Body posture detection method, storage medium, device and terminal equipment

Abstract

The present invention relates to the field of medical equipment technology, and discloses a posture detection method, storage medium, apparatus, and terminal device, comprising: obtaining posture data of a user to be detected, the posture data being collected by a plurality of cameras and limb motion acquisition sensors arranged around the user to be detected; inputting the posture data into a trained neural network posture detection model to obtain posture detection information; scoring the posture detection information using pre-stored posture standard information to obtain a posture detection result of the user to be detected; and sending the posture detection result and corresponding posture correction measure information to a display device for display. The plurality of limb motion acquisition devices, respectively worn on the limbs and torso of the user to be detected, can accurately collect the limb and joint data of the user to be detected, and can accurately reflect the smoothness value, symmetry value, etc. of the user's limb movements, so that the neural network posture detection model can obtain accurate posture detection information.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a posture detection method, storage medium, device and terminal equipment. Background Art

[0002] Posture assessment is a method of assessing one's posture and form, aiming to identify potential imbalances, muscle tension, or skeletal alignment issues. Poor posture can lead to muscle imbalances and increased joint stress, leading to pain and chronic injuries. For example, long-term rounded shoulders and hunched back posture can cause tension in the neck and back muscles, making cervical spondylosis and low back pain more likely. Correct posture, on the other hand, can enhance one's appearance, height, and confidence, improving both one's image and demeanor.

[0003] In the medical field, doctors can use posture assessments to identify potential musculoskeletal issues, providing guidance for diagnosis and treatment. For example, for patients with scoliosis, early posture assessments can help develop appropriate treatment plans. However, in the medical field, medical professionals typically visually observe the patient's posture while standing, walking, and sitting to initially determine whether common posture issues such as forward head tilt, rounded shoulders, hunchback, and anterior or posterior pelvic tilt exist. For example, forward head tilt indicates the head is in front of the shoulders, while rounded shoulders indicate a forward curvature of the shoulders. They then use tools such as tape measures and angle measuring instruments to measure various body parts. For example, they measure the height difference between the two shoulders to determine uneven shoulders and measure the curvature of the spine to assess the presence of scoliosis. Finally, they have the patient perform specific movements, such as squats, bends, and twists, to observe their fluidity and symmetry. This process allows for a complete posture assessment of the patient. However, this assessment method relies heavily on the doctor's personal experience, and inexperienced doctors can easily misjudge or misdiagnose the condition, resulting in inaccurate medical treatment.

[0004] Therefore, the above-mentioned technical defects need to be changed urgently. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention provides a posture detection method, storage medium, device, and terminal device, designed to accurately capture limb and joint data of the user being detected, accurately reflecting the smoothness and symmetry of the user's limb movements. This enables the neural network posture detection model to obtain accurate posture detection information, preventing misjudgments or misdiagnoses by posture detection devices.

[0006] In order to solve the above technical problems, the first aspect of the embodiments of the present application provides a posture detection method, which includes:

[0007] Acquiring body posture data of the user to be detected, wherein the body posture data is collected by a plurality of cameras arranged around the user to be detected and a plurality of body motion collection sensors, wherein the plurality of body motion collection sensors are respectively worn on each limb and torso of the user to be detected;

[0008] Inputting the posture data into a trained neural network posture detection model to obtain posture detection information, wherein the neural network posture detection model is trained by multiple posture training data sets, each posture training data set including: training posture data and marking information for marking the posture detection information;

[0009] Scoring the posture detection information based on pre-stored posture standard information to obtain the posture detection result of the user to be detected;

[0010] The posture detection results and corresponding posture correction measures are sent to the display device for display.

[0011] The posture detection method, wherein the step of obtaining the posture data of the user to be detected includes:

[0012] Use multiple cameras to obtain video information of the user to be detected in various postures and movements;

[0013] Acquire limb and joint data of the user to be detected through a number of limb motion collection sensors;

[0014] The limb and joint data are fitted to the video information to obtain the posture data.

