Gait motion data recognition system and method thereof, storage medium

Abstract

The application relates to the technical field of motion analysis, in particular to a footstep motion data recognition system and method thereof and a storage medium. A signal acquisition interaction module is laid under a target site floor tile screen; the signal acquisition interaction module is used for emitting probe light capable of penetrating the floor tile screen and receiving feedback light formed after the probe light is modulated; an optical feature shoe has a shoe sole made of special optical material; the special optical material is used for modulating the probe light to form the feedback light; a data processing module is connected to the signal acquisition interaction module, and the data processing module is used for performing motion data calculation according to the probe light and the feedback light to determine footstep motion representation data matched with the optical feature shoe in a motion process. The footstep motion data recognition system and method thereof can recognize footstep motion representation data related to footstep micro-motion details.

Description

Technical Field

[0001] This application relates to the field of motion analysis technology, and in particular to a foot movement data recognition system and method, and storage medium. Background Technology

[0002] Current basketball court athlete identification and status monitoring technologies have multiple limitations, making it difficult to meet the demands of next-generation smart courts for high precision, interference-free operation, and system integration. In terms of identification, existing solutions mainly rely on wearable devices such as wristbands or vests. These devices not only increase the physical burden on athletes but are also prone to damage or dislodgement during strenuous activity. Manual registration methods are inefficient and cannot achieve real-time dynamic tracking of athletes. On the other hand, while computer vision-based camera recognition solutions are widely used, they are susceptible to interference from complex lighting conditions on the court and occlusion during rapid movement, resulting in unstable recognition accuracy.

[0003] In terms of motion monitoring, existing technologies mostly capture athletes' macroscopic movement trajectories through field sensors, but they cannot effectively analyze the crucial micro-movement details of footwork. This coarse method of motion assessment cannot meet the needs of refined motion analysis. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a foot movement data recognition system and method, as well as a storage medium, capable of recognizing foot movement representation data concerning the details of foot micro-movements.

[0005] The foot movement data recognition system according to a first aspect embodiment of this application includes: A signal acquisition and interaction module is installed under the ground tile screen of the target site; wherein, the signal acquisition and interaction module is used to emit detection light that can pass through the ground tile screen, and to receive feedback light formed by modulating the detection light. An optical feature shoe, wherein the sole of the optical feature shoe is made of a special optical material; wherein the special optical material is used to modulate the detection light to form the feedback light; A data processing module is connected to the signal acquisition and interaction module. The data processing module is used to perform motion data calculation based on the detection light and the feedback light to determine the foot motion characterization data matching the optical feature shoe during the movement process.

[0006] According to some embodiments of this application, the special optical material forms a composite optical pattern on the sole of the optical feature shoe. The composite optical pattern includes an identity encoding sub-pattern and a pose representation sub-pattern, and the identity encoding sub-pattern and the pose representation sub-pattern have different optical properties.

[0007] According to some embodiments of this application, the identity coding sub-pattern is disposed in the forefoot area and heel area of ​​the optical feature shoe on the sole; the pose characterization sub-pattern is disposed in the waist area of ​​the optical feature shoe on the sole.

[0008] According to some embodiments of this application, the composite optical pattern further includes a directional characterization sub-pattern, which is disposed in the toe area of ​​the sole of the optical feature shoe.

[0009] According to some embodiments of this application, the signal acquisition and interaction module includes an LED display array and an optical sensor array. The LED display array is used to emit detection light that can pass through the ground tile screen, and the optical sensor array is used to receive feedback light formed after the detection light is modulated. The identity coding sub-pattern includes an identity recognition area formed by arranging multiple coding graphics, and the graphic size of each coding graphic is an interactive adaptation size; wherein, the interactive adaptation size is jointly determined by the lamp bead arrangement density of the lamp bead display array and the sensor arrangement density of the optical sensing array.

[0010] According to some embodiments of this application, the foot movement data recognition system is configured with multiple pairs of optical feature shoes; wherein each pair of optical feature shoes is divided into a left feature shoe and a right feature shoe, and the composite optical pattern of the left feature shoe and the composite optical pattern of the right feature shoe have a matching pattern relationship.

[0011] The foot movement data recognition method according to a second aspect of this application is applied to the foot movement data recognition system described in a first aspect of this application, the method comprising: The control signal acquisition and interaction module emits detection light that passes through the site's floor tile screen; During the movement of the optical feature shoe, the detection light is modulated by a special optical material on the sole of the shoe to form a feedback light; The feedback light is received through the signal acquisition and interaction module; In the data processing module, motion data is calculated based on the detection light and the feedback light to determine the foot motion characterization data matching the optical feature shoe during the movement process.

[0012] According to some embodiments of this application, the step of performing motion data calculation based on the probe light and the feedback light to determine foot motion characterization data matching the optical feature shoe during movement includes: Obtain the target signal of the probe light corresponding to the probe light ray; The feedback light beam is amplified to obtain the intermediate signal of the feedback light; The intermediate feedback light signal is subjected to anti-interference processing to filter out environmental noise signals, thereby obtaining the target feedback light signal. Motion data is calculated based on the detection light target signal and the feedback light target signal to determine the foot motion characterization data of the optical feature shoe.

[0013] According to some embodiments of this application, the special optical material forms a composite optical pattern on the sole of the optical feature shoe. The composite optical pattern includes an identity coding sub-pattern and a pose representation sub-pattern, and the identity coding sub-pattern and the pose representation sub-pattern have different optical properties. The process of calculating motion data based on the probe light target signal and the feedback light target signal to determine the foot motion characterization data of the optical feature shoe includes: Obtain motion characterization benchmark data; The feedback light target signal is partitioned and analyzed to obtain the identity encoding light signal and the pose representation light signal; The identity-encoded optical signal is decoded to obtain the identity information corresponding to the optical feature shoe; Based on the motion representation reference data, the pose representation optical signal is analyzed to obtain foot pose analysis data. Based on the identity information and the foot pose analysis data, the foot movement representation data of the optical feature shoe is generated.

[0014] According to some embodiments of this application, obtaining motion characterization benchmark data includes: Determine the standard pose of the optical feature shoe; Collect the static reference data corresponding to the standard pose of the pose representation sub-pattern; Based on the static reference data of the standard pose, the motion characterization reference data is generated; The step pose analysis data is obtained by performing pose analysis on the pose representation optical signal based on the motion representation reference data, including: Based on the pose representation light signal, determine the pose pattern representation data of the pose representation sub-pattern; The foot pose analysis data is obtained by performing pose analysis based on the static reference data and the pose pattern representation data.

[0015] According to some embodiments of this application, the acquisition of the static reference data corresponding to the standard pose of the pose representation sub-pattern includes: The system collects the feedback ray reference data and geometric contour reference information of the pose representation sub-pattern corresponding to the standard pose, as well as the relative position reference information between the pose representation sub-pattern and the signal acquisition interaction module under the standard pose. The static reference data is obtained by integrating the feedback ray reference data, the geometric contour reference information, and the relative position reference information; The step of performing pose analysis based on the static reference data and the pose pattern representation data to obtain the foot pose analysis data includes: The real-time data of the feedback light, the real-time information of the geometric contour, and the real-time information of the relative position between the pose representation sub-pattern and the signal acquisition and interaction module during the motion are extracted from the pose pattern representation data. Orientation analysis is performed based on the reference data of the feedback light and the real-time data of the feedback light to determine the distance between the sole of the optical feature shoe and the ground; Based on the real-time geometric contour information and the reference geometric contour information, morphological analysis is performed to determine the sole inclination data of the optical feature shoe; Motion trend analysis is performed based on the real-time relative position information and the relative position reference information to determine the motion trend characterization data of the optical feature shoe; The foot posture analysis data is generated based on the distance of the sole from the ground, the sole inclination data, and the motion trend representation data.

[0016] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the foot movement data recognition method as described in any one of the embodiments of the second aspect of this application.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program that is executed by a processor to implement the foot movement data recognition method as described in any one of the embodiments of the second aspect of this application.

