Posture processing method and intelligent wearable system

Abstract

This application discloses a posture processing method and smart wearable system, relating to the technical field of smart wearable devices. The posture processing method is applied to smart clothing, wherein the smart clothing includes multiple fiber-based stretch sensors woven into areas of the smart clothing corresponding to human joints. The posture processing method comprises: obtaining stretch detection values ​​from the fiber-based stretch sensors; determining corresponding angle values ​​based on the stretch detection values; and mapping the angle values ​​to the coordinate axes of a three-dimensional coordinate system for each of the human joints to obtain a three-dimensional posture. This application addresses the technical problem of low accuracy in user posture recognition using existing posture recognition schemes.

Description

Technical Field

[0001] The present application relates to the technical field of smart wearable devices, and in particular to a posture processing method and a smart wearable system. Background Art

[0002] In fields such as human-computer interaction, virtual reality, health monitoring and sports science, the research and application of human posture estimation technology has become an increasing focus.

[0003] Existing gesture recognition methods typically use inertial or optical motion capture. Inertial motion capture embeds inertial measurement units (IMUs) in key locations on the suit (such as joints and torso). By integrating IMU sensor data, the IMUs calculate the posture angles of various body parts. However, long-term use can lead to "drift" due to integration errors. Optical motion capture, on the other hand, relies on computer vision after acquiring images to capture motion, and is susceptible to occlusion and light interference. This results in lower accuracy in the user's gesture recognition.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a posture processing method and an intelligent wearable system, aiming to solve the technical problem of low accuracy of user posture recognition by existing posture recognition schemes.

[0006] To achieve the above-mentioned object, the present application proposes a posture processing method applied to a smart garment, wherein the smart garment comprises a plurality of fiber-based stretch sensors woven into areas of the smart garment corresponding to respective human joints;

[0007] The posture processing method comprises:

[0008] obtaining a tensile test value of the fiber-based tensile sensor;

[0009] Determining a corresponding angle value according to the stretching detection value;

[0010] The angle values ​​are mapped to the coordinate axes of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture.

[0011] In one embodiment, the step of determining the corresponding angle value according to the stretch detection value includes:

[0012] Acquiring an initial stretch value of the fiber-based stretch sensor, wherein the initial stretch value is a stretch value of the fiber-based stretch sensor when the fiber-based stretch sensor is in an initial standard state;

[0013] Calculating a stretch change value between the stretch detection value and the initial stretch value;

[0014] According to the angle mapping relationship between the stretch change value and the fiber-based stretch sensor, the angle value corresponding to the stretch detection value is obtained, wherein the angle mapping relationship describes the mapping relationship between the stretch change value and the angle value of the fiber-based stretch sensor.

[0015] In one embodiment, the step of mapping the angle values ​​to the coordinate axes of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture includes:

[0016] Determining, according to the arrangement position of the fiber-based stretch sensor, the coordinate axes of the three-dimensional coordinate system of the human joint corresponding to the fiber-based stretch sensor;

[0017] Mapping the angle value of the fiber-based tensile sensor to the coordinate axes of the corresponding three-dimensional coordinate system to obtain the local joint angle;

[0018] According to the transformation relationship between the three-dimensional coordinate systems of the human body joints, the local joint angles are connected in series to obtain the global human body posture as the three-dimensional posture.

[0019] In one embodiment, the step of concatenating the local joint angles according to the transformation relationship between the three-dimensional coordinate systems of the human joints to obtain the global human posture includes:

[0020] Acquire a designated human body region, and determine a local joint angle of each human body joint in the designated human body region;

[0021] According to the transformation relationship between the three-dimensional coordinate systems of the human joints, the local joint angles of the human joints in the specified human body area are connected in series to obtain the global human body posture.

[0022] In one embodiment, the human body joints in the designated human body region include ankle joints, knee joints, and hip joints;

[0023] After the steps of acquiring the designated human body region and determining the local joint angles of the human body joints in the designated human body region, the method includes:

[0024] In response to a jumping event, acquiring a landing acceleration, and calculating a ground reaction force based on the landing acceleration, wherein the landing acceleration is determined according to the vertical acceleration during the jumping event;

[0025] Determine the force thresholds of the ankle, knee, and hip joints based on their local joint angles;

[0026] According to the ground reaction force, a reverse recursive calculation is performed with the ankle joint as the starting point to obtain the ankle joint force, the knee joint force and the hip joint force;

[0027] The ankle joint force, knee joint force and hip joint force are compared with the corresponding force thresholds respectively, and when any one of the ankle joint force, knee joint force and hip joint force exceeds the corresponding force threshold, an injury warning message is output.

[0028] In one embodiment, the gesture processing method further includes:

[0029] In response to a motion analysis instruction, obtaining a joint chain and a standard motion angle sequence involved in the motion to be analyzed;

[0030] Generate a current action angle sequence by using the angles of each local joint in the joint chain;

[0031] Aligning the current motion angle sequence with the standard motion angle sequence to obtain a motion coordination deviation;

[0032] Generating posture correction prompt information according to the movement coordination deviation.

[0033] In one embodiment, the step of aligning the current motion angle sequence with the standard motion angle sequence to obtain the motion coordination deviation includes:

[0034] Mapping the current motion angle sequence and the standard motion angle sequence on the same time axis, and calculating the angle difference and timing difference between the key phase points in the standard motion angle sequence and the mapped phase points corresponding to the current motion angle sequence;

[0035] The angle difference and the timing difference are regarded as motion coordination deviation.

[0036] In one embodiment, after the step of mapping the angle values ​​to the coordinate axes of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture, the posture processing method further includes:

[0037] Importing the three-dimensional posture into a standard human body model to determine the real-time center of gravity position under the three-dimensional posture;

[0038] When the horizontal moving speed of the real-time center of gravity position is greater than a predetermined speed threshold, or the relative distance between the real-time center of gravity position and a predetermined human body support point is greater than a predetermined distance threshold, a fall warning message is output.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a smart wearable system, which includes smart clothing and a control terminal connected to the smart clothing for communication;

[0040] The smart clothing comprises: a base fabric layer and a plurality of fiber-based stretch sensors, wherein the fiber-based stretch sensors are woven into areas of the base fabric layer corresponding to respective human joints;

[0041] The control terminal is configured to implement the steps of the gesture processing method described above.

