore, it can be determined that the physical sign stability degree of the target object is unstable; when the physical sign information fails to match the abnormal physical sign information, it indicates that the target object is in a normal state. Therefore, it can be determined that the physical sign stability degree of the target object is stable.
[0115] Step S302: Obtain the limb speed of the target object from the limb information, and analyze the limb activity state of the target object based on whether the limb speed is greater than the preset speed threshold.
[0116] It should be noted that the preset speed threshold can be set in advance. For example, in this embodiment, in order to determine whether the limb is moving, the preset speed threshold can be set to 0.
[0117] It can be understood that when the limb speed is greater than the preset speed threshold, it indicates that the limb of the target object has moved. Therefore, it can be determined that the limb activity state of the target object is an active state; when the limb speed is less than or equal to the preset speed threshold, it indicates that the limb of the target object has not moved. Therefore, it can be determined that the limb activity state of the target object is an inactive state.
[0118] In this embodiment, by matching the physical sign information with the abnormal physical sign information and determining whether the limb speed is greater than the preset speed threshold, the stability degree of the physical signs and the limb activity state of the target object are respectively determined, so that the stability degree of the physical signs and the limb activity state of the target object can be analyzed more accurately.
[0119] Refer to Figure 6 , Figure 6 FIG. is a schematic flowchart of the fourth embodiment of the sedentary risk classification and early warning method of the present invention. Based on the above embodiments, the fourth embodiment of the sedentary risk classification and early warning method of the present invention is proposed.
[0120] In the fourth embodiment, step S40 includes:
[0121] Step S401: When the stability degree of the physical signs is unstable, determine that the sedentary risk level of the target object is the first risk level.
[0122] It should be understood that in order to more accurately classify the sedentary risk of the target object, in this embodiment, the sedentary risk of the target object is classified according to whether the stability degree of the physical signs is stable and whether the limb activity state is an active state.
[0123] It can be understood that when the stability degree of the physical signs is unstable (such as abnormal breathing, abnormal heartbeat, etc.), it indicates that the sedentary risk of the target object is high. Therefore, it is necessary to determine that the sedentary risk level of the target object is the first risk level, that is, to determine that the sedentary risk level of the target object is a high risk level.
[0124] Step S401': When the stability degree of the physical signs is stable and the limb activity state is an inactive state, determine that the sedentary risk level of the target object is the second risk level, and the second risk level is lower than the first risk level.
[0125] It should be understood that when the physical sign stability is stable and the limb activity state is inactive (for example, normal breathing, normal heartbeat, but the limbs are inactive for a long time), it indicates that the sedentary risk of the target object is medium. Therefore, it is necessary to determine that the sedentary risk level of the target object is the second risk level, that is, to determine that the sedentary risk level of the target object is the medium risk level.
[0126] Step S401": When the physical sign stability is stable and the limb activity state is active, determine that the sedentary risk level of the target object is the third risk level, and the third risk level is lower than the second risk level.
[0127] It can be understood that when the physical sign stability is stable and the limb activity state is active (for example, normal breathing, normal heartbeat, and the limbs have activities for a short time), it indicates that the sedentary risk of the target object is low. Therefore, it is necessary to determine that the sedentary risk level of the target object is the third risk level, that is, to determine that the sedentary risk level of the target object is the low risk level.
[0128] In this embodiment, the sedentary risk of the target object is classified according to whether the physical sign stability is stable and whether the limb activity state is active, so that the sedentary risk of the target object can be classified more accurately.
[0129] For the sake of easy understanding, reference is made to Figure 7 for illustration, but it does not limit the present invention. Figure 7 It is a schematic diagram of a radar detection device module according to an embodiment of the sedentary risk classification and warning method of the present invention. In the figure, the radar detection device includes a target detection module, a state judgment module, a physical sign detection module, and a warning module. Among them, the target detection module processes the echo signal of the target object in real time and detects the position information, height information, limb information (such as limb speed), etc. of the target object.
