Human body posture recognition method and system based on flexible sensor

Abstract

The invention discloses a human body posture recognition method and system based on a flexible sensor, and belongs to the field of human body posture recognition. User postures are pre-estimated and analyzed based on pressure data of all points of corresponding postures, and pressure abnormal conditions of all points of the corresponding postures are obtained corresponding to user posture changes; according to the pressure abnormal condition of each point corresponding to the posture and the set parameters of the corresponding user, the bearing pressure of each point corresponding to the posture is distributed and adjusted, so that the corresponding point is adjusted to the position corresponding to the bearing pressure, and the aspects of pressure monitoring, data processing and supporting adjustment are remarkably improved. The pressure distribution of all parts of the human body can be monitored and adjusted more accurately, the comfort level and sleep quality of the user are remarkably improved, and especially when the user turns over and changes sleeping postures, the system can make a quick response, and it is ensured that the optimal supporting effect is provided under any condition.

Description

Technical Field

[0001] The present invention belongs to the field of human body posture recognition, and in particular relates to a human body posture recognition method and system based on a flexible sensor. Background Art

[0002] With improved living standards and a growing awareness of health, sleep quality is receiving increasing attention. High-quality sleep is not only crucial for physical health but also directly impacts daily life and work efficiency. However, traditional mattress designs typically feature a fixed firmness, making it difficult to dynamically adjust to the pressure of different parts of the body. This can lead to discomfort during extended use, especially in critical areas like the waist, shoulders, and neck, where support is often insufficient or pressure is excessive.

[0003] While some smart mattresses currently on the market attempt to address this issue through the use of sensors and mechanical adjustment mechanisms, they still suffer from significant deficiencies in accuracy, real-time response, and dynamic adjustment. For example, the sensors of some smart mattresses are not highly accurate and cannot accurately detect subtle changes in human pressure. Other systems have slow response times and are unable to make timely adjustments, resulting in the mattress failing to quickly adapt to the new pressure distribution when the user turns over or changes posture during the night, thus affecting the sleep experience. Furthermore, existing smart mattresses may experience stability issues over long periods of operation, such as sensor aging, data processing delays, and wear and tear of mechanical components. These issues can reduce system reliability and user experience. To address the technical issues raised in this background technology, this application designs a human posture recognition method and system based on flexible sensors. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention proposes a human posture recognition method and system based on flexible sensors, which estimates and analyzes the user's posture based on the pressure data of each point corresponding to the posture, obtains the pressure anomaly of each point corresponding to the posture according to the change of the user's posture, and distributes and adjusts the pressure of each point corresponding to the posture according to the pressure anomaly of each point corresponding to the posture and the set parameters of the corresponding user, so that the corresponding points are adjusted to the corresponding pressure-bearing positions, achieving significant improvements in pressure monitoring, data processing and support adjustment. Compared with existing smart mattresses, it can more accurately monitor and adjust the pressure distribution of various parts of the human body, significantly improving the user's comfort and sleep quality. In particular, when the user turns over or changes sleeping position, the system can respond quickly to ensure the best support effect under any circumstances.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, a human body posture recognition method based on a flexible sensor is provided, which comprises the following specific steps: Step 1: Obtain pressure data of each point corresponding to the posture through the pressure sensing module, and simultaneously obtain the setting parameters of the corresponding user; Step 2: Estimating and analyzing the user's posture based on the pressure data of each point corresponding to the posture; Step 3: Obtain abnormal pressure conditions at each point corresponding to the user's posture changes; Step 4: According to the abnormal pressure conditions of the points corresponding to the posture and the set parameters of the corresponding user, the pressure of the points corresponding to the posture is distributed and adjusted, so that the corresponding points are adjusted to the positions corresponding to the pressure.

[0006] As a preferred technical solution for the flexible sensor-based human posture recognition method, the specific content of obtaining pressure data for each point corresponding to the posture through the pressure sensing module in step 1 is: obtaining pressure data for each pressure point of the user's posture based on an array of pressure sensors distributed on the mattress; the specific content of obtaining the corresponding user's set parameters in step 1 is: obtaining the patient level of the patient's body part and other body parts requiring protection data set by the user.