[0042] In one embodiment, the smart clothing further includes a data acquisition module;

[0043] The data acquisition module is electrically connected to each of the fiber-based stretching sensors via a wire, and is used to collect sensor signals from each of the fiber-based stretching sensors and send the signals to a control terminal.

[0044] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the posture processing method described above are implemented.

[0045] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the posture processing method described above are implemented.

[0046] One or more technical solutions proposed in this application have at least the following technical effects:

[0047] The present application is applied to smart clothing, which includes multiple fiber-based stretch sensors woven into the areas of the smart clothing corresponding to each human joint. Since the degree of fiber stretching in the areas corresponding to each human joint is different when the human joint is at different angles, the present application can obtain the stretch detection value of the fiber-based stretch sensor and then determine the corresponding angle value based on the stretch detection value. Therefore, the present application can map the angle value to the coordinate axis of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture. Thus, the present application uses the fiber-based stretch sensor to accurately identify the angle value of each human joint in three-dimensional space from the mechanical stretch level, and map it to the three-dimensional coordinate system of each human joint to form a three-dimensional posture. Compared with existing inertial motion capture and optical motion capture, the posture processing method of the present application is not only more adaptable to different scenarios, but also because it performs angle recognition from the mechanical stretch level, there is no problem of integral drift, which can effectively improve the accuracy of user posture recognition. In addition, inertial motion capture and optical motion capture require fitting multiple degrees of freedom (position + posture) of the whole body based on inertial information and image information, resulting in a large amount of computational data and high processing difficulty. The present application directly fits the human body posture through the joint angles detected by the fiber-based tensile sensor, with a small amount of computational data and less processing difficulty, which can improve the real-time performance of user posture recognition and achieve rapid response and feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 A flowchart of the first embodiment of the posture processing method of this application is provided;

[0051] Figure 2 A schematic structural diagram of the smart clothing involved in an embodiment of the present application;

[0052] Figure 3 A scene diagram of a three-dimensional coordinate system of each human joint involved in the embodiments of this application;

[0053] Figure 4 A schematic diagram of a scenario of coordinate axis limits in a three-dimensional coordinate system involved in an embodiment of the present application;

[0054] Figure 5 This is a diagram of a setting scenario of a fiber-based tensile sensor according to an embodiment of the present application;

[0055] Figure 6 A schematic diagram of a standard human body model involved in an embodiment of the present application;

[0056] Figure 7 A flowchart of the second embodiment of the posture processing method of this application is provided;

[0057] Figure 8 A flowchart of the third embodiment of the posture processing method of this application is provided;

[0058] Figure 9 This is a system structure diagram of the smart wearable system in the embodiment of this application;

[0059] Figure 10 This is an example diagram of the smart clothing involved in the embodiments of the present application;

[0060] Figure 11 This is another example diagram of the smart clothing involved in an embodiment of the present application.

[0061] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0063] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0064] The main solution of the embodiment of the present application is: the smart clothing includes multiple fiber-based stretch sensors, and the fiber-based stretch sensors are woven into the areas of the smart clothing corresponding to each human joint; the stretch detection value of the fiber-based stretch sensor is obtained; the corresponding angle value is determined according to the stretch detection value; the angle value is mapped to the coordinate axis of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture.

[0065] Existing gesture recognition methods typically use inertial or optical motion capture. Inertial motion capture embeds inertial measurement units (IMUs) in key locations on the suit (such as joints and torso). By integrating IMU sensor data, the IMUs calculate the posture angles of various body parts. However, long-term use can lead to "drift" due to integration errors. Optical motion capture, on the other hand, relies on computer vision after acquiring images to capture motion, and is susceptible to occlusion and light interference. This results in lower accuracy in the user's gesture recognition.

[0066] This application provides a solution that, using a fiber-based stretch sensor, accurately identifies the angle values ​​of each human joint in three-dimensional space from a mechanical stretching perspective, mapping these angles to a three-dimensional coordinate system for each joint to form a three-dimensional pose. Compared to existing inertial motion capture and optical motion capture, this proposed pose processing method is not only more adaptable to different scenarios, but also, because it identifies angles from a mechanical stretching perspective, it eliminates the problem of integral drift, effectively improving the accuracy of user pose recognition. Furthermore, inertial and optical motion capture require fitting multiple degrees of freedom (position and pose) of the entire body based on inertial and image information, resulting in a large amount of computational data and high processing difficulty. However, this application directly fits human poses using joint angles detected by the fiber-based stretch sensor, requiring less computational data and less processing difficulty. This improves the real-time nature of user pose recognition and enables rapid response and feedback.

[0067] Based on this, the embodiment of the present application provides a posture processing method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the gesture processing method of the present application.

[0068] In this embodiment, the posture processing method is applied to smart clothing, and the smart clothing includes a plurality of fiber-based stretch sensors, and the fiber-based stretch sensors are woven into the areas of the smart clothing corresponding to the joints of the human body;

[0069] The posture processing method includes steps S10 to S30:

[0070] Step S10, obtaining a tensile detection value of the fiber-based tensile sensor;

[0071] It should be noted that if Figure 2 As shown, the smart clothing can be a garment in the form of a coat, pants, gloves, sleeves, etc., and the base fabric layer of the smart clothing ( Figure 2 The black area in the figure is made of elastic fibers (such as spandex, polyester, nylon, polyamide, or blended elastic fibers) to conform to the user's body surface. In addition, the elastic fibers can also be treated with functional treatments (such as antibacterial treatment, washing-resistant coating, fatigue-resistant coating, etc.). The smart clothing includes multiple fiber-based stretch sensors woven into the base fabric layer of the smart clothing and the areas corresponding to the human joints ( Figure 2 ), to detect the stretching amplitude of the corresponding region of each human joint, representing the corresponding angle value. It is understood that the fiber-based stretch sensor is at least positioned in the region where stretching occurs during human joint movement. Exemplarily, the fiber-based stretch sensor can be incorporated into the base fabric layer by weaving, embroidery, or adhesion.