[0130] For the sake of easy understanding, reference is made to Figure 8 for illustration, but it does not limit the present invention. Figure 8 It is a schematic logic diagram of a state detection module according to an embodiment of the sedentary risk classification and warning method of the present invention. In the figure, the state detection module is used to judge whether the target object is sitting down. For the maximum position fluctuation value (i.e., max_pos_mv in the figure) within a preset time period is less than the preset position fluctuation threshold (i.e., mv_thr in the figure), and the number of target frames (i.e., height_cnt in the figure) with a height value greater than the preset sitting height threshold is less than the preset number of frames (i.e., heithr_thr in the figure), it is determined that the target object is in a sitting state, and the cumulative sitting duration of the target object in the sitting state is counted; otherwise, it is determined that the target object is not in a sitting state.
[0131] For the sake of easy understanding, reference is made toFigure 9 This is for illustration purposes only and does not limit the present invention. Figure 9 FIG. is a schematic logic diagram of a physical sign detection module according to an embodiment of the sedentary risk classification and early warning method of the present invention. In the figure, the physical sign detection module is used to detect the physical sign information of the sitting target, providing data support for classifying the sedentary state. Among them, sdntry_state represents the sedentary state, which is the state when the cumulative sitting duration of the target object in the sitting state is greater than the preset duration.
[0132] For the sake of easy understanding, reference is made to Figure 10 This is for illustration purposes only and does not limit the present invention. Figure 10 FIG. is a schematic logic diagram of an alarm module according to an embodiment of the sedentary risk classification and early warning method of the present invention. In the figure, the early warning module is used to determine the risk level of the sedentary target. For a target object with stable physical signs and an active limb activity state, its sedentary risk level is determined to be a low risk; for a target object with stable physical signs and an inactive limb activity state, its sedentary risk level is determined to be a medium risk; for a target object with unstable physical signs (such as respiratory disorders, heartbeat signal disorders, etc.), its sedentary risk level is determined to be a high risk.
[0133] In order to facilitate the user to understand the sedentary risk level through the background device, in this embodiment, an early warning message is also sent to the corresponding background device based on the early warning strategy. After the user obtains the early warning message through the background device, they can make independent responses for different risk levels, improving the nursing efficiency. Of course, in this embodiment, the preset alarm device can also be directly controlled based on the early warning strategy for early warning to remind the on-site staff to check, improving the nursing efficiency.
[0134] In addition, referring to Figure 11 , an embodiment of the present invention also provides a sedentary risk classification and early warning device, which includes:
[0135] A detection module 10, configured to detect the cumulative sitting duration of the target object in the sitting state, and detect whether the target object is in a sedentary state according to the cumulative sitting duration, where the sedentary state is the state when the cumulative sitting duration of the target object in the sitting state is greater than the preset duration;
[0136] An acquisition module 20, configured to acquire the physical sign information and limb information of the target object when it is detected that the target object is in a sedentary state;
[0137] An analysis module 30, configured to analyze the stability degree of the physical signs of the target object according to the physical sign information, and analyze the limb activity state of the target object according to the limb information;
[0138] A determination module 40, configured to determine the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state;
[0139] An early warning module 50, configured to generate an early warning strategy according to the sedentary risk level, and send an early warning message to a corresponding background device based on the early warning strategy, and / or control a preset alarm device to give an early warning based on the early warning strategy.
[0140] In this embodiment, it discloses detecting the cumulative sitting duration of the target object in the sitting state, and detecting whether the target object is in a sedentary state according to the cumulative sitting duration. Wherein, the sedentary state is a state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration. When it is detected that the target object is in a sedentary state, the physical sign information and limb information of the target object are obtained, the physical sign stability degree of the target object is detected according to the physical sign information, and the limb activity state of the target object is detected according to the limb information. The sedentary risk level of the target object is determined according to the physical sign stability degree and the limb activity state, an early warning strategy is generated according to the sedentary risk level, an early warning message is sent to a corresponding background device based on the early warning strategy, and / or a preset alarm device is controlled to give an early warning based on the early warning strategy; Since this embodiment introduces limb speed and physical sign information for sedentary risk detection, it can achieve more accurate sedentary detection, and then can achieve the risk level classification of different sedentary actions, improve the accuracy of sedentary risk classification and early warning, and because the present invention generates a corresponding early warning strategy according to the sedentary risk level for early warning, it can facilitate users to make independent responses to different risk levels, and then can improve the nursing efficiency.