[0018] The foot movement data recognition system, method, and storage medium according to the embodiments of this application have at least the following beneficial effects: This application proposes a foot movement data recognition system, method, and storage medium. The system includes a signal acquisition and interaction module installed beneath a surface tile screen at the target site. This module emits detection light that can pass through the surface tile screen and receives feedback light formed by modulating the detection light. An optical feature shoe has a sole made of a special optical material. This special optical material modulates the detection light to form the feedback light. A data processing module is connected to the signal acquisition and interaction module. This data processing module performs motion data calculations based on the detection light and the feedback light to determine foot movement characterization data matching the optical feature shoe during movement. The foot movement data recognition system and method of this application can identify foot movement characterization data concerning the details of micro-step movements.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 A schematic diagram of a foot movement data recognition system provided in an embodiment of this application; Figure 2A Another schematic diagram of a foot movement data recognition system provided in this application embodiment; Figure 2B This is a schematic diagram of the sole of an optical feature shoe provided in an embodiment of this application; Figure 3 A flowchart illustrating a foot movement data recognition method provided in this application embodiment; Figure 4 Another flowchart of a foot movement data recognition method provided in an embodiment of this application; Figure 5 Another flowchart of a foot movement data recognition method provided in an embodiment of this application; Figure 6 Another flowchart of a foot movement data recognition method provided in an embodiment of this application; Figure 7 Another flowchart of a foot movement data recognition method provided in an embodiment of this application; Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0022] In the description of this application, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0023] In the description of this application, it should be understood that the orientation descriptions, such as up, down, left, right, front, and back, are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0024] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0025] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setting," "installation," and "connection" should be interpreted broadly. Those skilled in the art can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution. Furthermore, the identification of specific steps in the following text does not imply a limitation on the order of steps or execution logic. The execution order and logic between each step should be understood and inferred from the content described in the embodiments.

[0026] Current basketball court athlete identification and status monitoring technologies have multiple limitations, making it difficult to meet the demands of next-generation smart courts for high precision, interference-free operation, and system integration. In terms of identification, existing solutions mainly rely on wearable devices such as wristbands or vests. These devices not only increase the physical burden on athletes but are also prone to damage or dislodgement during strenuous activity. Manual registration methods are inefficient and cannot achieve real-time dynamic tracking of athletes. On the other hand, while computer vision-based camera recognition solutions are widely used, they are susceptible to interference from complex lighting conditions on the court and occlusion during rapid movement, resulting in unstable recognition accuracy.

[0027] In terms of motion monitoring, existing technologies mostly capture athletes' macroscopic movement trajectories through field sensors, but they cannot effectively analyze the crucial micro-movement details of footwork. This coarse method of motion assessment cannot meet the needs of refined motion analysis.

[0028] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a foot movement data recognition system and method, as well as a storage medium, capable of recognizing foot movement representation data concerning the details of foot micro-movements.

[0029] The foot movement data recognition system of this application embodiment consists of at least three core parts: a signal acquisition and interaction module, an optical feature shoe, and a data processing module. These three parts form a complete technical closed loop through the transmission and processing of optical signals, and together realize the accurate recognition and data extraction of foot movement status during field sports.

[0030] Reference Figure 1 The foot movement data recognition system according to the embodiments of this application may include: The signal acquisition and interaction module is laid under the ground tile screen of the target site; the signal acquisition and interaction module is used to emit detection light that can pass through the ground tile screen, and to receive feedback light formed by modulating the detection light. It should be noted that the signal acquisition and interaction module, as the system's sensing front end, is physically deployed beneath the court's paving stone screen. This module performs bidirectional optical signal processing. On one hand, it actively emits detection light that can penetrate the paving stone screen; this light possesses specific wavelength characteristics and can transmit upwards through the paving stone medium. On the other hand, it receives feedback light modulated by shoe soles, completing the capture and acquisition of optical signals. This design of placing the signal acquisition and interaction module beneath the paving stone screen protects the sensor hardware from direct foot traffic damage while maintaining the integrity of the optical detection path through the screen's light-transmitting properties, achieving physical integration with the existing basketball court infrastructure.

[0031] Optical feature shoes, the soles of which are made of special optical materials; these special optical materials are used to modulate the detection light to form feedback light; It should be noted that the core technical feature of the optical feature shoe, as the passive response end of the system, lies in the special optical material embedded in the sole. This special optical material possesses the ability to optically modulate probe light of a specific wavelength. When the probe light emitted by the signal acquisition and interaction module penetrates the floor tile screen and illuminates the sole, the special optical material modulates the incident probe light according to its inherent optical properties (high reflectivity or high absorptivity). This modulation process changes the intensity distribution, reflection angle, or spectral characteristics of the light, thereby forming feedback light carrying information about the optical pattern of the sole. This feedback light penetrates downward through the floor tile screen and is received by the signal acquisition and interaction module.

[0032] The data processing module, connected to the signal acquisition and interaction module, is used to perform motion data calculation based on the probe light and feedback light to determine the foot motion representation data of the shoe matching the optical features during the movement process.

[0033] It should be noted that the data processing module establishes a communication relationship with the signal acquisition and interaction module through an electrical connection, forming the data processing hub of the system. The data processing module receives the raw emission parameters of the probe light and the characteristic data of the feedback light from the signal acquisition and interaction module. By comparing and analyzing the differences between the incident and reflected light in terms of intensity, phase, and spatial distribution, it executes a motion data calculation algorithm. The calculation process may include extracting the spatial distribution characteristics, intensity distribution patterns, and temporal variation laws of the optical pattern on the sole from the feedback light, thereby determining the foot motion characterization data of the shoe matching this optical feature during movement. This foot motion characterization data can encompass multi-dimensional motion parameters such as athlete identification, foot orientation angle, sole distance from the ground, and sole tilt angle.

[0034] Reference Figure 2A According to some embodiments of this application, special optical materials form composite optical patterns on the soles of optical feature shoes. The composite optical patterns include an identity coding sub-pattern and a pose representation sub-pattern, and the identity coding sub-pattern and the pose representation sub-pattern have different optical properties.

[0035] It should be noted that special optical materials are used to construct a composite optical pattern on the sole surface of the optical feature shoe. This pattern serves as the physical carrier for optical information exchange between the sole and the signal acquisition and interaction module. The composite optical pattern consists of two functional areas: an identity encoding sub-pattern and a pose representation sub-pattern. The two sub-patterns have a specific spatial distribution relationship on the sole plane and are made of special optical materials with different optical properties, enabling them to produce differentiated optical responses under the same detection light.

[0036] The identity coding sub-pattern primarily functions to provide unique identification information. It typically employs a specific dot matrix or graphic coding structure, constructed from special optical materials with high reflectivity or high absorptivity for specific wavelengths of probe light. When the probe light emitted by the signal acquisition and interaction module illuminates the identity coding sub-pattern, the special optical material in that area modulates the incident light according to its optical properties, forming feedback light with a specific intensity distribution and spatial pattern. By analyzing the coding pattern in this feedback light, the data processing module can decode and obtain the unique identification of the athlete wearing the optically characteristic shoes, thus achieving the identification function.

[0037] Regarding the pose representation sub-pattern, its function is to assist in extracting motion posture information. It is also composed of special optical materials, but the optical properties of the materials used differ significantly from those of the identity encoding sub-pattern. In some embodiments, if the identity encoding sub-pattern uses a high-reflectivity material, the pose representation sub-pattern typically uses a high-absorption material, and vice versa, thus forming a dual-region structure with a clear contrast in reflectivity on the sole. When the optical feature shoe is in motion, the spatial posture of the sole relative to the signal acquisition and interaction module changes, and the projection position, geometry, and reflected light intensity of the pose representation sub-pattern on the sensor array change accordingly, generating real-time feedback light characteristics different from the static reference.

[0038] It should be noted that the difference in optical properties between the identity encoding sub-pattern and the pose representation sub-pattern constitutes the physical basis for signal comparison. The intensity contrast, boundary features, and relative positional relationship formed by the two sub-patterns in the feedback light provide a spatial reference benchmark for the data processing module. By analyzing the signal intensity difference, geometric deformation degree, or positional offset of the pose representation sub-pattern relative to the identity encoding sub-pattern, the embodiments of this application can calculate pose parameters such as sole orientation, ground clearance, and tilt angle. This dual-region comparison design effectively suppresses the absolute signal intensity error caused by ambient light fluctuations and individual sensor differences, improving the accuracy of pose detection.