[0015] The posture detection method, wherein the step of acquiring limb and joint data of the user to be detected by using a plurality of limb motion acquisition sensors, comprises:

[0016] Initialize several body motion collection sensors;

[0017] Acquire dynamic data from several body motion collection sensors, where the dynamic data is collected based on the user to be detected performing standard movements;

[0018] Based on the three-dimensional human body model, the dynamic data is analyzed and calibrated to obtain the limb and joint data of the user to be tested.

[0019] The posture detection method includes a step of analyzing and calibrating dynamic data based on a three-dimensional human body model to obtain limb and joint data of the user to be detected, wherein the limb and joint data include limb reference points, spacing between limbs, connection angles between limbs, and positional relationships between limbs.

[0020] The posture detection method, wherein the step of inputting posture data into a trained neural network posture detection model to obtain posture detection information, includes:

[0021] Splitting the body data into video frames to obtain several video frames, each of which contains corresponding limb and joint data;

[0022] Based on the limb and joint data, obtain several limb reference points of the user to be detected;

[0023] Compare several limb reference points using a neural network posture detection model and score the video frames;

[0024] The video frame with the highest score is selected as the posture detection information.

[0025] In the posture detection method, in the step of scoring the posture detection information using pre-stored posture standard information to obtain the posture detection result of the user to be detected, the posture detection result includes:

[0026] Any one or more of the round shoulder test results, hunchback test results, forward head extension test results, knee hyperextension test results, excessive lumbar flexion test results, anterior pelvic tilt test results, posterior pelvic tilt test results, uneven shoulders test results, bow legs test results, X-legs test results, scoliosis test results, and lateral pelvic tilt test results.

[0027] The posture detection method, after the step of sending the posture detection result and the corresponding posture correction measure information to a display device for display, comprises:

[0028] The posture detection result, the corresponding posture correction measure information and the identity information of the user to be detected are sent to the printing device.

[0029] A second aspect of an embodiment of the present application provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in any of the above posture detection methods.

[0030] A third aspect of the present application provides a posture detection device, comprising:

[0031] An acquisition module is used to acquire body posture data of the user to be detected. The body posture data is collected by a number of cameras set up around the user to be detected and a number of body movement acquisition sensors, which are respectively worn on the limbs and torso of the user to be detected;

[0032] A posture detection module is used to input posture data into a trained neural network posture detection model to obtain posture detection information. The neural network posture detection model is trained using multiple posture training data sets, each of which includes: training posture data and marking information for marking the posture detection information;

[0033] The posture scoring module is used to score the posture detection information based on the pre-stored posture standard information to obtain the posture detection result of the user to be detected;

[0034] The output module is used to send the posture detection results and corresponding posture correction measures information to the display device for display.

[0035] A fourth aspect of an embodiment of the present application provides a terminal device, comprising: a processor, a memory, and a communication bus; the memory stores a computer-readable program executable by the processor;

[0036] The communication bus realizes the connection and communication between the processor and the memory;

[0037] When the processor executes the computer-readable program, the steps in any of the above-mentioned posture detection methods are implemented.

[0038] Beneficial Effects: Compared to the prior art, the present invention provides a posture detection method, storage medium, apparatus, and terminal device. The method comprises: obtaining posture data of a user to be detected, the posture data being collected by a plurality of cameras and limb motion sensors positioned around the user to be detected, the limb motion sensors being worn on each limb and torso of the user to be detected; inputting the posture data into a trained neural network posture detection model to obtain posture detection information, the neural network posture detection model being trained using multiple posture training data sets, each of which includes training posture data and tagging information for tagging the posture detection information; scoring the posture detection information using pre-stored posture standard information to obtain a posture detection result for the user to be detected; and transmitting the posture detection result and corresponding posture correction measures to a display device for display. The limb motion detection devices, worn on each limb and torso of the user, can accurately capture the limb and joint data of the user to be detected, accurately reflecting the smoothness and symmetry of the user's limb movements. This enables the neural network posture detection model to obtain accurate posture detection information. Fitting limb and joint data to the video information collected by the camera can produce a more intuitive display effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a posture detection method provided by the present invention;

[0040] Figure 2 for Figure 1 Schematic diagram of the process of step S10;

[0041] Figure 3 for Figure 1 Schematic diagram of the process of step S12;

[0042] Figure 4 for Figure 3 Flow diagram of step S20

[0043] Figure 5 A schematic structural diagram of a posture detection device provided by the present invention;

[0044] Figure 6 This is a structural principle diagram of the terminal device provided by the present invention. DETAILED DESCRIPTION

[0045] The present invention provides a posture detection method, storage medium, apparatus, and terminal device. To clarify the objectives, technical solutions, and effects of the present invention, the present invention is further described below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention.