[0141] In one embodiment, the obtaining module 20 is further configured to, when it is detected that the target object is in a sedentary state, receive an echo signal corresponding to the detection signal, where the detection signal is emitted by a radar detection device, and the echo signal is a signal generated by reflection when the detection signal propagates to the target object; perform feature analysis on the echo signal, and obtain the physical sign information and limb information of the target object according to the analysis result.
[0142] In one embodiment, the obtaining module 20 is further configured to perform a comparative analysis on the detection signal and the echo signal to obtain signal change information of the echo signal compared with the detection signal, where the signal change information includes phase change information and / or frequency change information; capture the chest movement information and limb movement information of the target object according to the signal change characteristics; obtain the physical sign information of the target object according to the chest movement information, and obtain the limb information of the target object according to the limb movement information.
[0143] In one embodiment, the analysis module 30 is further configured to match the physical sign information with abnormal physical sign information, and analyze the physical sign stability degree of the target object according to whether the physical sign information matches the abnormal physical sign information; obtain the limb speed of the target object from the limb information, and analyze the limb activity state of the target object according to whether the limb speed is greater than a preset speed threshold.
[0144] In one embodiment, the determination module 40 is further configured to, when the physical sign stability degree is unstable, determine that the sedentary risk level of the target object is the first risk level; when the physical sign stability degree is stable and the limb activity state is an inactive state, determine that the sedentary risk level of the target object is the second risk level, and the second risk level is lower than the first risk level; when the physical sign stability degree is stable and the limb activity state is an active state, determine that the sedentary risk level of the target object is the third risk level, and the third risk level is lower than the second risk level.
[0145] In one embodiment, the detection module 10 is further configured to obtain the position information and height information of the target object; detect the maximum position fluctuation value of the target object within a preset time period according to the position information; count the number of target frames in which the height value of the target object is greater than a preset sitting height threshold within the preset time period according to the height information; when the maximum position fluctuation value is less than a preset position fluctuation threshold and the number of target frames is less than a preset number of frames, determine that the target object is in a sitting state; count the cumulative sitting duration of the target object in the sitting state, and detect whether the target object is in a sedentary state according to the cumulative sitting duration.
[0146] In one embodiment, the detection module 10 is further configured to transmit a detection signal to the target object through a radar detection device, and receive an echo signal generated by reflection when the detection signal propagates to the target object; compare the detection signal with the echo information, and obtain the position information and height information of the target object according to the comparison result.
[0147] Other embodiments or specific implementation manners of the sedentary risk classification and early warning device of the present invention may refer to the above method embodiments, and will not be described in detail here.
[0148] In addition, an embodiment of the present invention further provides a storage medium, on which a sedentary risk classification and early warning program is stored. When the sedentary risk classification and early warning program is executed by a processor, the sedentary risk classification and early warning method as described above is implemented.
[0149] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or system including that element.
[0150] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.
[0151] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a Read Only Memory image (ROM) / Random Access Memory (RAM), magnetic disk, optical disk), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present invention.
[0152] The above are only the preferred embodiments of the present invention, and do not limit the scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the protection scope of the present invention.
Claims
1. A sedentary risk classification and early warning method, characterized in that, The sedentary risk grading and warning method includes: Detecting the cumulative sitting duration of the target object in the sitting state, and detecting whether the target object is in a sedentary state according to the cumulative sitting duration, where the sedentary state is the state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration; When it is detected that the target object is in a sedentary state, obtaining the physical sign information and limb information of the target object; Analyzing the physical sign stability degree of the target object according to the physical sign information, and analyzing the limb activity state of the target object according to the limb information; Determining the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state; Generating a warning strategy according to the sedentary risk level, and sending a warning message to the corresponding background device based on the warning strategy, and / or warning based on the warning strategy by controlling a preset warning device.
2. The sedentary risk classification and early warning method according to claim 1, wherein The obtaining the physical sign information and limb information of the target object when it is detected that the target object is in a sedentary state includes: When it is detected that the target object is in a sedentary state, receiving an echo signal corresponding to a detection signal, where the detection signal is emitted by a radar detection device, and the echo signal is a signal reflected when the detection signal propagates to the target object; Performing feature analysis on the echo signal, and obtaining the physical sign information and limb information of the target object according to the analysis result.