[0072] In addition, it should be noted that the fiber-based stretching sensor is a fiber-shaped sensor, and the fiber-based stretching sensor causes voltage changes as the fiber stretches. Therefore, this embodiment can determine the fiber stretching amplitude in the area where the fiber-based stretching sensor is located through the voltage change.

[0073] This embodiment can establish a communication connection with each fiber-based stretching sensor through wireless communication or wired communication, thereby receiving the sensor signal of each fiber-based stretching sensor and obtaining the stretching detection value of the fiber-based stretching sensor.

[0074] Step S20, determining a corresponding angle value according to the stretching detection value;

[0075] It should be noted that the stretch detection value represents the fiber length. The stretch detection value may be a voltage value directly output by a fiber-based stretch sensor, or a length value converted from the voltage value.

[0076] Since the stretching amplitude of fibers varies at different angles in human joints, taking the elbow joint as an example, when the elbow joint is flexed, the fibers in the area corresponding to the dorsal side of the elbow joint are stretched, and the deeper the elbow joint flexes (i.e., the smaller the angle between the humerus and the ulna or radius), the greater the stretching amplitude of the fibers. The fiber-based stretching sensor is set on the smart clothing in the area corresponding to the dorsal side of the elbow joint, and there is a mapping relationship between the fiber stretching amplitude detected by the fiber-based stretching sensor and the angle value of the elbow joint in the flexion direction. Therefore, this embodiment can determine the fiber stretching amplitude of the smart clothing in the area corresponding to each human joint based on the stretching detection value, and then determine the angle value of the human joint corresponding to the stretching detection value based on the mapping relationship between the fiber stretching amplitude and the angle value.

[0077] In a feasible implementation, step S20 may include steps S21 to S23:

[0078] Step S21, obtaining an initial stretch value of the fiber-based stretch sensor, wherein the initial stretch value is a stretch value of the fiber-based stretch sensor when the fiber-based stretch sensor is in an initial standard state;

[0079] Step S22, calculating a stretch change value between the stretch detection value and the initial stretch value;

[0080] Step S23: obtaining an angle value corresponding to the stretch detection value according to a mapping relationship between the stretch change value and the angle corresponding to the fiber-based stretch sensor, wherein the angle mapping relationship describes a mapping relationship between the stretch change value and the angle value of the fiber-based stretch sensor.

[0081] It should be noted that the initial stretch value refers to the stretch value of the fiber-based stretch sensor in its initial standard state, which is the state of the fiber-based stretch sensor in a specified standard posture (such as a natural upright posture, an upright posture with arms extended, etc.). For example, in this embodiment, the smart garment can be worn on a standard human body model in a specified standard posture, and the stretch detection value of the fiber-based stretch sensor can be used as the initial stretch value. This embodiment can also guide the user to use the stretch detection value of the fiber-based stretch sensor as the initial stretch value after wearing the smart garment and assuming a specified standard posture.

[0082] This embodiment can also obtain the initial stretch value of the fiber-based stretch sensor, where the initial stretch value is the stretch value of the fiber-based stretch sensor in its initial standard state. This can be accomplished by calculating a stretch change value between the stretch detection value and the initial stretch value, where the stretch change value is the difference between the stretch detection value and the initial stretch value. This embodiment can then obtain the angle value corresponding to the stretch detection value based on a mapping relationship between the stretch change value and the angle corresponding to the fiber-based stretch sensor, where the angle mapping relationship describes the mapping relationship between the stretch change value and the angle value of the fiber-based stretch sensor. The angle mapping relationship can be described in the form of a mapping table, a fitting function, or the like. For example, the voltage divider value is the voltage output by the fiber-based stretch sensor after being stretched. Since the signal directly output by the fiber-based stretch sensor is generally an analog signal, the voltage divider value can be converted to decimal to obtain the current stretch value. The difference between the current stretch value and the initial stretch value is then calculated to obtain a stretch change value. The angle value corresponding to the stretch detection value is then calculated based on the stretch change value and the fitting function. For example, the fitting function corresponding to the fiber-based stretch sensor on the X-axis of the left shoulder is f(x) = 11.924x. The current stretch value is 464, and the initial stretch value is 460. The stretch change value is the current stretch value minus the initial stretch value, that is, 464-460=4, and the angle value f(4) = 11.924*4=47.70°. The fitting function corresponding to the fiber-based stretch sensor on the Y-axis of the left shoulder is f(x) = -1.8407x+90. The current stretch value is 985, and the initial stretch value is 953. The stretch change value is the current stretch value minus the initial stretch value, that is, 985-953=32, and the angle value f(32) = -1.8407*32+90=31.10°. The fitting function for the fiber-based stretch sensor on the Z-axis of the left shoulder is f(x) == -1.8058x. The current stretch value is 340, and the initial stretch value is 359. The stretch change is the current stretch value minus the initial stretch value, i.e., 340 - 359 = -19. The angle value f(-19) = -1.8058 * -19 = 34.31°. Furthermore, to reduce errors, this embodiment can provide multiple fiber-based stretch sensors along each coordinate axis of the three-dimensional coordinate system corresponding to each human joint. By fusing the detection values ​​of these multiple fiber-based stretch sensors, the stretch detection value of the fiber-based stretch sensor along each coordinate axis of the three-dimensional coordinate system corresponding to the human joint is obtained.

[0083] Step S30 , mapping the angle value to the coordinate axis of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture.

[0084] It should be noted that the three-dimensional coordinate system of the human joint is a coordinate system pre-set with the joint of the human joint as the origin. For example, Figure 3 As shown, the positive direction of the X-axis of the three-dimensional coordinate system is the direction of the bone connected to one end of the human joint, the positive direction of the Y-axis points to a specified direction perpendicular to the positive direction of the X-axis (such as pointing to the front of the body), and the Z-axis is the direction perpendicular to the plane formed by the X-axis and the Y-axis. In addition, Figure 4 As shown, due to the limitations of the human body structure, the value range of each coordinate axis in the three-dimensional coordinate system corresponding to different human joints is determined according to the range of motion of the human joint.

[0085] It should also be noted that the three-dimensional posture can be the whole body posture of the human body, or the limb posture of part of the human body joints, such as the limb posture of the hand area, the limb posture of the upper body area, etc.