[0039] It should be understood that the composite optical pattern in this embodiment achieves integrated encoding of identity information and posture information. The identity encoding sub-pattern provides a stable and unchanging identity identifier, while the posture representation sub-pattern provides dynamic features that change with the motion state. Both are simultaneously acquired in a single optical detection through the different optical properties of special optical materials. The data processing module can simultaneously analyze the identity encoding of the identity encoding sub-pattern and the posture features of the posture representation sub-pattern based on the same feedback beam, without the need for additional sensors or independent acquisition channels, thus achieving simultaneous completion of identity recognition and motion posture detection. This integrated design enables the optical feature shoe to simultaneously support high-precision identity recognition and refined motion state monitoring without passive operation or additional electronic components.

[0040] According to some embodiments of this application, the foot movement data recognition system is configured with multiple pairs of optical feature shoes; wherein, each pair of optical feature shoes is divided into a left feature shoe and a right feature shoe, and the composite optical pattern of the left feature shoe and the composite optical pattern of the right feature shoe have a matching pattern relationship.

[0041] It should be noted that the foot movement data recognition system is equipped with multiple pairs of optical feature shoes. This configuration indicates that the system has the ability to simultaneously support the identification and movement status monitoring of multiple athletes. Each pair of optical feature shoes corresponds to a specific athlete's identity. By identifying the composite optical patterns on the soles of different optical feature shoes, this embodiment can simultaneously track and manage multiple moving targets, meeting the group data collection needs in high-intensity competitive sports scenarios such as basketball.

[0042] In some embodiments, each pair of optical feature shoes is physically divided into a left feature shoe and a right feature shoe. This division allows the system to clearly distinguish between the athlete's left and right feet. In court sports such as basketball, parameters such as the movement trajectory, landing sequence, and rotation angle of the left and right feet have different technical implications for tactical analysis and motion evaluation. Therefore, distinguishing between the left and right feet helps in achieving refined motion state monitoring. The left and right feature shoes are configured independently, each with a complete sole optical structure.

[0043] It should be noted that there is a matching pattern relationship between the composite optical pattern of the left and right characteristic shoes. This relationship is reflected in the specific correspondence between the layout design of the identity coding sub-patterns and pose representation sub-patterns on the soles of the left and right shoes. This matching relationship may take the form of symmetrical layout, mirror structure, or complementary coding, that is, the pattern layout of the left and right soles is geometrically symmetrical, or they form a complementary pair in coding logic. For example, a specific dot matrix arrangement in the left identity coding sub-pattern has a identifiable pairing feature with the dot matrix arrangement in the right identity coding sub-pattern, enabling the system to determine that the two shoes belong to the same pair of equipment.

[0044] It should be understood that this matching pattern relationship provides the technical basis for the data processing module to identify the left and right feet. When two shoes with matching pattern relationships are detected simultaneously, they can be automatically identified as the left and right feet of the same athlete, avoiding identity confusion or mismatch of left and right feet caused by multiple athletes exercising simultaneously. In addition, the matching pattern relationship also helps the system analyze the relative movement relationship between the left and right feet, such as stride length, stride frequency, and gait parameters such as the difference in landing time between the left and right feet, providing accurate data pairing basis for sports biomechanics analysis. By identifying the matching pattern relationship, the embodiments of this application can establish a complete bipedal gait model, improving the accuracy and completeness of motion data calculation.

[0045] According to some embodiments of this application, the identification coding sub-pattern is placed in the forefoot and heel areas of the sole of the optical feature shoe, while the posture representation sub-pattern is placed in the midfoot area of ​​the sole. This zonal arrangement is designed based on the anatomical features and functional requirements of the human foot. The forefoot and heel areas are the main load-bearing areas when the foot contacts the ground. Placing the identification coding sub-pattern in these areas ensures that the coded pattern maintains effective optical interaction with the court surface during normal standing or running, avoiding interruption of the identification signal due to the arch of the foot being suspended or the sole partially lifting off the ground. The midfoot area corresponds to the arch of the foot and is the area where the foot undergoes the most significant bending deformation during movement. Placing the posture representation sub-pattern here maximizes the capture of changes in the pitch angle and pronation / inversion of the foot, providing sensitive optical modulation signals for posture calculation.

[0046] In some embodiments, the composite optical pattern further includes a direction representation sub-pattern, which is disposed in the toe area of ​​the sole. As the forward endpoint of the foot, the toe directly reflects the athlete's intended direction of movement. By setting a dedicated direction representation sub-pattern in the toe area, the optical sensing array of the court tile screen can quickly determine the athlete's real-time direction of travel by detecting the positional offset and reflection characteristics of this sub-pattern in the grid coordinate system. This eliminates the need for positional difference calculations across multiple consecutive frames, reducing the algorithmic complexity and response latency of direction recognition.

[0047] In some embodiments, the signal acquisition and interaction module includes an LED display array and an optical sensor array, which work together to perform active optical detection. The LED display array emits detection light that can penetrate the stress layer of the court paving stone screen. This light then penetrates upwards through the mask structure and reaches the sole surface. The optical sensor array receives feedback light formed after the detection light is modulated by the composite optical pattern on the sole. The modulation process here refers to the selective reflection of the detection light on the sole surface: the black base area absorbs the light, while the white coded pattern area reflects the light, thus forming a contrasting distribution of light spots on the optical sensor array. This active emission-passive modulation optical mechanism eliminates the need for a built-in power supply or active light-emitting elements in the sole, reducing the structural complexity and maintenance costs of smart shoes.

[0048] In some embodiments, the identity coding sub-pattern comprises an identity recognition area formed by the arrangement of multiple coded patterns. The size of each coded pattern is defined as an interactive adaptation size, which is not arbitrarily set but is jointly determined by the LED bead arrangement density of the LED display array and the sensor arrangement density of the optical sensor array. Specifically, the size of the coded pattern needs to match the optical resolution of the floor tile screen: if the pattern size is too small, a single pattern may not be able to cover a sufficient number of sensor nodes in the optical sensor array, resulting in weak reflected signals or unstable detection; if the pattern size is too large, adjacent coded patterns may overlap on the optical sensor array due to insufficient spacing, causing coding confusion. The determination of the interactive adaptation size also needs to consider the arrangement density of the LED display array, because the uniformity of the emitted light directly affects the edge sharpness of the reflected light spot. By jointly optimizing the size of the coded pattern and the arrangement density of the two arrays, it is ensured that each coded pattern forms an independently distinguishable light spot response on the optical sensor array, while maintaining the necessary spacing between adjacent light spots, thereby achieving reliable binary coding recognition.

[0049] Reference Figure 2B This diagram illustrates the spatial correspondence between a pair of optical feature shoes and a signal acquisition and interaction module. The gray grid background represents the shared plane of the LED display array and optical sensor array within the field's tiled screen. Red markers at grid intersections correspond to the display LEDs and optical sensor nodes within the array partitions, forming a fixed photoelectric sensing grid. The left and right soles are symmetrically arranged within this grid coordinate system; their black outline represents the sole base, made of a low-reflectivity material, used to absorb the detection light emitted by the LED display array.

[0050] The shoe sole features identification code sub-patterns in the forefoot and heel areas, composed of multiple white dot-shaped codes. Six codes are placed in the forefoot area, and two in the heel area, resulting in a total of 8 bits of binary code capacity for both feet. The size of each code is an adaptive size, determined by the LED density of the LED display array and the sensor density of the optical sensor array. This ensures that each code covers an integer number of sensor nodes in the grid coordinate system, and that adjacent codes maintain minimal spacing to avoid optical crosstalk. This size design allows the optical sensor array to clearly distinguish the individual light spots reflected from each code, thus accurately decoding the athlete's identification information.

[0051] It is worth noting that the distance between each coded pattern is the interactive adaptation distance, which can also be determined by the LED bead arrangement density of the LED display array and the sensor arrangement density of the optical sensor array. During the actual movement of the optical feature shoe, adjacent coded patterns need to form distinguishable independent light spot responses on the optical sensor array. If the interval is set too small, the light spots generated on the optical sensor array by the feedback light reflected from adjacent coded patterns will overlap, causing two originally independent coded signals to merge into a single signal, resulting in aliasing and bit errors in identity recognition information. If the interval is set too large, the limited planar area of ​​the sole will reduce the number of coded patterns that can be accommodated, directly reducing the information capacity and coding efficiency of the identity coding sub-pattern.