3. The sedentary risk classification and early warning method according to claim 2, wherein The performing feature analysis on the echo signal, and obtaining the physical sign information and limb information of the target object according to the analysis result includes: Performing comparative analysis on the detection signal and the echo signal to obtain signal change information of the echo signal compared with the detection signal, where the signal change information includes phase change information and / or frequency change information; Capturing the chest movement information and limb movement information of the target object according to the signal change characteristics; Obtaining the physical sign information of the target object according to the chest movement information, and obtaining the limb information of the target object according to the limb movement information.
4. The sedentary risk classification and early warning method according to any one of claims 1 to 3, characterized in that, The analyzing the physical sign stability degree of the target object according to the physical sign information, and analyzing the limb activity state of the target object according to the limb information includes: Matching the physical sign information with abnormal physical sign information, and analyzing the physical sign stability degree of the target object according to whether the physical sign information matches the abnormal physical sign information; Obtaining the limb speed of the target object from the limb information, and analyzing the limb activity state of the target object according to whether the limb speed is greater than a preset speed threshold.
5. The sedentary risk classification and warning method according to any one of claims 1 to 3, characterized in that The determining the sedentary risk level of the target object according to the physical sign stability degree and the limb activity state includes: When the physical sign stability degree is unstable, determining that the sedentary risk level of the target object is the first risk level; When the physical sign stability degree is stable and the limb activity state is an inactive state, determining that the sedentary risk level of the target object is the second risk level, and the second risk level is lower than the first risk level; When the physical sign stability level is stable and the limb activity state is an active state, it is determined that the sedentary risk level of the target object is the third risk level, and the third risk level is lower than the second risk level.
6. The sedentary risk classification and early warning method according to any one of claims 1 to 3, characterized in that, The detecting the cumulative sitting duration of the target object in the sitting state and detecting whether the target object is in a sedentary state according to the cumulative sitting duration includes: Obtaining the position information and height information of the target object; Detecting the maximum position fluctuation value of the target object within a preset time period according to the position information; Counting the number of target frames in which the height value of the target object is greater than a preset sitting height threshold within the preset time period according to the height information; When the maximum position fluctuation value is less than a preset position fluctuation threshold and the number of target frames is less than a preset number of frames, it is determined that the target object is in the sitting state; Counting the cumulative sitting duration of the target object in the sitting state and detecting whether the target object is in a sedentary state according to the cumulative sitting duration.
7. The sedentary risk classification and early warning method according to claim 6, characterized in that, The obtaining the position information and height information of the target object includes: Transmitting a detection signal to the target object through a radar detection device and receiving an echo signal generated by reflection when the detection signal propagates to the target object; Comparing the detection signal with the echo information and obtaining the position information and height information of the target object according to the comparison result.
8. A sedentary risk classification and early warning device, characterized in that, The sedentary risk classification and early warning device includes: A detection module, configured to detect the cumulative sitting duration of the target object in the sitting state and detect whether the target object is in a sedentary state according to the cumulative sitting duration, where the sedentary state is a state when the cumulative sitting duration of the target object in the sitting state is greater than a preset duration; An obtaining module, configured to obtain the physical sign information and limb information of the target object when it is detected that the target object is in a sedentary state; An analysis module, configured to analyze the physical sign stability level of the target object according to the physical sign information and analyze the limb activity state of the target object according to the limb information; A determination module, configured to determine the sedentary risk level of the target object according to the physical sign stability level and the limb activity state; An early warning module, configured to generate an early warning strategy according to the sedentary risk level, send an early warning message to a corresponding background device based on the early warning strategy, and / or control a preset alarm device to give an early warning based on the early warning strategy.
9. A sedentary risk classification and early warning device, characterized in that, The sedentary risk classification and early warning device includes: a memory, a processor, and a sedentary risk classification and early warning program stored on the memory and executable on the processor. When the sedentary risk classification and early warning program is executed by the processor, it implements the sedentary risk classification and early warning method according to any one of claims 1 to 7.
10. A storage medium, characterized in that, A sedentary risk classification and early warning program is stored on the storage medium. When the sedentary risk classification and early warning program is executed by a processor, it implements the sedentary risk classification and early warning method according to any one of claims 1 to 7.