[0086] Because the angle values ​​corresponding to the fiber-based stretch sensors describe angles along a single degree of freedom (i.e., a specific coordinate axis), this embodiment requires mapping the angle values ​​corresponding to each fiber-based stretch sensor to the coordinate axes of the three-dimensional coordinate system for each human joint to obtain a three-dimensional posture. For example, this embodiment determines the coordinate axes of the three-dimensional coordinate system for the human joint corresponding to the fiber-based stretch sensor based on the placement of the fiber-based stretch sensor. The angle values ​​of the fiber-based stretch sensor can then be mapped to the coordinate axes of the corresponding three-dimensional coordinate system to obtain the local joint angle, where the local joint angle is the three-dimensional vector of the human joint within the corresponding three-dimensional coordinate system. Because each three-dimensional coordinate system is established based on the skeletal orientation of each joint and one end, and because the joints have relative positional relationships, this embodiment can concatenate the local joint angles according to the transformation relationship between the three-dimensional coordinate systems of the joints to obtain the global human posture as the three-dimensional posture. This embodiment thus imports the three-dimensional posture into a human body model, enabling real-time mapping and visualization of user movements. Furthermore, this embodiment can use the obtained three-dimensional posture for scenarios such as motion analysis, health rehabilitation, human-computer interaction, and virtual reality.

[0087] In a feasible implementation, step S30 may include steps S31 to S33:

[0088] Step S31, determining the coordinate axes of the three-dimensional coordinate system of the human joint corresponding to the fiber-based stretch sensor according to the installation position of the fiber-based stretch sensor;

[0089] Step S32, mapping the angle value of the fiber-based tensile sensor to the coordinate axis of the corresponding three-dimensional coordinate system to obtain the local joint angle;

[0090] Step S33 , according to the transformation relationship between the three-dimensional coordinate systems of the human body joints, the local joint angles are connected in series to obtain the global human body posture as the three-dimensional posture.

[0091] It should be noted that the fiber-based stretching sensors are arranged within the range of each human joint, so as to detect the fiber stretching amplitude of each human joint in the direction of each coordinate axis in a three-dimensional coordinate system.

[0092] like Figure 5As shown, the red segments in the figure represent fiber-based stretch sensors. For the neck, fiber-based stretch sensors 7 and 13 are used to detect the fiber stretch amplitude of the trapezius muscle on the right side of the human body in the X-axis direction; fiber-based stretch sensors 8 and 14 are used to detect the fiber stretch amplitude of the trapezius muscle on the left side of the human body in the X-axis direction. Fiber-based stretch sensor 1 is used to detect the fiber stretch amplitude of the trapezius muscle on the right side of the human body in the Y-axis direction; fiber-based stretch sensor 2 is used to detect the fiber stretch amplitude of the trapezius muscle on the left side of the human body in the Y-axis direction. Fiber-based stretch sensors 5 and 11 are used to detect the fiber stretch amplitude of the trapezius muscle on the right side of the human body in the Z-axis direction; fiber-based stretch sensors 6 and 12 are used to detect the fiber stretch amplitude of the trapezius muscle on the left side of the human body in the Z-axis direction. For the shoulder, fiber-based stretch sensor 21 is used to detect the fiber stretch amplitude of the right shoulder in the X-axis direction; fiber-based stretch sensor 22 is used to detect the fiber stretch amplitude of the left shoulder in the X-axis direction. Fiber-based stretch sensor 3 is used to detect the fiber stretch amplitude in the Y-axis direction of the right shoulder of the human body; fiber-based stretch sensor 4 is used to detect the fiber stretch amplitude in the Y-axis direction of the left shoulder of the human body. Fiber-based stretch sensor 9 is used to detect the fiber stretch amplitude in the Z-axis direction of the right shoulder of the human body; fiber-based stretch sensor 10 is used to detect the fiber stretch amplitude in the Z-axis direction of the left shoulder of the human body. For the chest cavity, fiber-based stretch sensor 15 is used to detect the fiber stretch amplitude in the Z-axis direction of the chest cavity. For the waist, fiber-based stretch sensors 16 and 17 are used to detect the fiber stretch amplitude in the X-axis direction of the waist; fiber-based stretch sensors 18 and 19 are used to detect the fiber stretch amplitude in the Y-axis direction of the waist; and fiber-based stretch sensor 20 is used to detect the fiber stretch amplitude in the Z-axis direction of the waist. For the elbow joint, fiber-based stretch sensor 23 is used to detect the fiber stretch amplitude in the X-axis direction of the elbow joint on the right side of the human body; fiber-based stretch sensor 24 is used to detect the fiber stretch amplitude in the X-axis direction of the elbow joint on the left side of the human body; fiber-based stretch sensor 25 is used to detect the fiber stretch amplitude in the Z-axis direction of the elbow joint on the right side of the human body; fiber-based stretch sensor 26 is used to detect the fiber stretch amplitude in the Z-axis direction of the elbow joint on the left side of the human body. Due to the limitations of the human body structure, the elbow joint only has the ability to move in these two degrees of freedom, so there is no need to set up a fiber-based stretch sensor to detect the Y-axis direction of the elbow joint. For the hip joint, fiber-based stretch sensors 31 and 32 are used to detect the fiber stretch amplitude in the X-axis direction of the hip joint; fiber-based stretch sensors 29 and 30 are used to detect the fiber stretch amplitude in the Y-axis direction of the hip joint; fiber-based stretch sensors 27 and 28 are used to detect the fiber stretch amplitude in the Z-axis direction of the hip joint.For the knee joint, fiber-based stretch sensor 33 is used to detect the fiber stretch amplitude in the Z-axis direction of the right knee joint; fiber-based stretch sensor 34 is used to detect the fiber stretch amplitude in the Z-axis direction of the left knee joint. Due to the limitations of the human body structure, the knee joint has only this one degree of freedom of motion, so fiber-based stretch sensors for detecting the knee joint in the X- and Y-axis directions are unnecessary. Therefore, this embodiment can determine the coordinate axes of the three-dimensional coordinate system of the human joint corresponding to the fiber-based stretch sensor based on the placement of the fiber-based stretch sensor. The angle value of the fiber-based stretch sensor is then mapped to the coordinate axes of the corresponding three-dimensional coordinate system to obtain the three-dimensional vector (local joint angle) of the human joint in the corresponding three-dimensional coordinate system, i.e., the angle of the human body in three-dimensional space. Since each three-dimensional coordinate system is established with the joint point of each human joint as the origin, the position and connection relationship between the human joints can be used as the transformation relationship between the three-dimensional coordinate systems of the human joints. Furthermore, this embodiment can concatenate all local joint angles according to the transformation relationship between the three-dimensional coordinate systems of the human joints to obtain the global human posture corresponding to the entire human body as the three-dimensional posture. Regarding the method of connecting the local joint angles in series, this embodiment can first determine the joint parent-child relationship between the joints of the human body, for example: root joint: waist, torso chain: waist→chest→neck, left lower limb chain: waist→left hip→left knee→left ankle, etc. Starting from the root joint in the joint parent-child relationship, each local joint angle is multiplied by the transformation matrix describing the transformation relationship step by step to obtain the global joint angle of each human joint and form the global human body posture. Of course, this embodiment can also connect some local joint angles in series according to the transformation relationship between the three-dimensional coordinate systems of each human joint to obtain the global human body posture corresponding to the partial human body area as the three-dimensional posture.