[0052] The midfoot area of ​​the sole features a posture representation pattern, appearing as a white, belt-like graphic running horizontally through the middle of the sole. This area corresponds to the arch of the foot. When an athlete jumps or lands, the midfoot area bends and deforms, causing a change in the relative angle between the posture representation pattern and the stress-bearing surface structure of the court surface. The intensity of the reflected light emitted by the LED display array changes accordingly in this area. The optical sensor array calculates the pitch angle and pronation / inversion state of the foot by detecting the difference in reflectivity at different grid nodes of the belt-like pattern.

[0053] The toe area features directional sub-patterns, using white material to indicate the direction of the forefoot. Simultaneously, two coded patterns on the heel area are asymmetrically arranged between the left and right feet, distinguishing them by the difference in the offset direction of the white heel dots relative to the center line of the sole. When the signal acquisition and interaction module is active, the LED display array emits detection light upwards. This light penetrates the stress layer of the court tile screen and reaches the sole surface. The detection light undergoes selective reflection modulation through the identity coding sub-pattern, posture representation sub-pattern, and direction representation sub-pattern to form feedback light. The black base area absorbs the light, while the white pattern areas reflect it. The feedback light penetrates downwards through the stress layer and is received by the optical sensor array, completing the conversion from optical signal to electrical signal, enabling simultaneous detection of the athlete's identity, posture, direction, and left / right foot attributes.

[0054] It should be understood that the composite optical patterns of optical feature shoes are diverse in type and are not limited to the examples mentioned above.

[0055] Reference Figure 3 The foot movement data recognition method according to the embodiments of this application, applied to the foot movement data recognition system of the embodiments of this application, may include: Step S301: Control the signal acquisition and interaction module to emit detection light that passes through the site's floor tile screen; Step S302: During the movement of the optical feature shoe, the detection light is modulated by a special optical material on the sole of the shoe to form a feedback light. Step S303: Receive feedback light through the signal acquisition and interaction module; Step S304: In the data processing module, motion data is calculated based on the detection light and feedback light to determine the foot motion representation data matching the optical feature shoe during the movement process.

[0056] According to the foot movement data recognition method of this application embodiment, the method is applied to a foot movement data recognition system. Through the coordinated operation of the control signal acquisition and interaction module, the optical feature shoe, and the data processing module, accurate recognition and data acquisition of foot movement states during basketball movements are achieved. The core of this application embodiment lies in establishing an optical closed loop between the detection light and the feedback light. By analyzing the physical characteristics in the light transmission path, quantitative data reflecting the athlete's foot movement state is extracted.

[0057] In step S301 of some embodiments, the control signal acquisition and interaction module emits detection light that passes through the site floor tile screen; It should be noted that, in this embodiment, the signal acquisition and interaction module laid beneath the floor tile screen generates and emits upward-facing detection light that can penetrate the floor tile screen. This detection light has specific wavelength characteristics, allowing it to pass through the floor tile screen medium without significant attenuation, thereby ensuring that the light can effectively reach the sole of the optical feature shoe located above the floor tile screen, providing the necessary incident light source for subsequent optical modulation.

[0058] In step S302 of some embodiments, during the movement of the optical feature shoe, the detection light is modulated by a special optical material on the sole of the optical feature shoe to form a feedback light. It should be noted that during the movement of the optically-featured shoes, the special optical materials in the sole optically modulate the incident probe light. This modulation process is based on the reflection or absorption characteristics of the special optical materials for specific wavelengths of light. When the probe light shines on the composite optical pattern on the sole surface, the material changes the intensity distribution, propagation direction, or spectral characteristics of the light according to its physical properties, thereby forming feedback light carrying optical information from the sole. This modulation process encodes the athlete's foot position, posture, and other physical state information into transmittable optical signals.

[0059] In step S303 of some embodiments, feedback light is received through the signal acquisition and interaction module; It should be noted that the signal acquisition and interaction module performs the light receiving operation, capturing the feedback light transmitted downwards after being modulated by the shoe sole. This feedback light penetrates the court tile screen and is received by the signal acquisition and interaction module located below the tile screen. The signal acquisition and interaction module converts the optical signal into an electrical signal and transmits it to the connected data processing module. This receiving process completes the entire transmission loop of the optical signal from emission, modulation to acquisition, providing the raw signal input for subsequent data processing.

[0060] In step S304 of some embodiments, in the data processing module, motion data is calculated based on the probe light and the feedback light to determine the foot motion characterization data matching the optical feature shoe during the movement process.

[0061] It should be noted that in the data processing module, this embodiment performs motion data processing. The data processing module receives the raw parameters of the probe light and the characteristic data of the feedback light from the signal acquisition and interaction module. By comparing and analyzing the differences between the incident light and the reflected light in terms of intensity, spatial distribution, and temporal characteristics, a specific algorithm is used to extract the identification code information and pose change information of the optical feature shoe from the feedback light. Based on the comparative analysis results of the above optical signals, the data processing module determines the foot motion representation data matching the optical feature shoe during the movement process. This data includes multi-dimensional motion parameters such as athlete identification, foot orientation, distance from the ground, and sole inclination angle.

[0062] It should be understood that the above steps constitute a complete embodiment, in which the emission of the probe light provides the physical premise for subsequent modulation, the modulation of the special optical material converts the foot movement state into a transmittable optical signal, the reception of the feedback light establishes the physical channel for signal acquisition, and the motion data processing converts the optical signal into usable digital foot movement representation data. Each step complements the others; the bidirectional light processing capability of the signal acquisition interaction module, the optical modulation characteristics of the special optical material, and the processing algorithm of the data processing module work together to achieve high-precision recognition of field movement foot data under wearable conditions.

[0063] Reference Figure 4 According to some embodiments of this application, step S304, which calculates motion data based on the probe light and the feedback light to determine foot motion characterization data matching the optical feature shoe during movement, may include: Step S401: Obtain the target signal of the probe light corresponding to the probe light beam; Step S402: Amplify the feedback light to obtain the intermediate signal of the feedback light; Step S403: Perform anti-interference processing on the intermediate signal of the feedback light to filter out environmental noise signals and obtain the target signal of the feedback light; Step S404: Motion data calculation is performed based on the probe light target signal and the feedback light target signal to determine the foot motion characterization data of the optical feature shoe.

[0064] In some embodiments, step S401 involves acquiring the target signal of the probe light corresponding to the probe light ray; It should be noted that the data processing module acquires the target signal corresponding to the probe light beam. This target signal records the intensity parameters, wavelength characteristics, and emission timing of the original probe light beam emitted by the signal acquisition and interaction module. Since the probe light beam attenuates to some extent during transmission through the field paving stones, acquiring the target signal serves to establish an original reference standard for the light beam emission, providing a foundation for subsequent comparative analysis with the feedback beam. As a raw signal record without shoe-sole modulation, the target signal reflects the optical characteristics of the system's transmitter and the inherent attenuation characteristics of the transmission medium.

[0065] In step S402 of some embodiments, the feedback light is amplified to obtain an intermediate signal of the feedback light; It should be noted that the feedback light is amplified to obtain the intermediate signal of the feedback light. The feedback light is the echo light after the probe light is modulated by the composite optical pattern on the sole. During its propagation, it undergoes bidirectional penetration attenuation of the mask structure, reflection loss of the sole material, and scattering and absorption by the air medium, resulting in relatively weak light energy received by the optical sensing array. In this embodiment, the feedback light is amplified by converting the weak photocurrent signal into a voltage signal and performing primary gain amplification through a transimpedance amplifier or charge amplifier circuit, so that the signal amplitude reaches the voltage range that the subsequent analog-to-digital conversion circuit can process. However, at this point, the signal is already superimposed with circuit thermal noise, power supply ripple, and interference components from ambient background light.

[0066] In step S403 of some embodiments, anti-interference processing is performed on the intermediate signal of the feedback light to filter out environmental noise signals and obtain the target signal of the feedback light; It should be noted that anti-interference processing is performed on the intermediate signal of the feedback light to filter out environmental noise signals, ultimately yielding the target signal of the feedback light. This anti-interference processing is a multi-level signal purification process. During its formation and transmission, the feedback light not only carries modulation information from the special optical materials of the shoe sole but also contains interference components such as infrared noise from ambient light sources, electromagnetic interference, and thermal noise from the sensor itself. By setting a signal strength threshold or employing a feature pattern matching algorithm, this embodiment identifies and eliminates environmental noise components that do not conform to the expected signal characteristics, retaining the effective optical features generated by the modulation of the composite optical pattern of the shoe sole. This processing ensures that the target signal of the feedback light used in subsequent calculations accurately reflects the true optical characteristics of the shoe sole, avoiding misjudgments caused by environmental interference.