[0046] Those skilled in the art will appreciate that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when the present application scheme states that an element is "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0047] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0048] The invention will be further explained below through description of embodiments in conjunction with the accompanying drawings.

[0049] First, it's important to note that in the medical field, medical professionals typically visually observe the patient's posture while standing, walking, and sitting to initially determine if there are common posture issues such as forward head tilt, rounded shoulders, hunchback, and anterior or posterior pelvic tilt. For example, forward head tilt indicates the head is in front of the shoulders, while rounded shoulders indicate a forward curvature of the shoulders. They then use tools such as tape measures and angle measuring instruments to measure various body parts. For example, they measure the height difference between the shoulders to determine uneven shoulders and measure the curvature of the spine to assess scoliosis. Finally, they have the patient perform specific movements, such as squats, bends, and twists, to observe their fluidity and symmetry. This process completes the patient's posture assessment. This method relies heavily on the physician's personal experience, and inexperienced physicians can easily misjudge and misdiagnose, resulting in inaccurate medical treatment.

[0050] To this end, this solution provides a posture detection method, storage medium, device, and terminal device. This invention is a posture detection system that integrates intelligence, data, and information. In the medical field, it can automatically perform posture detection on patients and provide corresponding posture correction and treatment recommendations, effectively improving the efficiency and quality of posture detection. Furthermore, the posture detection system is not limited to the medical field. It can also be used in various home settings and sports training scenarios.

[0051] The device of the present application mainly includes several somatosensory cameras, several limb motion acquisition sensors, a computer (host), an interactive display controller, and audio equipment. Among them, several somatosensory cameras are set around the user to be detected, which can capture the patient's body posture video images at different angles, making it convenient to build a 3D human body model. In addition, several limb motion acquisition sensors are worn on the limbs and torso of the user to be detected. The limb motion acquisition sensors can capture data of the limbs and joints of the user to be detected. Through these data, parameters such as the angle, length, and height of multiple parts of the human body can be measured to comprehensively evaluate the body posture. For example, the curvature angle of the spine, the inclination angle of the shoulder and neck, and the forward and backward tilt angle of the pelvis can be measured. The interactive display controller and audio equipment can establish a channel for human-computer interaction. Interactive information can be displayed through the interactive display controller, including guiding the user to be detected to perform motion detection. It is convenient for the system to capture the body posture of the user to be detected, as well as to capture the limb and joint data of the user to be detected. The somatosensory camera can also perform image functions such as face recognition and motion recognition on the user to be detected, and establish a personal profile.

[0052] The embodiment of the present invention provides a posture detection method, the execution subject of which can be a server side equipped with a posture detection module, wherein the posture detection module is used to perform posture detection and scoring on the user to be detected and to obtain corresponding guidance. It is understandable that the execution subject of this embodiment can be an intelligent terminal connected to the posture detection module, such as a smart phone, a smart bracelet, or a server host. For example, the host obtains the posture data of the user to be detected, and the posture data is collected by a number of cameras set around the user to be detected and a number of limb motion acquisition sensors, and the limb motion acquisition sensors are respectively worn on the limbs and torso of the user to be detected; the host inputs the posture data into a trained neural network posture detection model to obtain posture detection information, and the neural network posture detection model is trained by multiple posture training data sets, each posture training data set including: training posture data and marking information for marking posture detection information; the host scores the posture detection information using pre-stored posture standard information to obtain the posture detection result of the user to be detected; the host sends the posture detection result and corresponding posture correction measure information to a display device for display.

[0053] It should be noted that the above application scenarios are only shown to facilitate understanding of the present invention, and the embodiments of the present invention are not limited in this respect. On the contrary, the embodiments of the present invention can be applied to any applicable scenario.

[0054] In order to further illustrate the content of the invention, embodiments are described in detail below with reference to the accompanying drawings.