[0093] In a feasible implementation, step S33 may include steps A10 to A20:

[0094] Step A10, obtaining a designated human body region, and determining the local joint angle of each human body joint in the designated human body region;

[0095] Step A20 , according to the transformation relationship between the three-dimensional coordinate systems of the human joints, the local joint angles of the human joints in the specified human body region are connected in series to obtain the global human body posture.

[0096] It should be noted that the designated human body region is a designated human body region where a global human body posture is expected to be constructed, such as a torso region, an upper body region, a lower body region, and the like.

[0097] This embodiment can obtain a designated human body region for pose construction, then determine the human joints within the designated human body region and the local joint angles of these human joints. Furthermore, based on the transformation relationship between the three-dimensional coordinate systems of the human joints, the local joint angles of the human joints within the designated human body region are concatenated to obtain the global human body pose.

[0098] The embodiments of the present application are different from inertial motion capture solutions. Inertial motion capture solutions use inertial information or optical information to identify the positions of key points in a global coordinate system (such as a ground coordinate system) to achieve posture recognition. Even if this method aims to obtain the global human body posture of a specified human body area, the interference caused by the overall posture change in the global coordinate system will lead to excessive recognition errors in the local human body area. For example, when the global human body posture is constructed only for the hand and elbow areas, when the points in the hand and elbow areas are constructed in the global coordinate system, these points are usually difficult to avoid the interference caused by the overall posture change. For example, when the upper body rotates, even if the hand and elbow do not change, the inertial information of the hand and elbow will change, so that the constructed global human body posture is actually unable to be decoupled from the overall posture, and the posture description accuracy of the specified human body area is achieved.

[0099] In a feasible implementation manner, step S30 may include steps S40 to S50:

[0100] Step S40, importing the three-dimensional posture into a standard human body model to determine the real-time center of gravity position under the three-dimensional posture;

[0101] Step S50: Outputting a fall warning message after the horizontal moving speed of the real-time center of gravity position is greater than a predetermined speed threshold, or the relative distance between the real-time center of gravity position and a predetermined human body support point is greater than a predetermined distance threshold.

[0102] It should be noted that if Figure 6 As shown, the standard human body model is a three-dimensional model of a human body under a specified standard body shape.

[0103] Because existing inertial and optical motion capture methods require extensive data processing based on inertial or optical information, ensuring real-time output of 3D poses is difficult. This embodiment imports the 3D pose into a standard human model to bind the global joint angles in the 3D pose to the model joints in the standard human model, allowing the standard human model to assume the 3D pose. Furthermore, this embodiment can calculate the real-time center of gravity position in the 3D pose based on the standard human model with the imported 3D pose. For example, this embodiment can obtain the mass ratio of each body part in the standard human model, such as 50% for the torso, 8% for the head and neck, 5% for each arm, and 16% for each leg. Next, the center of gravity position of each body part needs to be determined. The center of gravity position of each body part in the standard human model with the imported 3D pose is also determined (e.g., near the geometric center of the body part, or at a corresponding proportional position). For example, the center of gravity of the thigh is located midway between the hip joint and the knee joint, or closer to the hip joint, and the center of gravity of the upper arm is located at a certain proportional position proximal to the shoulder joint, such as 43%. The mass ratio of each body part is then multiplied by its center of gravity position, and the sum is divided by the total mass to obtain the real-time center of gravity position in the three-dimensional posture. This can be done by calculating the relative horizontal distance between the real-time center of gravity position at the previous moment and the current real-time center of gravity position, and the time difference between the real-time center of gravity position at the previous moment and the current real-time center of gravity position, and then calculating the horizontal movement speed of the real-time center of gravity position based on the relative horizontal distance and time difference. This embodiment can determine whether the horizontal movement speed of the real-time center of gravity position is greater than a predetermined speed threshold, and whether the relative distance between the real-time center of gravity position and a predetermined human support point is greater than a predetermined distance threshold, where the predetermined human support point is the center of gravity position of the human body in a predetermined stable posture. Therefore, this embodiment can output a fall warning message when the horizontal moving speed of the real-time center of gravity position is greater than a predetermined speed threshold, or the relative distance between the real-time center of gravity position and the predetermined human body support point is greater than a predetermined distance threshold, indicating that the real-time center of gravity position has changed rapidly, or is no longer in a stable posture, and the user is at risk of falling. The fall warning message is used to warn of the risk of falling, and can be output in the form of text, images, voice, etc. If the horizontal moving speed of the real-time center of gravity position is not greater than the predetermined speed threshold, and the relative distance between the real-time center of gravity position and the predetermined human body support point is not greater than the predetermined distance threshold, it can be determined that there is no risk of falling. This embodiment achieves timely warning of the risk of falling by virtue of high-accuracy joint angles and high real-time performance.