[0067] In some more specific embodiments, anti-interference processing may include, but is not limited to: using bandpass filters to filter out stray ambient light from non-detection bands such as fluorescent lamps and sunlight; using adaptive filtering algorithms or background light cancellation techniques to suppress optical crosstalk noise generated when the LED display array is working; extracting useful signals with the same modulation frequency as the detection light through correlation detection or lock-in amplification techniques; and using digital signal processing algorithms for smoothing and denoising to eliminate transient electrical noise and glitches. It should be understood that after anti-interference processing, the effective optical information reflected by each optical sub-pattern on the sole is retained in the feedback light target signal, while various noise substrates are suppressed.

[0068] In basketball scenarios, interference suppression requires addressing the complex and ever-changing lighting environment of the court and signal disturbances caused by the movement itself. Professional basketball courts are typically equipped with high-brightness LED lighting systems, television broadcast spotlights, and stroboscopic photography equipment. These light sources may contain infrared radiation components with wavelengths similar to the probe light, which can easily interfere with the signal acquisition and interaction module. Therefore, in basketball detection scenarios, modulation and demodulation techniques can be used to modulate the probe light at a specific frequency, enabling the signal acquisition and interaction module to synchronously demodulate the received feedback light at the same frequency. By using lock-in amplification or bandpass filtering, specific frequency signal components can be extracted, effectively suppressing background noise from ambient light sources, particularly DC or low-frequency variations.

[0069] Specifically, the data processing module first controls the signal acquisition and interaction module to modulate the probe light beam at a specific frequency, giving it a specific frequency signature. Then, the signal acquisition and interaction module emits this modulated probe light beam, which passes through the court's tile screen and illuminates the sole of the optical feature shoe. Once the light beam is modulated by the special optical material on the sole to form a feedback beam, the signal acquisition and interaction module receives this feedback beam. At this point, the feedback beam contains infrared noise from ambient light sources such as the LED lighting system and spotlights. Next, the data processing module synchronously demodulates the received signal at the same specific frequency as the emitted signal, extracting the signal components of that specific frequency band through lock-in amplification or bandpass filtering, while simultaneously filtering out DC or low-frequency background noise components from the ambient light sources. Finally, the data processing module obtains a clean feedback light target signal after filtering out environmental noise, which is used for subsequent motion data calculation.

[0070] It should be noted that during basketball games, athletes' shoes may be contaminated with dust, rubber debris, or sweat, which can alter the surface reflectivity of the special optical materials on the soles, leading to unexpected attenuation or scattering of the feedback light intensity. To address this, some embodiments may introduce a reference calibration mechanism in the data processing module. This mechanism utilizes a pre-defined reference reflection area within an identity-coded sub-pattern or pose characterization sub-pattern to monitor changes in the reference intensity of the feedback light in real time. When the reflectivity of a specific area is detected to be significantly lower than the pre-stored static reference data, it is determined to be due to surface contamination rather than a change in ground distance. Based on this, adaptive gain compensation or dynamic threshold adjustment is performed on the feedback light target signal to maintain the accuracy of signal resolution.

[0071] Specifically, a reference reflection area is first preset in the composite optical pattern of the shoe sole, using a special optical material with high reflectivity as the reflection intensity benchmark. Subsequently, during system initialization, static benchmark reflection intensity data of this reference reflection area in a standard pose is acquired and stored. During actual movement, the data processing module monitors the actual reflection intensity of this reference reflection area in the current feedback light in real time. Next, the data processing module compares the actual reflection intensity with the pre-stored static benchmark reflection intensity, calculating the intensity difference. When this difference exceeds a preset contamination threshold, the data processing module determines that dust, sweat, or other contaminants on the sole surface cause abnormal reflectivity attenuation. Then, the data processing module performs adaptive gain compensation on the overall feedback light target signal based on this intensity difference to offset the signal attenuation caused by surface contamination. Finally, the data processing module obtains the compensated feedback light target signal, ensuring the accuracy of pose parameter calculations such as ground clearance.

[0072] To address the potential signal overlap interference that can occur when multiple players are on the court simultaneously during a basketball game, some embodiments employ a processing strategy combining temporal coding and spatial filtering. The signal acquisition and interaction module emits probe light according to a preset time-division multiplexing sequence, ensuring that only feedback signals from shoes with specific optical features are processed within a specific time window, thus avoiding aliasing of multi-target reflected light signals on the sensor array. Furthermore, by utilizing the specific geometric contour features of the identity coding sub-pattern and pose characterization sub-pattern in the composite optical pattern, spatial matching filtering is implemented in the data processing module. This retains only optical signals conforming to the preset pattern shape and size distribution, filtering out edge noise or crosstalk signals generated by adjacent players' shoe soles entering the detection area.

[0073] Specifically, the data processing module first assigns a specific time-division multiplexing timing code to each pair of optical feature shoes, establishing a detection time window sequence for different footwear. Then, the signal acquisition and interaction module emits detection light beams within the specific time window according to this time-division multiplexing timing sequence, ensuring that signal acquisition is performed only for the specific target at any given time. When receiving feedback light, due to temporal isolation, reflected light signals from the soles of other nearby athletes will not overlap with the current target signal in time. Next, the data processing module extracts the geometric contour features of the identity code sub-pattern and pose representation sub-pattern in the feedback light, including the shape parameters and size distribution of the patterns. Then, the data processing module performs spatial matching filtering, retaining only optical signal components that conform to the preset geometric contour features. This spatial filtering process filters out edge noise or crosstalk signals generated by the entry of nearby players' soles into the detection area. Finally, the data processing module obtains the pure feedback light target signal of the specific target optical feature shoe, avoiding signal aliasing caused by multiple athletes being present simultaneously.

[0074] Because basketball involves rapid changes of direction, jumps, and sudden stops, there may be brief obstructions or extreme tilt angles between the shoe sole and the court's tiled surface, causing momentary interruptions or a sharp drop in intensity of the feedback light signal. To address this, a processing method combining multi-frame data fusion and motion prediction can be employed. A temporal buffer mechanism for the feedback light target signal is established in the data processing module. When the signal quality of the current frame is detected to be below the signal-to-noise ratio threshold, motion trajectory prediction is performed by combining footwork representation data from previous frames. Subsequent frames are then used for verification and correction, ensuring the continuity and stability of the footwork representation data output even during rapid movements.

[0075] Specifically, the data processing module first establishes a temporal buffer mechanism for the feedback light target signal, continuously storing the foot motion representation data of the previous frame, including position, velocity, and acceleration parameters. Then, the data processing module detects the signal-to-noise ratio (SNR) of the current frame's feedback light signal to evaluate signal quality. When the SNR falls below a preset quality threshold, the data processing module determines that the current frame has experienced a momentary signal interruption or occlusion due to rapid changes in direction, jumps, or extreme tilting. Next, the data processing module calls the foot motion representation data of the previous frame stored in the temporal buffer mechanism and calculates the foot motion state of the current frame based on a motion trajectory prediction algorithm, including predicted position and attitude parameters. Then, the data processing module continues to receive feedback light signals from subsequent frames. When the signal quality recovers, it uses the actual detection data from subsequent frames to verify and correct the predicted data, eliminating accumulated prediction errors. Finally, the data processing module obtains continuous and stable foot motion representation data output, ensuring the temporal continuity of data during rapid movement.

[0076] In some embodiments, step S404 involves performing motion data calculation based on the probe light target signal and the feedback light target signal to determine the foot motion characterization data of the optical feature shoe.

[0077] It should be noted that motion data is calculated based on the probe light target signal and the feedback light target signal to determine the foot motion representation data of the optical feature shoe. The data processing module compares and analyzes the differences between the two target signals in terms of intensity distribution, spatial pattern, and temporal characteristics. In some embodiments, the feedback light target signal may contain identity coding sub-pattern and pose representation sub-pattern information. By calculating the intensity attenuation, geometric deformation, and positional offset of the feedback light signal relative to the probe light signal, this embodiment calculates the spatial pose parameters and identity information of the sole, ultimately generating foot motion representation data including foot orientation, ground clearance, sole tilt angle, and athlete identity.