[0055] The posture detection method provided in this embodiment is as follows: Figure 1 As shown, the method includes:

[0056] Step S10: Acquire body posture data of the user to be detected, wherein the body posture data is collected by a plurality of cameras arranged around the user to be detected and a plurality of body motion collection sensors, wherein the plurality of body motion collection sensors are respectively worn on the limbs and torso of the user to be detected;

[0057] Specifically, several somatosensory cameras are set up around the user to be tested, which can capture video images of the patient's body posture at different angles, facilitating the creation of a 3D human body model. In addition, several limb motion acquisition sensors are worn on the limbs and torso of the user to be tested. The limb motion acquisition sensors can capture data on the limbs and joints of the user to be tested. This data can be used to measure parameters such as the angle, length, and height of multiple parts of the human body to comprehensively assess body posture. For example, the curvature angle of the spine, the inclination angle of the shoulder and neck, and the forward and backward tilt angle of the pelvis can be measured.

[0058] To quickly initialize the body motion sensors and build a 3D body model of the user, the user must be instructed to perform specific movements and postures. This allows for a more comprehensive understanding of their posture and can expose any posture issues.

[0059] To this end, the present application solution is also provided with interactive devices such as an interactive display controller and audio. These interactive devices can establish a channel for human-computer interaction. Interactive information can be displayed through the interactive display controller. This includes displaying images of movements and postures for guiding the user to be detected on the display, issuing guiding voice through voice, etc. This guides the user to be detected to perform movement detection. It is convenient for the system to capture the posture of the user to be detected, as well as the limb and joint data of the user to be detected. Furthermore, the body sensing camera can also perform image functions such as face recognition and movement recognition on the user to be detected, and establish a personal profile.

[0060] Step S20: Inputting the posture data into a trained neural network posture detection model to obtain posture detection information. The neural network posture detection model is trained using multiple posture training data sets, each of which includes: training posture data and labeling information for labeling the posture detection information.

[0061] Specifically, the present application scheme uses artificial neural network technology to detect human posture. Neural networks can automatically learn the characteristics of human posture and can process complex image and video data. Compared with traditional posture detection methods, neural network posture detection models have higher accuracy. Since neural networks can process data quickly, real-time posture detection can be achieved. This is very important for application scenarios that require real-time monitoring of human posture (such as fitness training, rehabilitation treatment, etc.). Neural networks can adapt to different lighting conditions, background environments and changes in human posture. This makes it have good performance in various practical application scenarios. The performance and detection capabilities of neural networks can be continuously improved by increasing training data and adjusting the network structure. At the same time, it can also be combined with other technologies (such as sensor fusion, cloud computing, etc.) to achieve wider applications.

[0062] This solution uses a body-sensing camera and a body motion sensor in step S10 to capture image or video data of the user to be detected. This data can include different human postures, movements, and performances in different scenarios. Computer vision technology is then used to process the collected data and extract body-related features. These features can include the position and angle of human joints, body contours, and so on.

[0063] It's important to note that before using the neural network posture detection model, it must first be trained using a large posture training dataset. Specifically, the neural network is trained using features extracted from the training dataset as input and body posture categories (such as normal, hunched, and rounded shoulders) as output. During training, the neural network continuously adjusts its weights and biases to ensure that the output is as close to the actual body type as possible.

[0064] When performing actual posture detection, the newly collected human body data is input into the trained neural network, and the network will output the corresponding posture category, thereby realizing posture detection.

[0065] Step S30: Score the posture detection information using pre-stored posture standard information to obtain a posture detection result of the user to be detected;

[0066] Specifically, in step S20, the posture data is input into a trained neural network posture detection model to obtain posture detection information. The neural network posture detection model is trained using multiple posture training data sets, each of which includes training posture data and labeling information for labeling the posture detection information. The posture detection information at this point represents the user's actual posture information. Subsequent steps will require scoring of the posture detection information to obtain the final detection result. Therefore, in this embodiment, the posture detection information is scored using pre-stored posture standard information to obtain the posture detection result for the user to be tested.

[0067] Step S40: Send the posture detection result and the corresponding posture correction measure information to a display device for display.