[0104] The first embodiment of the present application provides a posture processing method for smart clothing, wherein the smart clothing includes a plurality of fiber-based stretch sensors, and the fiber-based stretch sensors are woven into the areas of the smart clothing corresponding to the human joints. Since the degree of fiber stretching in the areas corresponding to the smart clothing and the human joints is different when the human joints are at different angles, this embodiment can obtain the stretch detection value of the fiber-based stretch sensor, and then determine the corresponding angle value based on the stretch detection value. In this embodiment, each human joint is provided with an independent three-dimensional coordinate system. Since a single fiber-based stretch sensor can identify the stretch amplitude of the human joint at different angles in a single direction, this embodiment can map the angle value to the coordinate axis of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture. This embodiment utilizes fiber-based stretch sensors to accurately identify the angle values ​​of each human joint in three-dimensional space from a mechanical stretching perspective, mapping these angles to a three-dimensional coordinate system for each joint to form a three-dimensional pose. Compared to existing inertial motion capture and optical motion capture, this embodiment's pose processing approach is not only more adaptable to different scenarios, but also, because it identifies angles from a mechanical stretching perspective, eliminates the problem of integral drift, effectively improving the accuracy of user pose recognition. Furthermore, inertial and optical motion capture require fitting multiple degrees of freedom (position and pose) of the entire body based on inertial and image information, resulting in a large amount of computational data and high processing difficulty. However, this embodiment directly fits human poses using joint angles detected by fiber-based stretch sensors, resulting in a smaller amount of computational data and reduced processing difficulty. This improves the real-time nature of user pose recognition and enables rapid response and feedback.

[0105] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 7 , each human joint in the designated human body region includes an ankle joint, a knee joint and a hip joint;

[0106] Step A10 includes steps B10 to B40:

[0107] Step B10, in response to the jumping event, obtaining a landing acceleration, and calculating a ground reaction force based on the landing acceleration, wherein the landing acceleration is determined according to the vertical acceleration at the time of the jumping event;

[0108] Step B20, determining force thresholds of the ankle joint, knee joint, and hip joint based on the local joint angles of the ankle joint, knee joint, and hip joint;

[0109] Step B30, performing reverse recursive calculation based on the ground reaction force with the ankle joint as the starting point to obtain ankle joint force, knee joint force, and hip joint force;

[0110] Step B40, comparing the ankle joint force, knee joint force and hip joint force with the corresponding force thresholds respectively, and outputting injury warning information when any one of the ankle joint force, knee joint force and hip joint force exceeds the corresponding force threshold.

[0111] It should be noted that for jumping scenarios (such as shooting, long jump, etc.), if the angles of the ankle joint, knee joint and hip joint are different when the user lands, the support force that can be provided will also be different. For example, when the local joint angle of the ankle joint is 0°, that is, when the calf is perpendicular to the ground, the support force provided is the largest. The greater the angle difference between the local joint angle and 0°, the smaller the support force that can be provided. Therefore, this embodiment can set a corresponding force threshold for the local joint angle of each human joint, where the force threshold is the support force of the human joint at the local joint angle. The jumping event is an event in which the user performs a jumping action.

[0112] This embodiment can determine a jump event by obtaining the user's vertical acceleration perpendicular to the ground. If the vertical acceleration shows an initial decrease (during the pre-squat phase) followed by a sharp increase (at the moment of liftoff), a jump event can be determined. This embodiment can then obtain landing acceleration in response to the jump event, where the landing acceleration is determined based on the vertical acceleration at the time of the jump event. The landing acceleration can be the sum of the gravitational acceleration and the absolute value of the vertical acceleration. This embodiment can also account for air resistance during the jump and fall process. The landing acceleration can also be a correction to the sum of the gravitational acceleration and the absolute value of the vertical acceleration, namely, the product of the sum of the gravitational acceleration and the absolute value of the vertical acceleration and a predetermined correction factor. Thus, Newton's second law can be used to calculate the ground reaction force from the landing acceleration. The ground reaction force is the product of the landing acceleration and the body mass. This embodiment can query the mapping relationship between the joint angles of the human body and the force thresholds based on the local joint angles of the ankle, knee, and hip joints to obtain the force thresholds corresponding to the ankle, knee, and hip joints at their respective local joint angles. Furthermore, this embodiment can calculate the ankle joint force, knee joint force and hip joint force at the moment of landing based on the ground reaction force and the ankle joint force, by reverse recursion with the ankle joint as the starting point. For example, the ankle joint force Fankle: Fankle = m foot *g−F GRF , where m foot is the mass of the foot, g is the acceleration due to gravity, F GRF is the ground reaction force. ankle Knee joint force Fknee transmitted to the knee joint: Fknee=Fankle+m shank *g, where m footis the mass of the calf, and the knee joint force F ankle Hip joint force Fhip after being transmitted to the hip joint: Fhip=Fknee+m thigh *g, where m thigh is the mass of the thigh. The ankle, knee, and hip joint forces can be compared with corresponding force thresholds. If any of the ankle, knee, and hip joint forces exceeds the corresponding force threshold, an injury warning message is output. The injury warning message is used to warn of injury risks and can be output in the form of text, images, or voice.

[0113] In a second embodiment of the present application, the landing acceleration is obtained in response to a jumping event, and the ground reaction force is calculated based on the landing acceleration, wherein the landing acceleration is determined according to the vertical acceleration at the time of the jumping event; the force thresholds of the ankle joint, knee joint and hip joint are determined according to the local joint angles of the ankle joint, knee joint and hip joint; according to the ground reaction force, the ankle joint force, knee joint force and hip joint force are obtained by reverse recursive calculation with the ankle joint as the starting point; the ankle joint force, knee joint force and hip joint force are respectively compared with the corresponding force thresholds, and when any one of the ankle joint force, knee joint force and hip joint force exceeds the corresponding force threshold, an injury warning message is output. This embodiment estimates the corresponding landing acceleration using vertical acceleration to calculate the ground reaction force at the moment of landing from a jump. It then recursively calculates the joint force using the ankle joint as the starting point, accurately decomposing the transmission path of the impact force in the joint chain (ankle-knee-hip). The force threshold adapted to the current posture is determined based on the real-time local joint angle. Therefore, if any joint force exceeds the limit, there may be risks such as joint sprains and dislocations, and an injury warning message is immediately output, enabling monitoring and warning of possible injuries after a jump event.