[0007] As a preferred technical solution for the human posture recognition method based on flexible sensors, the step 2 of estimating and analyzing the user posture based on the pressure data of each point corresponding to the posture includes the following specific steps: S21, acquiring image data of various parts of the user, and simultaneously acquiring pressure data of various points corresponding to the posture, acquiring contours of the component parts based on the corresponding ratios of the pressure data of various points corresponding to the posture, and acquiring posture data of each component part using the contours of the component parts and the image data of the corresponding angles of each part of the user; The steps of obtaining the contour of the component parts based on the corresponding ratio of the pressure data of each point in the corresponding posture include the following specific steps: S211, obtaining pressure data for each point corresponding to the posture, obtaining corresponding ratio data of the pressure at each point in the posture, importing the corresponding ratio of the pressure into the three-dimensional construction model to construct a length image of the corresponding ratio of the corresponding point, and then fusing the length images of all the points in the posture into a posture contour according to the corresponding position; S212: Acquire three-dimensional images of the posture contours of the corresponding regions, simultaneously acquire three-dimensional image data of various parts of the user, obtain three-dimensional images of the three-dimensional image data of various parts of the user in various angles of the in-bed posture, import the three-dimensional images of the posture contours of the various regions of the user and the three-dimensional images of the three-dimensional image data of various parts of the user in various angles into a similarity calculation formula, calculate the similarity between the three-dimensional images of the posture contours of the various regions of the user and the three-dimensional images of the three-dimensional image data of various parts of the user in various angles of the in-bed posture, obtain the corresponding angle of the corresponding part of the user with the greatest similarity to the posture contours of the various regions, and set it as the posture data of each component part. This step can also be achieved by directly inputting the pressure cloud map and using the convolution layer to automatically learn local features for classification. Through multi-layer convolution and pooling operations, the model can capture the spatial structure and local changes in the pressure map; S22: Combining the posture data of each component part to obtain an overall posture image of the user.

[0008] As a preferred technical solution of the human posture recognition method based on flexible sensors, obtaining the abnormal pressure conditions of each point corresponding to the posture corresponding to the user's posture changes includes the following specific steps: S31, obtaining all overall posture images of the user during this rest period, and obtaining pressure data of each part at each time; S32. Import the pressure data of each part at each time into the pressure abnormal value analysis formula to calculate the pressure abnormal value of each pressure part, wherein the pressure abnormal value calculation formula of the zth part is: , where T is the compression time of the zth part, dt is the time integral, mzt is the compression pressure data of the zth part at time t, mz is the maximum value of the safety range of the compression pressure of the corresponding zth part, and Tm is the compression time safety value.

[0009] As a preferred technical solution of the human posture recognition method based on flexible sensors, the method of distributing and adjusting the pressure of each point corresponding to the posture according to the abnormal pressure conditions of each point corresponding to the posture and the set parameters of the corresponding user includes the following specific steps: Obtain the corresponding pressure abnormal value of each part and the corresponding user setting parameters and substitute them into the pressure distribution coefficient calculation formula of each point to calculate the pressure distribution coefficient. Among them, the pressure distribution coefficient calculation formula of the zth part is: , where P is the number of compressed parts, vz is the patient level of the zth compressed part, For the level weight, multiply the body weight by the pressure distribution coefficient of the corresponding part to obtain the pressure that the corresponding part needs to bear, and adjust the height of the corresponding part so that the corresponding point is adjusted to the position that bears the corresponding pressure.

[0010] In a second aspect, a human posture recognition system based on a flexible sensor is provided, which is implemented based on the above-mentioned human posture recognition method based on a flexible sensor, and specifically includes an information acquisition module, a posture estimation and analysis module, a pressure anomaly analysis module, and a pressure distribution and adjustment module; The information acquisition module is used to obtain pressure data of each point corresponding to the posture through the pressure sensing module, and simultaneously obtain the setting parameters of the corresponding user; The posture estimation and analysis module estimates and analyzes the user's posture based on the pressure data of each point corresponding to the posture; The pressure anomaly analysis module obtains the pressure anomaly of each point corresponding to the posture change according to the user's posture change; The pressure distribution and adjustment module is used to distribute and adjust the pressure borne by each point corresponding to the posture according to the abnormal pressure conditions of each point corresponding to the posture and the set parameters of the corresponding user, so that the corresponding point is adjusted to the position corresponding to the pressure borne.

[0011] According to a third aspect, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned human posture recognition method based on flexible sensors by calling the computer program stored in the memory.

[0012] In a fourth aspect, a computer-readable storage medium is provided, which stores instructions. When the instructions are executed on a computer, the computer executes the human posture recognition method based on the flexible sensor as described above.