[0068] Specifically, the system has a pre-set Posture Correction Measures database, which stores data on corrective measures for different posture issues. When a patient's posture issue is detected, the corresponding corrective measures are retrieved from the database, making it easier for doctors and users to understand corrective measures.

[0069] Further, such as Figure 2 As shown, the steps of obtaining the body posture data of the user to be detected include:

[0070] Step S11: using a plurality of camera devices to obtain video information of the user to be detected in various postures and movements;

[0071] Specifically, several cameras shoot the user to be tested at different positions, and while shooting, the user is guided to perform movements and postures through the interactive device, so that the camera can fully obtain the body posture video data of the patient to be tested.

[0072] Step S12: Acquire limb and joint data of the user to be detected through a plurality of limb motion acquisition sensors;

[0073] Specifically, in step S11 , while collecting video information, limb motion collection sensors worn on various limbs of the user to be detected synchronously collect limb and joint data of the user to be detected.

[0074] Step S13: Fit the limb and joint data to the video information to obtain body posture data.

[0075] Specifically, fitting limb and joint data into video information can more intuitively display the posture problems of the user to be detected.

[0076] Further, such as Figure 3 As shown, the steps of acquiring limb and joint data of a user to be detected through a plurality of limb motion acquisition sensors include:

[0077] Step S121: Initialize a plurality of body movement acquisition sensors;

[0078] Specifically, in some embodiments, the size and shape of multiple limb motion sensors are configured to be consistent. In particular, the size and shape of multiple limb motion sensors worn on the patient's limbs are configured to be consistent, enhancing versatility. This eliminates the need for specialized development. When worn and used, the sensors simply need to be secured to the approximate locations of the user's limbs and body using fasteners. Subsequent calculations and interpolation compensation can yield a complete and accurate posture model.

[0079] Therefore, before each posture detection, several body motion sensors need to be initialized. Calibration is performed based on the relative positions of the body motion sensors. The system then recalibrates the position and angle of each body motion sensor.

[0080] Step S122: acquiring dynamic data from a plurality of body motion collection sensors, the dynamic data being acquired based on the user to be detected performing standard movements;

[0081] Specifically, dynamic data allows the system to more accurately calculate the position and angle of each limb movement sensor. This dynamic data requires the user to perform standard movements, so that the system can clearly understand the user's posture and perform targeted calculations.

[0082] Step S123: Analyze and calibrate the dynamic data based on the three-dimensional human body model to obtain limb and joint data of the user to be detected.

[0083] Furthermore, based on the three-dimensional human body model, the dynamic data is analyzed and calibrated to obtain the limb and joint data of the user to be detected. The limb and joint data include limb reference points, the distance between limbs, the connection angle between limbs, and the positional relationship between limbs.

[0084] Further, such as Figure 4 As shown, the steps of inputting posture data into the trained neural network posture detection model to obtain posture detection information include:

[0085] Step S21: splitting the body data into video frames to obtain a plurality of video frames, each of which contains corresponding limb and joint data;

[0086] Specifically, in order to accurately obtain the true body posture of the user to be detected and reflect their body posture, it is necessary to obtain standard video frames in the body posture data that can be used to calculate and compare the standard body posture. Therefore, it is necessary to split the body posture data into video frames.

[0087] Step S22: Based on the limb and joint data, obtain several limb reference points of the user to be detected;

[0088] Specifically, when screening and obtaining standards that can be used to calculate and compare video frames with standard postures, it is necessary to first obtain several limb reference points of the user to be detected on the video frame, and use the several limb reference points to determine whether the current video frame matches the video frame with the standard posture.

[0089] Step S23: comparing several limb reference points using a neural network posture detection model and scoring the video frames;

[0090] Step S24: Select the video frame with the highest score as body posture detection information.

[0091] Furthermore, in the step of scoring the posture detection information using the pre-stored posture standard information to obtain the posture detection result of the user to be detected, the posture detection result includes:

[0092] Any one or more of the round shoulder test results, hunchback test results, forward head extension test results, knee hyperextension test results, excessive lumbar flexion test results, anterior pelvic tilt test results, posterior pelvic tilt test results, uneven shoulders test results, bow legs test results, X-legs test results, scoliosis test results, and lateral pelvic tilt test results.