[0114] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 8 The posture processing method further includes steps C10 to C40:

[0115] Step C10, in response to the motion analysis instruction, obtaining the joint chain and standard motion angle sequence involved in the motion to be analyzed;

[0116] Step C20, generating a current action angle sequence from the angles of each local joint in the joint chain;

[0117] Step C30, aligning the current motion angle sequence with the standard motion angle sequence to obtain a motion coordination deviation;

[0118] Step C40: generating posture correction prompt information according to the movement coordination deviation.

[0119] It should be noted that the motion analysis instruction is a command instructing the analysis of a motion behavior, such as the analysis of push-ups, yoga movements, sit-ups, and other motion behaviors. The joint chain involved in the motion to be analyzed is the joint chain composed of the human joints involved in the motion to be analyzed. The standard motion angle sequence is a time sequence composed of the local joint angles of each human joint involved in the joint chain under the standard motion to be analyzed.

[0120] This embodiment can respond to the motion analysis instruction, obtain the joint chain and standard action angle sequence involved in the motion to be analyzed, and then arrange the local joint angles in the joint chain involved in time order to generate the current motion angle sequence. Then, the current motion angle sequence and the standard motion angle sequence are aligned on the time axis, and then the angle difference and timing difference between the key phase point in the standard motion angle sequence and the mapping phase point corresponding to the current motion angle sequence can be calculated. The key phase point is the phase point of the landmark node of the motion to be analyzed in the standard motion angle sequence. For example, taking the motion to be analyzed as sit-ups, the landmark nodes are the four nodes of "back", "lying", "standing up" and "sitting". The mapping phase point is the phase point in the current motion angle sequence that corresponds to the key phase point in the standard motion angle sequence. Then, this embodiment can calculate the difference information (such as angle difference, timing difference) between the key phase point and the mapped phase point as the motion coordination deviation. Thus, this embodiment generates posture correction prompt information based on the motion coordination deviation. For example, in the case of a large angle difference, the local joint angle of the human joint that needs to be corrected and the angle value that needs to be adjusted are prompted; in the case of a large timing difference, the coordination between the human joints that needs to be corrected and the time point of the key phase point that needs to be adjusted are prompted (such as advancing the execution time point of the landmark node in the motion to be analyzed, delaying the execution time point of the landmark node in the motion to be analyzed, etc.).

[0121] In some embodiments, step C30 may include steps D10 to D20:

[0122] Step D10, mapping the current motion angle sequence and the standard motion angle sequence on the same time axis, and calculating the angle difference and timing difference between the key phase points in the standard motion angle sequence and the mapped phase points corresponding to the current motion angle sequence;

[0123] Step D20: taking the angle difference and the timing difference as motion coordination deviation.

[0124] This embodiment can map the current action angle sequence and the standard action angle sequence onto the same time axis, and then match the key phase points in the standard action angle sequence with the current action angle sequence to obtain the mapping phase points in the current action angle sequence corresponding to the key phase points. Furthermore, this embodiment can calculate the angle difference and timing difference between the key phase points in the standard action angle sequence and the mapping phase points corresponding to the current action angle sequence, and use the angle difference and timing difference as the motion coordination deviation, thereby determining the action standard degree of the motion to be analyzed with the help of the angle difference in the motion coordination deviation, and determining the action coordination degree with the help of the timing difference in the motion coordination deviation.

[0125] In the third embodiment of the present application, by responding to a motion analysis instruction, the joint chain and the standard motion angle sequence of the motion to be analyzed are obtained, and the local joint angles in the joint chain are used to generate a current motion angle sequence. The current motion angle sequence and the standard motion angle sequence are aligned to obtain a motion coordination deviation, and a posture correction prompt is generated based on the motion coordination deviation. This embodiment realizes multi-joint collaborative analysis on the time axis and locates the motion coordination deviation by performing nonlinear alignment on the current motion angle sequence and the standard motion angle sequence, so as to realize the deviation in the angle and coordination of the motion to be analyzed so as to provide a posture correction prompt.

[0126] This application provides a smart wearable system, such as Figure 9 As shown, the smart wearable system includes a smart garment 201 and a control terminal 202 in communication with the smart garment 201;

[0127] The smart garment 201 comprises: a base fabric layer and a plurality of fiber-based stretch sensors, wherein the fiber-based stretch sensors are woven into areas of the base fabric layer corresponding to respective human joints;

[0128] The control terminal 202 is configured to implement the steps of the gesture processing method of the above embodiment.

[0129] It should be noted that the smart clothing 201 can be a top, pants, gloves, sleeves, etc. The base fabric layer ( Figure 9 The black area in the figure is made of elastic fibers (such as spandex, polyester, nylon, polyamide, or blended elastic fibers) to conform to the user's body surface. In addition, the elastic fibers can also be treated with functional treatments (such as antibacterial treatment, washing-resistant coating, fatigue-resistant coating, etc.). Smart clothing 201 includes multiple fiber-based stretch sensors woven into the base fabric layer of smart clothing 201 and into the areas corresponding to the human joints ( Figure 9), to detect the stretching amplitude of the corresponding region of each human joint, representing the corresponding angle value. It is understood that the fiber-based stretch sensor is at least positioned in the region where stretching occurs during human joint movement. Exemplarily, the fiber-based stretch sensor can be incorporated into the base fabric layer by weaving, embroidery, or adhesion.

[0130] The control terminal 202 may be a terminal device independent of the smart clothing, or a control unit integrated into the smart clothing. The control terminal 202 may include: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the gesture processing method in the above-mentioned embodiment 1. The control terminal of the smart wearable system in the embodiment of the present application may include, but is not limited to, terminal devices such as smart phones, smart watches, head-mounted display devices, laptop computers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), and desktop computers.

[0131] In some embodiments, the smart garment 201 further includes a data acquisition module;

[0132] The data acquisition module is electrically connected to each of the fiber-based stretching sensors via a wire, and is configured to collect sensor signals from each of the fiber-based stretching sensors and send the signals to the control terminal 202 .

[0133] like Figure 10 As shown, taking smart clothing as an example, Figure 10 The thick black line in the figure is the fiber-based tensile sensor, and the thin light red line is the conductor. Figure 11 As shown, taking smart clothing as gloves as an example, Figure 11 The thick black lines in the figure represent fiber-based stretch sensors, and the thin light red lines represent conductors. Therefore, smart clothing 201 also includes a data acquisition module, which is electrically connected to each fiber-based stretch sensor via conductors and is used to collect sensor signals from each fiber-based stretch sensor and transmit them to control terminal 202.