[0013] Compared with the prior art, the present invention has the following advantages: This application estimates and analyzes the user's posture based on the pressure data of each point corresponding to the posture, obtains the pressure anomaly of each point corresponding to the posture according to the changes in the user's posture, and distributes and adjusts the pressure of each point corresponding to the posture according to the pressure anomaly of each point corresponding to the posture and the set parameters of the corresponding user, so that the corresponding points are adjusted to the corresponding pressure-bearing positions, achieving significant improvements in pressure monitoring, data processing and support adjustment. Compared with existing smart mattresses, it can more accurately monitor and adjust the pressure distribution of various parts of the human body, significantly improving the user's comfort and sleep quality. In particular, when the user turns over or changes sleeping position, the system can respond quickly to ensure the best support effect in any situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1Schematic diagram of the overall process of the human body posture recognition method based on flexible sensors of the present invention; Figure 2 Schematic diagram of the specific process of step three of the human posture recognition method based on flexible sensors of the present invention; Figure 3 Schematic diagram of the overall framework of the human posture recognition system based on flexible sensors of the present invention; Figure 4 Schematic diagram of a bed applicable to the present invention. DETAILED DESCRIPTION

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the embodiments of the specification.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0017] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0018] Example 1 In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 2 As shown, this embodiment provides a human posture recognition method based on a flexible sensor, which includes the following specific steps: Step 1: Figure 4 A pressure sensing module is installed on the bed body shown, and the pressure data of each point corresponding to the posture is obtained through the pressure sensing module, and the setting parameters of the corresponding user are obtained at the same time; The mattress body includes: Flexible pressure sensor: Made of new flexible materials, the sensor has high sensitivity and high resolution, capable of accurately monitoring the pressure of different parts of the human body in real time. The sensor is embedded in the mattress surface and seamlessly integrates with the mattress to ensure the overall comfort and aesthetics of the mattress; Data processing unit: This unit receives and processes data from the flexible pressure sensor, uses advanced algorithms to analyze the pressure distribution of the human body, and quickly calculates the support strength and position that need to be adjusted in each area. The data processing unit also has a self-learning function that can adjust according to the user's long-term usage habits to optimize the personalized sleep experience; Control algorithm and communication module: The system generates adjustment instructions through complex data analysis and exchanges data with sensors and actuators using wired or wireless methods. The control algorithm dynamically adjusts the support modules and partition control system inside the mattress based on real-time pressure data to ensure that the mattress adapts to the user's needs at any time; Mechanical structure and airbag structure: By combining the mechanical structure and airbag design, the mattress can precisely adjust the position and strength of the internal support unit to achieve dynamic adjustment of hardness. The airbag structure can work independently in different areas to provide optimal support for the user's body shape and sleeping position. Independent support module and partition control: Each support unit of the mattress is independently controlled, combined with partition control to achieve more precise pressure distribution adjustment; In this embodiment, the specific content of obtaining pressure data corresponding to each point of the user's posture through the pressure sensing module in step 1 is: obtaining pressure data related to each pressure point of the user's posture based on the array of pressure sensors distributed on the mattress; the specific content of obtaining the corresponding user's set parameters in step 1 is: obtaining the patient level set by the user, such as the patient's diseased area or the area requiring protection, which is set by the user according to his or her own disease condition, for example, categorized from 1 to 5, with 5 being the most severe; Step 2: Estimating and analyzing the user's posture based on the pressure data of each point corresponding to the posture; In this embodiment, the step 2 of estimating and analyzing the user's posture based on the pressure data of each point corresponding to the posture includes the following specific steps: S21, acquiring image data of various parts of the user, and simultaneously acquiring pressure data of various points corresponding to the posture, acquiring contours of the component parts based on the corresponding ratios of the pressure data of various points corresponding to the posture, and acquiring posture data of each component part using the contours of the component parts and the image data of the corresponding angles of each part of the user; The steps of obtaining the contour of the component parts based on the corresponding ratio of the pressure data of each point in the corresponding posture include the following specific steps: S211, obtaining pressure data for each point corresponding to the posture, obtaining corresponding ratio data of the pressure at each point in the posture, importing the corresponding ratio of the pressure into the three-dimensional construction model to construct a length image of the corresponding ratio of the corresponding point, and then fusing the length images of all the points in the posture into a posture contour according to the corresponding position; S212: Acquire a three-dimensional image of the posture contour of each corresponding region, simultaneously acquire three-dimensional image data of each part of the user, acquire three-dimensional images of the three-dimensional image data of each part of the user at various angles of the user's posture in bed, introduce the three-dimensional image of the posture contour of each region of the user and the three-dimensional image data of each part of the user at various angles of the user's posture in bed into a similarity calculation formula, calculate the similarity between the three-dimensional image of the posture contour of each region of the user and the three-dimensional image data of each part of the user at various angles of the user's posture in bed, obtain the corresponding angle of the corresponding part of the user with the greatest similarity to the posture contour of each region, and set it as the posture data of each component part. The similarity calculation formula between the three-dimensional image of the posture contour of the kth region of the user and the three-dimensional image of the three-dimensional image data of the mth part of the user at angle a of the user's posture in bed can be: , where nk is the number of points in the three-dimensional image of the posture contour of the k-th region, xi is the proportional length of the i-th point in the