[0093] Furthermore, after the step of sending the posture detection result and the corresponding posture correction measure information to the display device for display, the method further includes:

[0094] The posture detection result, the corresponding posture correction measure information and the identity information of the user to be detected are sent to the printing device.

[0095] In summary, if Figure 1 As shown, this embodiment provides a posture detection method, which includes: obtaining posture data of a user to be detected, the posture data being collected by a plurality of cameras and limb motion collection sensors arranged around the user to be detected, the limb motion collection sensors being worn on each limb and torso of the user to be detected; inputting the posture data into a trained neural network posture detection model to obtain posture detection information, the neural network posture detection model being trained using multiple posture training data sets, each of which includes training posture data and labeling information for labeling the posture detection information; scoring the posture detection information using pre-stored posture standard information to obtain a posture detection result for the user to be detected; and transmitting the posture detection result and corresponding posture correction measure information to a display device for display. The plurality of limb motion collection devices worn on each limb and torso of the user can accurately collect limb and joint data of the user to be detected, accurately reflecting the smoothness and symmetry values ​​of the user's limb movements, thereby enabling the neural network posture detection model to obtain accurate posture detection information. Fitting limb and joint data to the video information collected by the camera can produce a more intuitive display effect.

[0096] In order to better implement the above method, the embodiment of the present application further provides a posture detection device 100, which can be integrated into an electronic device, such as a terminal, a server, a personal computer, etc. For example, in this embodiment, the device may include: 101 acquisition module, 102 posture detection module, 103 posture scoring module and 104 output module, which are as follows (for example Figure 5 ):

[0097] 101 acquisition module, used to acquire body posture data of the user to be detected, the body posture data is collected by a plurality of cameras arranged around the user to be detected and a plurality of body motion collection sensors, the plurality of body motion collection sensors are respectively worn on the limbs and torso of the user to be detected;

[0098] 102 posture detection module, used for inputting posture data into a trained neural network posture detection model to obtain posture detection information, wherein the neural network posture detection model is trained by multiple posture training data sets, each posture training data set including: training posture data and marking information for marking the posture detection information;

[0099] 103 posture scoring module, used to score the posture detection information according to the pre-stored posture standard information to obtain the posture detection result of the user to be detected;

[0100] 104 output module, used for sending the posture detection results and corresponding posture correction measures to the display device for display.

[0101] In some embodiments, a posture detection device 100 includes an acquisition module 101, a posture detection module 102, a posture scoring module 103, and an output module 104, wherein the acquisition module 101 acquires the posture data of the user to be detected, and the posture data is collected by several cameras set around the user to be detected and several limb motion acquisition sensors, and the several limb motion acquisition sensors are respectively worn on the limbs and torso of the user to be detected; the posture detection module 102 inputs the posture data into a trained neural network posture detection model to obtain posture detection information, and the neural network posture detection model is trained by multiple posture training data sets, each posture training data set includes: training posture data and marking information for marking the posture detection information; the posture scoring module 103 scores the posture detection information by using pre-stored posture standard information to obtain the posture detection result of the user to be detected; the output module 104 sends the posture detection result and corresponding posture corrective measure information to a display device for display.

[0102] In specific implementation, the above units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above units can be found in the previous method embodiments and will not be repeated here.

[0103] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0104] Based on the above posture detection method, this embodiment provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in the posture detection method of the above embodiment. For example, executing the above description Figure 1 Steps S10 to S40 of the method, Figure 2 Steps S11 to S13 of the method, Figure 3 Steps S121 to S123 of the method, Figure 4 The method steps S21 to S24 are as follows:

[0105] Acquiring body posture data of the user to be detected, where the body posture data is collected by a number of cameras set up around the user to be detected and a number of body motion collection sensors, which are respectively worn on the limbs and torso of the user to be detected;

[0106] Inputting the posture data into a trained neural network posture detection model to obtain posture detection information, wherein the neural network posture detection model is trained by multiple posture training data sets, each posture training data set including: training posture data and marking information for marking the posture detection information;

[0107] Scoring the posture detection information based on pre-stored posture standard information to obtain the posture detection result of the user to be detected;

[0108] The posture detection results and corresponding posture correction measures are sent to the display device for display.