[0078] Reference Figure 5 According to some embodiments of this application, a special optical material forms a composite optical pattern on the sole of an optically-featured shoe. The composite optical pattern includes an identity encoding sub-pattern and a pose representation sub-pattern, and the identity encoding sub-pattern and the pose representation sub-pattern have different optical properties. Step S403 performs motion data calculation based on the probe light target signal and the feedback light target signal to determine the foot motion representation data of the optically-featured shoe, which may include: Step S501: Obtain motion characterization benchmark data; Step S502: Perform partitioned signal analysis on the feedback light target signal to obtain the identity encoding light signal and the pose representation light signal; Step S503: Decode the identity-encoded optical signal to obtain the identity identification information corresponding to the optical feature shoe; Step S504: Based on the motion representation reference data, perform pose analysis on the pose representation optical signal to obtain foot pose analysis data; Step S505: Generate foot motion representation data of optical feature shoes based on identity information and foot pose analysis data.

[0079] According to the embodiments of this application, the motion data processing involved in step S403 achieves a complete conversion from raw optical signals to structured motion data through five sequentially connected processing steps. This process takes the probe light target signal and the feedback light target signal as input, and through reference data retrieval, signal partitioning and parsing, identity information decoding, pose parameter calculation, and data fusion generation, finally outputs foot motion representation data containing identity identifiers and spatial posture.

[0080] In some embodiments, step S501 involves acquiring motion characterization reference data; It should be noted that at the beginning of the solution process, the data processing module performs the operation of acquiring motion representation reference data. This reference data is pre-stored in the system storage unit and corresponds to the feedback ray reference data, geometric contour reference information, boundary feature reference data, and relative position reference information of the optical feature shoe's pose representation sub-pattern in the standard pose. The purpose of acquiring motion representation reference data is to establish a reference standard for subsequent pose analysis, enabling the system to quantify and compare the differences between the optical features under actual motion and the standard state, thereby calculating the pose change.

[0081] In step S502 of some embodiments, the feedback light target signal is partitioned and analyzed to obtain the identity encoding light signal and the pose representation light signal; It should be noted that the data processing module performs a partitioned signal analysis operation on the feedback light target signal. Since the composite optical pattern of the shoe sole contains two functional regions with different optical properties—an identity encoding sub-pattern and a pose representation sub-pattern—the feedback light target signal correspondingly contains superimposed optical information from these two sub-patterns. Based on pre-stored pattern layout parameters, the data processing module identifies and separates the region signal corresponding to the identity encoding sub-pattern as the identity encoding light signal within the spatial distribution of the feedback light target signal, and simultaneously extracts the region signal corresponding to the pose representation sub-pattern as the pose representation light signal. This partitioned analysis process utilizes the deterministic relationship between the two sub-patterns in the spatial layout of the composite optical pattern, achieving effective separation of the mixed signal.

[0082] In step S503 of some embodiments, the identity-encoded optical signal is decoded to obtain the identity identification information corresponding to the optical feature shoe; It should be noted that in the identity decoding stage, the data processing module decodes the identity-encoded optical signal. In some embodiments, this process can identify specific dot matrix structures or graphic encoding patterns in the identity-encoded optical signal, and then perform pattern matching with a pre-stored identity encoding template to parse out the unique identity information corresponding to the optical feature shoe. This identity information serves as the athlete's identity tag, associating and binding the subsequently calculated pose data with a specific individual.

[0083] In step S504 of some embodiments, pose analysis is performed on the pose representation optical signal based on motion representation reference data to obtain foot pose analysis data; It should be noted that the data processing module performs pose analysis on the pose representation optical signal based on the acquired motion representation reference data. This process compares the real-time geometric contour, boundary features, and relative position of the pose representation optical signal with the corresponding parameters in the motion representation reference data item by item. By calculating the differences between the real-time signal and the reference data in terms of shape deformation, position offset, and intensity attenuation, the data processing module calculates the rotation angle, vertical distance, and horizontal displacement of the optical feature shoe relative to the standard pose, thus obtaining the foot pose analysis data.

[0084] In some embodiments, step S505 generates foot motion representation data of the optical feature shoe based on the identity information and foot pose analysis data.

[0085] It should be noted that the data processing module performs data integration, generating foot motion representation data for the optical feature shoe based on the athlete's identification information and foot pose analysis data. This data, in a structured form, simultaneously contains the athlete's identification and the spatial pose parameters of both feet, completing a full embodiment from raw optical signal acquisition to usable motion information output. In this embodiment, the motion representation reference data is used to support regional positioning during partition analysis and directly participates in pose analysis calculation; the identification encoded optical signal and the pose representation optical signal respectively support the two functional dimensions of identification recognition and pose detection; and the fusion of foot pose analysis data and identification information reflects the characteristics of integrated encoding of composite optical patterns.

[0086] Reference Figure 6 According to some embodiments of this application, step S501, obtaining motion characterization reference data, may include: Step S601: Determine the standard pose of the optical feature shoe; Step S602: Collect static reference data of pose representation sub-pattern corresponding to standard pose; Step S603: Generate motion characterization reference data based on the static reference data of the standard pose; In step S504, pose analysis is performed on the pose representation optical signal based on the motion representation reference data to obtain foot pose analysis data, which may include: Step S604: Determine the pose pattern representation data of the pose representation sub-pattern based on the pose representation light signal. Step S605: Perform pose analysis based on static reference data and pose pattern representation data to obtain foot pose analysis data.

[0087] In some embodiments, step S601 involves determining the standard pose of the optical feature shoe; It should be noted that in the process of establishing motion characterization benchmark data, the standard pose of the optical feature shoe must first be determined. This standard pose serves as the reference benchmark for all subsequent pose measurements and analyses. It is usually defined as the geometric state in which the sole is horizontally attached to the surface of the court tile screen, facing the signal acquisition and interaction module, and with zero distance from the ground. This state provides the system with a unified coordinate reference system and measurement origin.

[0088] In some embodiments, to address the high-intensity competition and complex technical movements required in basketball, a multi-dimensional calibration strategy can be implemented for the standard pose determination process. Since basketball players not only stand with both feet horizontally on the ground during actual games, but also exhibit numerous transitional postures with shoe soles tilted, such as during jump preparation and defensive sliding, the standard pose determination for the optical feature shoe can be supplemented by establishing a horizontal standard pose where the sole is completely horizontally in contact with the court tiles, as well as a jump preparation standard pose and a defensive sliding standard pose where the sole is at a specific angle to the tiles. Furthermore, considering that the left and right feet bear different force functions and trajectories in basketball, the standard poses for the left and right feature shoes need to be determined independently, establishing differentiated coordinate reference systems for the left and right feet. This determination of multiple standard poses provides diverse reference states for subsequent static baseline data acquisition, enabling the system to match corresponding baseline data for comparison based on different technical movements, thus improving the relevance and accuracy of pose analysis.

[0089] Specifically, to address the multi-standard posture requirements in basketball scenarios, a multi-dimensional calibration and intelligent matching implementation can be constructed. This embodiment first determines the horizontal standard posture, take-off preparatory posture, and defensive sliding posture of the left and right characteristic shoes based on the technical movement characteristics of basketball, establishing differentiated coordinate reference systems. Then, for each standard posture, the signal acquisition and interaction module emits a probe light and receives feedback light modulated by the shoe sole posture representation sub-pattern, acquiring static reference data corresponding to each standard posture, including feedback light reference data, geometric contour reference information, boundary feature reference data, and relative position reference information. Next, the data processing module generates multi-level motion representation reference data based on this static reference data, constructing a reference database containing horizontal states, tilted take-off states, and sliding states. In actual motion monitoring, after the data processing module determines the pose pattern representation data based on the pose representation light signal, it first identifies the characteristics of the current action mode, intelligently matches the closest standard pose data group from the multi-level motion representation reference data, and then performs pose analysis based on the matched static reference data to obtain foot pose analysis data adapted to the current basketball technique.