three-dimensional image of the posture contour of the k-th region, fma is the number of points in the three-dimensional image data of the user's m-part in bed posture a angle, yj is the proportional length of the j-th point in the three-dimensional image data of the user's m-part in bed posture a angle. This step can also be achieved by directly inputting the pressure cloud map and using the convolution layer to automatically learn local features for classification. Through multi-layer convolution and pooling operations, the model can capture the spatial structure and local changes in the pressure map. S22, combining the posture data of each component part to obtain an overall posture image of the user; In this embodiment, it is necessary to point out the second implementation method for obtaining the posture data of the component parts: S23, data preprocessing; pressure cloud normalization: normalize the pressure data to eliminate individual differences between different sensors and ensure data consistency. Noise filtering: remove noise from the data through filtering algorithms, commonly used methods include Gaussian filtering and median filtering. Data standardization: adjust the pressure cloud to a fixed size for subsequent processing; S24. Feature Extraction and Region Segmentation: Divide the pressure cloud map into multiple regions or grids corresponding to different parts of the human body. Threshold segmentation, edge detection, or clustering algorithms (such as K-means and DBSCAN) can be used to identify high-pressure areas. Shape Feature Extraction: Extract shape features from the pressure cloud map, such as area, aspect ratio, boundary contours, and pressure peaks. These features help identify different parts of the human body (such as the head, shoulders, and hips). Heat Map Generation: Convert the pressure cloud map into a heat map or grayscale image, making high-pressure areas more prominent and easier to identify. S25. Part recognition; Machine learning algorithm: Use supervised learning methods (such as support vector machines, decision trees, random forests, etc.) or deep learning methods (such as convolutional neural networks (CNN)) to train the extracted features to distinguish different body parts. CNN model: The pressure cloud map can be directly input, and the convolution layer can be used to automatically learn local features for classification. Through multi-layer convolution and pooling operations, the model can capture the spatial structure and local changes in the pressure map; it needs to be trained with labeled data (for example, a known lying posture data set with areas of different human body parts marked), and each pressure cloud map is matched with its corresponding body part label; post-processing of the results; multi-scale fusion: considering that the pressure distribution of different parts of the human body may span multiple scales, multi-scale fusion technology is needed to ensure the recognition accuracy of different areas; Step 3: Obtain abnormal pressure conditions at each point corresponding to the user's posture changes; In this embodiment, obtaining the abnormal pressure conditions at each point corresponding to the user's posture changes according to the user's posture changes includes the following specific steps: S31, obtaining all overall posture images of the user during this rest period, and obtaining pressure data of each part at each time; S32. Import the pressure data of each part at each time into the pressure abnormal value analysis formula to calculate the pressure abnormal value of each pressure part, wherein the pressure abnormal value calculation formula of the zth part is: , where T is the compression time of the z-th part, dt is the time integral, mzt is the compression pressure data of the z-th part at time t, mz is the maximum value of the safety range of the compression pressure of the corresponding z-th part, and Tm is the compression time safety value; Step 4: Distribute and adjust the pressure on each point corresponding to the posture according to the abnormal pressure conditions at each point corresponding to the posture and the set parameters of the corresponding user, so that the corresponding point is adjusted to the position corresponding to the pressure; In this embodiment, distributing and adjusting the pressure on each point corresponding to the posture according to the abnormal pressure conditions at each point corresponding to the posture and the set parameters of the corresponding user includes the following specific steps: Obtain the corresponding pressure abnormal value of each part and the corresponding user setting parameters and substitute them into the pressure distribution coefficient calculation formula of each point to calculate the pressure distribution coefficient. Among them, the pressure distribution coefficient calculation formula of the zth part is: , where P is the number of compressed parts, vz is the patient level of the zth compressed part, For the level weight, multiply the body weight by the pressure distribution coefficient of the corresponding part to obtain the pressure that the corresponding part needs to bear, and adjust the height of the corresponding part so that the corresponding point is adjusted to the corresponding pressure-bearing position. As for how to adjust the height of the corresponding part so that the corresponding point is adjusted to the corresponding pressure-bearing position, in order to adjust the height of the corresponding part of the mattress to reach the corresponding pressure-bearing position, the system needs to adopt a sophisticated feedback control mechanism; there is a set of height adjustment mechanisms inside the mattress, such as a linear drive, air pump or electromagnetic adjustment system, which is used to change the height of a specific area. Each adjustment area should be independently controlled so that it can be adjusted separately. The control unit compares the current pressure value with the set target pressure value and calculates the difference. Through the closed-loop control system, the control unit sends an adjustment command to the height adjustment mechanism in real time so that the actual pressure value approaches the target pressure value. When the user changes the sleeping position or the weight distribution changes, the system needs to be able to respond quickly and readjust the height of the corresponding part; it can be automatically adjusted through a preset algorithm or manually adjusted through user input; The advantages of this embodiment over the existing technology are: the user posture is estimated and analyzed based on the pressure data of each point corresponding to the posture, the pressure anomaly of each point corresponding to the posture is obtained according to the change of the user posture, and the pressure of each point corresponding to the posture is distributed and adjusted according to the pressure anomaly of each point corresponding to the posture and the set parameters of the corresponding user, so that the corresponding points are adjusted to the corresponding pressure-bearing positions, and significant improvements are achieved in pressure monitoring, data processing and support adjustment. Compared with existing smart mattresses, it can more accurately monitor and adjust the pressure distribution of various parts of the human body, significantly improving the user's comfort and sleep quality. In particular, when the user turns over or changes sleeping position, the system can respond quickly to ensure the best support effect in any situation.