[0109] In some embodiments, the step of obtaining the body posture data of the user to be detected includes:

[0110] Use multiple cameras to obtain video information of the user to be detected in various postures and movements;

[0111] Acquire limb and joint data of the user to be detected through a number of limb motion collection sensors;

[0112] The limb and joint data are fitted to the video information to obtain the posture data.

[0113] In some embodiments, the step of acquiring limb and joint data of a user to be detected through a plurality of limb motion collection sensors includes:

[0114] Initialize several body motion collection sensors;

[0115] Acquire dynamic data from several body motion collection sensors, where the dynamic data is collected based on the user to be detected performing standard movements;

[0116] Based on the three-dimensional human body model, the dynamic data is analyzed and calibrated to obtain the limb and joint data of the user to be tested.

[0117] In some embodiments, the step of identifying posture data using a neural network posture detection model to obtain posture detection information includes:

[0118] Splitting the body data into video frames to obtain several video frames, each of which contains corresponding limb and joint data;

[0119] Based on the limb and joint data, obtain several limb reference points of the user to be detected;

[0120] Compare several limb reference points using a neural network posture detection model and score the video frames;

[0121] The video frame with the highest score is selected as the posture detection information.

[0122] Based on the above posture detection method, the present invention also provides a terminal device, such as Figure 6 As shown, it includes at least one processor 20; a display screen 21; and a memory 22. It may also include a communications interface 23 and a bus 24. The processor 20, display screen 21, memory 22, and communications interface 23 can communicate with each other via bus 24. The display screen 21 is configured to display a preset user guidance interface in the initial setup mode. The communications interface 23 can transmit information. The processor 20 can call the logic instructions in the memory 22 to execute the method in the above embodiment.

[0123] In addition, the logic instructions in the memory 22 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0124] The memory 22, as a computer-readable storage medium, can be configured to store software programs or computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes the software programs, instructions, or modules stored in the memory 22 to perform functional applications and data processing, thereby implementing the methods in the above embodiments.

[0125] The memory 22 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 22 may include high-speed random access memory and non-volatile memory. For example, various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, may also be transient storage media.

[0126] In addition, the specific process of loading and executing the multiple instructions in the storage medium and the processor in the mobile terminal has been described in detail in the above method and will not be described here one by one.

[0127] In summary, compared with the prior art, the present invention has the following advantageous effects: a posture detection method, storage medium, apparatus, and terminal device, wherein the method comprises: obtaining posture data of a user to be detected, the posture data being collected by a plurality of cameras and limb motion acquisition sensors disposed around the user to be detected, the limb motion acquisition sensors being worn on each limb and torso of the user to be detected; inputting the posture data into a trained neural network posture detection model to obtain posture detection information, the neural network posture detection model being trained using multiple posture training data sets, each of which includes training posture data and labeling information for labeling the posture detection information; scoring the posture detection information using pre-stored posture standard information to obtain a posture detection result for the user to be detected; and transmitting the posture detection result and corresponding posture correction measure information to a display device for display. The plurality of limb motion acquisition devices, respectively worn on each limb and torso of the user, can accurately capture limb and joint data of the user to be detected, accurately reflecting the smoothness and symmetry values ​​of the user's limb movements. This enables the neural network posture detection model to obtain accurate posture detection information. Fitting limb and joint data to the video information collected by the camera can produce a more intuitive display effect.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A posture detection method, characterized in that: The method comprises: Acquiring body posture data of the user to be detected, wherein the body posture data is collected by a plurality of cameras arranged around the user to be detected and a plurality of limb motion collection sensors, wherein the plurality of limb motion collection sensors are respectively worn on the limbs and torso of the user to be detected. The step of acquiring the body posture data of the user to be detected includes: Acquiring video information of the user to be detected in standard actions by using a plurality of the aforementioned cameras; Acquiring limb and joint data of the user to be detected through a plurality of limb motion acquisition sensors; Fitting the limb and joint data to the video information to obtain the posture data, wherein the limb and joint data includes limb reference points, spacing between limbs, connection angles between limbs, and positional relationships between limbs; inputting the posture data into a trained neural network posture detection model to obtain posture detection information, wherein the neural network posture detection model is trained using multiple posture training data sets, each of which includes training posture data and labeling information for labeling the posture detection information; Scoring the posture detection information using pre-stored posture standard information to obtain a posture detection result of the user to be detected; The posture detection results and corresponding posture correction measures are sent to a display device for display: The step of inputting the posture data into a trained neural network posture detection model to obtain posture detection information includes: Performing video frame splitting on the body posture data to obtain a plurality of video frames, each of the video frames containing corresponding limb and joint data; Based on the limb and joint data, obtaining a plurality of limb reference points of the user to be detected; Comparing the plurality of limb reference points with the pre-stored standard posture information through a neural network posture detection model, and scoring the video frame; The video frame with the highest score is selected as the posture detection information.