[0090] In step S602 of some embodiments, static reference data corresponding to the standard pose of the pose representation sub-pattern is acquired. It should be noted that, after determining the standard pose, this embodiment of the application collects static reference data corresponding to the pose representation sub-pattern in the standard pose. This data specifically includes feedback light reference data (i.e., the intensity distribution and spectral characteristics of the reflected light from the pose representation sub-pattern in the standard pose), geometric contour reference information (i.e., the standard projection shape and size of the pattern on the infrared light sensor array), boundary feature reference data (i.e., the gradient changes, corner distribution, and contour curvature of the pattern edges), and relative position reference information between the pose representation sub-pattern and the signal acquisition interaction module (i.e., the standard coordinate position of the pattern centroid or specific feature points relative to the reference origin of the sensor array). This static reference data completely records the optical fingerprint and spatial characteristics of the pose representation sub-pattern in the standard state.

[0091] In some embodiments, during the static reference data acquisition stage, a multi-region, multiple acquisition and adaptive compensation mechanism can be implemented to address the impact of basketball court temperature changes and shoe sole wear on optical properties. Specifically, due to the frequent friction between the shoe sole and the court during basketball play, which causes changes in the surface roughness of special optical materials, this embodiment should select multiple feature sampling points on the pose representation sub-pattern of the shoe sole for multiple optical feature acquisitions, and calculate the average reflection intensity and geometric parameters to eliminate measurement errors caused by local material inhomogeneities. Additionally, the current court ambient temperature data should be recorded during the acquisition process, and a temperature-reflectivity compensation model should be established to correct for temperature drift in the feedback light reference data. Furthermore, considering that shoe sole wear after long-term use causes changes in the geometric dimensions of the pattern, this embodiment should set a periodic re-acquisition mechanism to automatically trigger the update acquisition of static reference data when abnormal reflectivity or geometric contour deviation exceeds a threshold. These optimized static reference data provide high-quality raw input for generating motion characterization reference data, ensuring the timeliness and accuracy of the reference data.

[0092] To address the issues of environmental fluctuations and shoe sole wear in basketball courts, an embodiment of temperature compensation and adaptive calibration can be constructed. First, while acquiring static reference data corresponding to the standard pose of the pose representation sub-pattern, this embodiment simultaneously acquires the current court ambient temperature data, establishing a temperature-reflectivity correlation model. Subsequently, multiple feature sampling points are selected on the shoe sole pose representation sub-pattern for multiple optical feature acquisitions, calculating the average reflection intensity and geometric parameters to generate statistically optimized static reference data. Next, the data processing module generates motion representation reference data based on the static reference data of the standard pose and sets periodic re-acquisition trigger conditions. During system operation, the data processing module continuously monitors the reflection intensity and geometric contour stability of the pose representation light signal. When abnormal attenuation of reflectivity or geometric contour deviation exceeding a preset threshold is detected, it is determined to be caused by wear of the special optical materials on the shoe sole or temperature drift, automatically triggering the re-acquisition process, updating the static reference data, and correspondingly correcting the motion representation reference data. Based on the updated motion representation reference data, the data processing module performs pose analysis on the real-time acquired pose pattern representation data, ensuring that the foot pose analysis data can compensate for measurement errors caused by material aging and environmental changes.

[0093] In some embodiments, step S603 generates motion characterization reference data based on static reference data of standard pose. It should be noted that, in this embodiment of the application, the static reference data of the standard pose described above is integrated and processed to generate motion characterization reference data. This data is stored as a fixed reference standard in the storage unit of the data processing module and is used for subsequent quantitative comparison with the optical features under actual motion conditions.

[0094] In some embodiments, during the generation of motion representation benchmark data, a multi-level motion representation benchmark database can be established to address the multi-posture requirements of basketball. This database not only includes benchmark data under standard horizontal postures but also benchmark data sets under specific tilt angle states such as jump preparation and defensive slides, forming a benchmark data set covering major basketball technical movements. Furthermore, a dynamic benchmark data update mechanism is introduced. By comparing previously collected static benchmark data, the wear trend of the special optical materials on the shoe sole is detected, and the geometric contour benchmark information and boundary feature benchmark data are automatically corrected to compensate for signal attenuation caused by material aging. This multi-level and dynamically maintained motion representation benchmark data provides rich reference templates and adaptive correction capabilities for subsequent posture analysis, enabling the system to automatically match the closest benchmark data set for comparison based on the current motion state.

[0095] In some embodiments, step S604 involves determining the pose pattern representation data of the pose representation sub-pattern based on the pose representation light signal. It should be noted that during the pose analysis of the pose representation optical signal based on the motion representation reference data, the data processing module first determines the pose pattern representation data of the pose representation sub-pattern based on the received pose representation optical signal. This data reflects the actual optical characteristics of the pose representation sub-pattern in the current motion state in real time, including the real-time feedback light intensity distribution, geometric contour shape, boundary features, and relative position coordinates on the sensor array.

[0096] In some embodiments, during the determination of pose pattern representation data, a multi-frame fusion and quality assessment strategy can be implemented to address motion blur and signal occlusion issues that may occur during rapid changes of direction and jumps in basketball. Because the sudden stops and jump shots or rapid breakthroughs of basketball players result in extremely high relative speeds between the shoe sole and the signal acquisition module, the pose representation optical signal acquired in a single instance may exhibit image blurring or feature point drift. Therefore, the data processing module should continuously acquire multiple frames of pose representation optical signals, perform multi-frame overlay and sharpening processing using image registration algorithms, and eliminate the impact of motion blur on geometric contour extraction. Simultaneously, the signal-to-noise ratio and feature point integrity of each frame are evaluated in real time. When partial occlusion or insufficient signal quality is detected, a prediction compensation mechanism based on previous frame data is activated, using the temporal continuity of foot movement to infer the pose pattern representation data of the current frame. This enhanced pose pattern representation data retains more complete geometric and positional features, providing reliable real-time input for subsequent pose analysis.

[0097] To address signal quality issues caused by high-speed movements in basketball, a multi-frame fusion and prediction compensation implementation method can be constructed. First, the data processing module establishes a temporal buffer mechanism for pose representation optical signals, continuously acquiring multiple frames of pose representation optical signals. Then, for each frame, signal-to-noise ratio evaluation and feature point integrity detection are performed. When motion blur or partial occlusion due to rapid changes in direction or jumps is detected in the current frame, that frame is marked as low-quality data. Next, for high-quality frames, the data processing module performs multi-frame overlay and sharpening processing using an image registration algorithm to eliminate the impact of motion blur on geometric contour extraction and enhance the clarity of the pose pattern representation data. For low-quality frames, the data processing module calls the foot pose analysis data of the previous frame stored in the temporal buffer mechanism, performs motion trajectory prediction based on the basketball foot kinematics model, and calculates the predicted pose pattern representation data for the current frame. Then, the data processing module compares the enhanced actual pose pattern representation data or the predicted pose pattern representation data with static reference data, and obtains the pose analysis result by calculating geometric deformation and position offset. Finally, the result is smoothed by a multi-frame data fusion algorithm to generate temporally continuous foot pose analysis data, effectively suppressing data jumps caused by momentary occlusion or signal interruption.

[0098] In step S605 of some embodiments, pose analysis is performed based on static reference data and pose pattern representation data to obtain foot pose analysis data.

[0099] It should be noted that the data processing module performs pose analysis based on the pre-stored static reference data and the real-time acquired pose pattern representation data. By calculating the differences between the two in terms of geometry, spatial position and light intensity distribution, the pose change parameters of the optical feature shoe relative to the standard pose are calculated, and foot pose analysis data including foot orientation angle, distance from the ground and inclination of the sole are obtained.

[0100] It should be understood that the above steps constitute a complete embodiment from baseline establishment to real-time analysis. In the baseline establishment phase, determining the standard pose provides a unified coordinate reference system for measurement. Static baseline data is collected, recording the optical fingerprint of the pose representation sub-pattern in the reference state. Generating motion representation baseline data integrates multi-dimensional baseline information to form callable data. In the real-time analysis phase, the pose pattern representation data extracts a snapshot of the optical features at the current moment. Pose analysis is performed by comparing the differences between real-time data and static baseline data to quantify and calculate the actual pose offset. Static baseline data is used throughout the entire embodiment, generated and recorded in the acquisition phase and called for comparison in the analysis phase, ensuring the accuracy and traceability of pose measurement. The pose representation sub-pattern, as a carrier of physical information, has its optical properties calibrated in the standard state and detected in real-time in motion. Through the analysis of differences between the preceding and following states, high-precision measurements of parameters such as foot orientation, distance from the ground, and tilt angle are achieved.