[0019] Example 2 like Figure 3 As shown, this embodiment provides a human posture recognition system based on a flexible sensor, which is implemented based on the above-mentioned human posture recognition method based on a flexible sensor, and specifically includes an information acquisition module, a posture estimation and analysis module, a pressure anomaly analysis module, and a pressure distribution and adjustment module; Among them, the information acquisition module is used to obtain the pressure data of each point corresponding to the posture through the pressure sensing module, and at the same time obtain the setting parameters of the corresponding user; the posture estimation and analysis module is used to estimate and analyze the user posture based on the pressure data of each point corresponding to the posture; the pressure anomaly analysis module is used to obtain the pressure anomaly of each point corresponding to the posture in response to the change of the user's posture; the pressure distribution and adjustment module is used to distribute and adjust the pressure borne by each point corresponding to the posture according to the pressure anomaly of each point corresponding to the posture and the setting parameters of the corresponding user, so that the corresponding point is adjusted to the position corresponding to the pressure borne.

[0020] Example 3 This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned human posture recognition method based on the flexible sensor by calling the computer program stored in the memory.

[0021] The electronic device may vary significantly due to different configurations or performance, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the flexible sensor-based human gesture recognition method provided in the above method embodiment. The electronic device may also include other components for implementing the device functions. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment will not be described in detail here.

[0022] Example 4 This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program is run on a computer device, the computer device is enabled to execute the above-mentioned flexible sensor-based human posture recognition method.

[0023] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0024] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned examples, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A human posture recognition method based on a flexible sensor, characterized in that: It includes the following specific steps: Step 1: Obtain pressure data of each point corresponding to the posture through the pressure sensing module, and simultaneously obtain the setting parameters of the corresponding user; Step 2: Estimating and analyzing the user's posture based on the pressure data of each point corresponding to the posture; Step 3: Obtain abnormal pressure conditions at each point corresponding to the user's posture changes; Step 4: According to the abnormal pressure conditions of the points corresponding to the posture and the set parameters of the corresponding user, the pressure of the points corresponding to the posture is distributed and adjusted, so that the corresponding points are adjusted to the positions corresponding to the pressure.