2. The posture detection method according to claim 1, characterized in that: The step of acquiring limb and joint data of the user to be detected by using a plurality of limb motion acquisition sensors includes: Initializing a plurality of the body motion acquisition sensors; Acquiring dynamic data from a plurality of the body motion collection sensors, wherein the dynamic data is collected based on the user to be detected performing standard movements; Based on the three-dimensional human body model, the dynamic data is analyzed and calibrated to obtain the limb and joint data of the user to be detected.

3. The posture detection method according to claim 2, characterized in that: In the step of analyzing and calibrating the dynamic data based on the three-dimensional human body model to obtain the limb and joint data of the user to be detected, the limb and joint data include limb reference points, the distance between limbs, the connection angle between limbs, and the positional relationship between limbs.

4. The posture detection method according to claim 1, characterized in that: In the step of scoring the posture detection information using pre-stored posture standard information to obtain the posture detection result of the user to be detected, the posture detection result includes: Any one or more of the round shoulder test results, hunchback test results, forward head extension test results, knee hyperextension test results, excessive lumbar flexion test results, anterior pelvic tilt test results, posterior pelvic tilt test results, uneven shoulders test results, bow legs test results, X-legs test results, scoliosis test results, and lateral pelvic tilt test results.

5. The posture detection method according to claim 1, characterized in that: After the step of sending the posture detection result and the corresponding posture correction measure information to a display device for display, the method further includes: The posture detection result, the corresponding posture correction measure information and the identity information of the user to be detected are sent to a printing device.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the posture detection method according to any one of claims 1 to 5.

7. A posture detection device, characterized in that: include: The acquisition module is used to acquire body posture data of the user to be detected. The body posture data is collected by a plurality of cameras arranged around the user to be detected and a plurality of body motion acquisition sensors, wherein the plurality of body motion acquisition sensors are respectively worn on the limbs and torso of the user to be detected. The steps of acquiring the body posture data of the user to be detected include: Acquiring video information of the user to be detected in standard actions by using a plurality of the aforementioned cameras; Acquiring limb and joint data of the user to be detected through a plurality of limb motion acquisition sensors; Fitting the limb and joint data to the video information to obtain the body posture data, wherein the limb and joint data include limb reference points, spacing between limbs, connection angles between limbs, and positional relationships between limbs; a posture detection module, configured to input the posture data into a trained neural network posture detection model to obtain posture detection information, wherein the neural network posture detection model is trained using a plurality of posture training data sets, each of which includes: training posture data and marking information for marking the posture detection information; A posture scoring module is used to score the posture detection information according to pre-stored posture standard information to obtain the posture detection result of the user to be detected; An output module is used to send the posture detection results and corresponding posture correction measures to a display device for display: The step of inputting the posture data into a trained neural network posture detection model to obtain posture detection information includes: Performing video frame splitting on the body posture data to obtain a plurality of video frames, each of the video frames containing corresponding limb and joint data; Based on the limb and joint data, obtaining a plurality of limb reference points of the user to be detected; Comparing the plurality of limb reference points with the pre-stored standard posture information through a neural network posture detection model, and scoring the video frame; The video frame with the highest score is selected as the posture detection information.

8. A terminal device, characterized in that: include: A processor, a memory and a communication bus; the memory stores a computer-readable program that can be executed by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, the steps of the posture detection method according to any one of claims 1 to 5 are implemented.

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