[0134] Figure 9 The smart wearable system shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0135] The smart wearable system provided by this application, using the posture processing method of the above embodiment, can solve the technical problem of low accuracy of user posture recognition in existing posture recognition solutions. Compared with the prior art, the beneficial effects of the smart wearable system provided by this application are the same as those of the posture processing method provided by the above embodiment, and the other technical features of the smart wearable system are the same as those disclosed in the method of the previous embodiment, which will not be repeated here.

[0136] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0137] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0138] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the gesture processing method in the above embodiment.

[0139] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0140] The computer-readable storage medium may be included in the control terminal, or may exist independently without being assembled into the control terminal.

[0141] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the control terminal, the control terminal: obtains the stretch detection value of the fiber-based stretch sensor; determines the corresponding angle value based on the stretch detection value; maps the angle value to the coordinate axis of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture.

[0142] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0143] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0144] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0145] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned gesture processing method. This computer-readable storage medium can address the technical issue of low accuracy in user gesture recognition using existing gesture recognition solutions. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the gesture processing method provided in the aforementioned embodiments and are not further elaborated here.

[0146] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned gesture processing method when executed by a processor.

[0147] The computer program product provided in this application can solve the technical problem of low accuracy in user gesture recognition by existing gesture recognition solutions. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the gesture processing method provided in the above embodiment, and will not be elaborated here.

[0148] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A posture processing method, characterized in that: Applied to smart clothing, the smart clothing includes a plurality of fiber-based stretch sensors woven into areas of the smart clothing corresponding to various human joints; The posture processing method comprises: obtaining a tensile test value of the fiber-based tensile sensor; Determining a corresponding angle value according to the stretching detection value; Determining, according to the arrangement position of the fiber-based stretch sensor, the coordinate axes of the three-dimensional coordinate system of the human joint corresponding to the fiber-based stretch sensor; Mapping the angle value of the fiber-based tensile sensor to the coordinate axes of the corresponding three-dimensional coordinate system to obtain the local joint angle; According to the transformation relationship between the three-dimensional coordinate systems of the human body joints, the local joint angles are connected in series to obtain the global human body posture as a three-dimensional posture.

2. The posture processing method according to claim 1, wherein: The step of determining the corresponding angle value according to the stretching detection value includes: Acquiring an initial stretch value of the fiber-based stretch sensor, wherein the initial stretch value is a stretch value of the fiber-based stretch sensor when the fiber-based stretch sensor is in an initial standard state; Calculating a stretch change value between the stretch detection value and the initial stretch value; According to the angle mapping relationship between the stretch change value and the fiber-based stretch sensor, the angle value corresponding to the stretch detection value is obtained, wherein the angle mapping relationship describes the mapping relationship between the stretch change value and the angle value of the fiber-based stretch sensor.

3. The posture processing method according to claim 1, wherein: The step of obtaining a global human body posture by connecting the local joint angles in series according to the transformation relationship between the three-dimensional coordinate systems of the human body joints comprises: Acquire a designated human body region, and determine a local joint angle of each human body joint in the designated human body region; According to the transformation relationship between the three-dimensional coordinate systems of the human joints, the local joint angles of the human joints in the specified human body area are connected in series to obtain the global human body posture.

4. The posture processing method according to claim 3, wherein: The human joints in the designated human body area include ankle joints, knee joints and hip joints; After the steps of acquiring the designated human body region and determining the local joint angles of the human body joints in the designated human body region, the method includes: In response to a jumping event, acquiring a landing acceleration, and calculating a ground reaction force based on the landing acceleration, wherein the landing acceleration is determined according to the vertical acceleration during the jumping event; Determine the force thresholds of the ankle, knee, and hip joints based on their local joint angles; According to the ground reaction force, a reverse recursive calculation is performed with the ankle joint as the starting point to obtain the ankle joint force, the knee joint force and the hip joint force; The ankle joint force, knee joint force and hip joint force are compared with the corresponding force thresholds respectively, and when any one of the ankle joint force, knee joint force and hip joint force exceeds the corresponding force threshold, an injury warning message is output.

5. The posture processing method according to claim 1, wherein: The posture processing method further includes: In response to a motion analysis instruction, obtaining a joint chain and a standard motion angle sequence involved in the motion to be analyzed; Generate a current action angle sequence by using the angles of each local joint in the joint chain; Aligning the current motion angle sequence with the standard motion angle sequence to obtain a motion coordination deviation; Generating posture correction prompt information according to the movement coordination deviation.

6. The posture processing method according to claim 5, wherein: The step of aligning the current motion angle sequence with the standard motion angle sequence to obtain a motion coordination deviation includes: Mapping the current motion angle sequence and the standard motion angle sequence on the same time axis, and calculating the angle difference and timing difference between the key phase points in the standard motion angle sequence and the mapped phase points corresponding to the current motion angle sequence; The angle difference and the timing difference are regarded as motion coordination deviation.

7. The posture processing method according to any one of claims 1 to 6, characterized in that: After the step of mapping the angle value to the coordinate axis of the three-dimensional coordinate system of each human joint to obtain a three-dimensional posture, the posture processing method further includes: Importing the three-dimensional posture into a standard human body model to determine the real-time center of gravity position under the three-dimensional posture; When the horizontal moving speed of the real-time center of gravity position is greater than a predetermined speed threshold, or the relative distance between the real-time center of gravity position and a predetermined human body support point is greater than a predetermined distance threshold, a fall warning message is output.

8. A smart wearable system, characterized in that: The smart wearable system includes smart clothing and a control terminal connected to the smart clothing; The smart clothing comprises: a base fabric layer and a plurality of fiber-based stretch sensors, wherein the fiber-based stretch sensors are woven into areas of the base fabric layer corresponding to respective human joints; The control terminal is configured to implement the steps of the gesture processing method according to any one of claims 1 to 7.

9. The smart wearable system according to claim 8, wherein: The smart clothing also includes a data acquisition module; The data acquisition module is electrically connected to each of the fiber-based stretching sensors via a wire, and is used to collect sensor signals from each of the fiber-based stretching sensors and send the signals to a control terminal.

Images