2. The human body posture recognition method based on flexible sensor according to claim 1, characterized in that: The step 2 of estimating and analyzing the user's posture based on the pressure data of each point corresponding to the posture includes the following specific steps: S21, acquiring image data of various parts of the user, and simultaneously acquiring pressure data of various points corresponding to the posture, acquiring contours of the component parts based on the corresponding ratios of the pressure data of various points corresponding to the posture, and acquiring posture data of each component part using the contours of the component parts and the image data of the corresponding angles of each part of the user; S22: Combining the posture data of each component part to obtain an overall posture image of the user.

3. The human body posture recognition method based on flexible sensor according to claim 2, characterized in that: The step of obtaining the abnormal pressure conditions at each point corresponding to the user's posture changes includes the following specific steps: S31, obtaining all overall posture images of the user during this rest period, and obtaining pressure data of each part at each time; S32. Import the pressure data of each part at each time into the pressure abnormal value analysis formula to calculate the pressure abnormal value of each pressure part, wherein the pressure abnormal value calculation formula of the zth part is: , where T is the compression time of the zth part, dt is the time integral, mzt is the compression pressure data of the zth part at time t, mz is the maximum value of the safety range of the compression pressure of the corresponding zth part, and Tm is the compression time safety value.

4. The human body posture recognition method based on flexible sensor according to claim 3, characterized in that: The method of distributing and adjusting the pressure on each point corresponding to the posture according to the abnormal pressure conditions at each point corresponding to the posture and the set parameters of the corresponding user includes the following specific steps: Obtain the corresponding pressure abnormal value of each part and the corresponding user setting parameters and substitute them into the pressure distribution coefficient calculation formula of each point to calculate the pressure distribution coefficient. Among them, the pressure distribution coefficient calculation formula of the zth part is: , where P is the number of compressed parts, vz is the patient level of the zth compressed part, For the level weight, multiply the body weight by the pressure distribution coefficient of the corresponding part to obtain the pressure that the corresponding part needs to bear, and adjust the height of the corresponding part so that the corresponding point is adjusted to the position that bears the corresponding pressure.

5. The human body posture recognition method based on flexible sensor according to claim 4, characterized in that: The step of obtaining the contour of the component parts based on the corresponding ratio of the pressure data of each point corresponding to the posture comprises the following specific steps: S211, obtaining pressure data for each point corresponding to the posture, obtaining corresponding ratio data of the pressure at each point in the posture, importing the corresponding ratio of the pressure into the three-dimensional construction model to construct a length image of the corresponding ratio of the corresponding point, and then fusing the length images of all the points in the posture into a posture contour according to the corresponding position; S212. Acquire the corresponding three-dimensional images of the posture contours of each region, and simultaneously acquire the three-dimensional image data of each part of the user, and acquire the three-dimensional images of the three-dimensional image data of each part of the user in various angles of the posture in bed. Import the three-dimensional images of the posture contours of each region of the user and the three-dimensional images of the three-dimensional image data of each part of the user in various angles of the posture in bed into a similarity calculation formula, calculate the similarity between the three-dimensional images of the posture contours of each region of the user and the three-dimensional images of the three-dimensional image data of each part of the user in various angles of the posture in bed, and obtain the corresponding angle of the corresponding part of the user with the greatest similarity to the posture contours of each region, and set it as the posture data of each component part.

6. A human posture recognition system based on a flexible sensor, which is implemented based on the human posture recognition method based on a flexible sensor according to any one of claims 1 to 5, characterized in that: It specifically includes an information acquisition module, a posture estimation and analysis module, a pressure anomaly analysis module and a pressure distribution and adjustment module; The information acquisition module is used to obtain pressure data of each point corresponding to the posture through the pressure sensing module, and simultaneously obtain the setting parameters of the corresponding user; The posture estimation and analysis module estimates and analyzes the user's posture based on the pressure data of each point corresponding to the posture; The pressure anomaly analysis module obtains the pressure anomaly of each point corresponding to the posture change according to the user's posture change; The pressure distribution and adjustment module is used to distribute and adjust the pressure borne by each point corresponding to the posture according to the abnormal pressure conditions of each point corresponding to the posture and the set parameters of the corresponding user, so that the corresponding point is adjusted to the position corresponding to the pressure borne.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the human posture recognition method based on flexible sensors as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the human posture recognition method based on a flexible sensor according to any one of claims 1 to 5.

Images