[0101] In some embodiments, during the pose analysis stage, kinematic model constraints and outlier removal mechanisms can be introduced to address the unique biomechanical characteristics of basketball. Since basketball players' footwork follows human kinematics principles and involves specific movement patterns such as jumping, landing, and turning, the data processing module should incorporate a basketball footwork kinematic model as a constraint when comparing static baseline data with pose pattern representation data to verify the rationality of the analyzed pose parameters. For example, if the analysis result shows that the sole inclination angle exceeds the physiological limits of the human body or the rate of change of distance from the ground violates the gravitational acceleration constraint, it is identified as an outlier and triggers re-analysis or interpolation using previous valid data. Simultaneously, multi-frame data fusion analysis is implemented, temporally associating the pose pattern representation data of the current frame with the data of previous and subsequent frames. Kalman filtering or particle filtering algorithms are used to smooth the pose analysis results, eliminating measurement noise caused by court vibrations or momentary occlusion. These optimization measures ensure that, even in high-intensity basketball scenarios, the foot pose analysis data maintains physical rationality and temporal continuity, accurately reflecting the athlete's actual movement state.

[0102] To address signal quality issues caused by high-speed movements in basketball, a multi-frame fusion and prediction compensation implementation method can be constructed. First, the data processing module establishes a temporal buffer mechanism for pose representation optical signals, continuously acquiring multiple frames of pose representation optical signals. Then, for each frame, signal-to-noise ratio evaluation and feature point integrity detection are performed. When motion blur or partial occlusion due to rapid changes in direction or jumps is detected in the current frame, that frame is marked as low-quality data. Next, for high-quality frames, the data processing module performs multi-frame overlay and sharpening processing using an image registration algorithm to eliminate the impact of motion blur on geometric contour extraction and enhance the clarity of the pose pattern representation data. For low-quality frames, the data processing module calls the foot pose analysis data of the previous frame stored in the temporal buffer mechanism, performs motion trajectory prediction based on the basketball foot kinematics model, and calculates the predicted pose pattern representation data for the current frame. Then, the data processing module compares the enhanced actual pose pattern representation data or the predicted pose pattern representation data with static reference data, and obtains the pose analysis result by calculating geometric deformation and position offset. Finally, the result is smoothed by a multi-frame data fusion algorithm to generate temporally continuous foot pose analysis data, effectively suppressing data jumps caused by momentary occlusion or signal interruption.

[0103] Reference Figure 7 According to some embodiments of this application, step S602, which involves acquiring static reference data corresponding to the standard pose of the pose representation sub-pattern, may include: Step S701: Collect the feedback ray reference data and geometric contour reference information of the pose representation sub-pattern corresponding to the standard pose, as well as the relative position reference information between the pose representation sub-pattern and the signal acquisition interaction module under the standard pose. Step S702: Integrate the feedback ray reference data, geometric contour reference information, and relative position reference information to obtain static reference data; In step S605, pose analysis is performed based on static reference data and pose pattern representation data to obtain foot pose analysis data, which may include: Step S703: Extract the real-time data of the feedback light, the real-time information of the geometric contour, and the real-time information of the relative position between the pose representation sub-pattern and the signal acquisition interaction module during the motion from the pose pattern representation data. Step S704: Based on the reference data of the feedback light and the real-time data of the feedback light, perform orientation analysis to determine the distance between the sole of the optical feature shoe and the ground. Step S705: Based on real-time geometric contour information and geometric contour reference information, perform morphological analysis to determine the sole inclination data of the optical feature shoe; Step S706: Based on the real-time relative position information and the relative position reference information, perform motion trend analysis to determine the motion trend characterization data of the optical feature shoe; Step S707: Generate foot pose analysis data based on the distance of the sole from the ground, the inclination angle of the sole, and the motion trend representation data.

[0104] In some embodiments, step S701 involves acquiring feedback ray reference data and geometric contour reference information of the pose representation sub-pattern corresponding to the standard pose, as well as the relative position reference information between the pose representation sub-pattern and the signal acquisition interaction module under the standard pose. It should be noted that the process of acquiring static reference data of the pose representation sub-pattern corresponding to the standard pose first involves the acquisition of multi-dimensional optical features. Specifically, in this embodiment, the feedback light reference data of the pose representation sub-pattern in the standard pose is acquired through a signal acquisition interaction module. This data records the intensity distribution and spectral characteristics of the reflected light in the standard state. Simultaneously, geometric contour reference information is acquired, reflecting the standard projection shape and size of the pose representation sub-pattern on the infrared sensor array. Furthermore, relative position reference information between the pose representation sub-pattern and the signal acquisition interaction module in the standard pose is acquired, which determines the standard coordinate position of the pattern's centroid or feature points relative to the sensor array's reference origin. These reference data characterize the physical features of the pose representation sub-pattern in the reference state from three dimensions: optical properties, geometric shape, and spatial position.

[0105] In step S702 of some embodiments, feedback ray reference data, geometric contour reference information and relative position reference information are integrated to obtain static reference data; It should be noted that the embodiments of this application integrate the above-mentioned feedback light reference data, geometric contour reference information and relative position reference information to obtain complete static reference data. This data is stored in the data processing module as a fixed reference standard and is used for subsequent quantitative comparison with the optical features under actual motion conditions.

[0106] In some embodiments, step S703 extracts real-time feedback light data, real-time geometric contour information, and real-time relative position information between the pose representation sub-pattern and the signal acquisition interaction module during the motion from the pose pattern representation data. It should be noted that during actual motion monitoring, the data processing module extracts three types of real-time information from the pose pattern representation data, corresponding to the static reference data. These include real-time feedback light data, which reflects the actual reflected light intensity distribution of the pose representation sub-pattern under the current motion state; real-time geometric contour information, which records the actual projected shape of the pattern on the sensor array in the current posture; and real-time relative position information between the pose representation sub-pattern and the signal acquisition interaction module during motion, which identifies the real-time coordinates of the pattern's centroid or feature points. These three types of real-time data constitute a complete description of the current optical state of the pose representation sub-pattern, providing real-time input for subsequent pose analysis.

[0107] In some embodiments, step S704 involves performing orientation analysis based on the feedback light reference data and the feedback light real-time data to determine the distance between the sole of the optical feature shoe and the ground. It should be noted that the data processing module first performs a azimuth resolution operation. This process compares the reference data and real-time data of the feedback light, utilizing the inverse square law governing infrared light propagation. By calculating the attenuation ratio of the real-time reflected light intensity relative to the reference intensity in the standard pose, a mathematical mapping relationship between light intensity and propagation distance is established, thereby determining the ground clearance of the sole of the optical feature shoe. This resolution process relies on the physical law of light intensity changing with distance, transforming the quantization differences of the optical signal into quantization parameters of spatial position.

[0108] In some embodiments, step S705 involves performing morphological analysis based on real-time geometric contour information and geometric contour reference information to determine the sole inclination data of the optical feature shoe. It should be noted that the data processing module performs morphological analysis. This process involves morphological comparison between real-time geometric contour information and reference geometric contour information. When the sole of the optical feature shoe tilts or rolls, the projection of the pose representation sub-pattern onto the sensor array undergoes stretching, compression, or perspective distortion. By calculating the affine transformation matrix or projection transformation parameters between the real-time geometric contour and the reference geometric contour, this embodiment calculates the tilt angle of the sole relative to the horizontal plane, determining the sole tilt angle data of the optical feature shoe. This analysis process utilizes the principles of optical projection geometry to convert shape deformation into pose angle.

[0109] In some embodiments, step S706 involves analyzing motion trends based on real-time relative position information and relative position reference information to determine motion trend characterization data for the optical feature shoe. It should be noted that the data processing module performs motion trend analysis. This process compares the spatial position based on real-time relative position information and relative position reference information. By calculating the coordinate offset of the pattern's center of gravity or feature points from the standard pose at the current moment, and combining this with time series analysis, the direction and velocity trend of the optical feature shoe in the horizontal plane are determined, generating motion trend characterization data. This analysis process reflects the trajectory characteristics of the athlete's footwork through the temporal changes in spatial displacement.

[0110] In some embodiments, step S707 generates foot pose analysis data based on the distance of the sole from the ground, the inclination of the sole, and the motion trend representation data.

[0111] It should be noted that the data processing module integrates and generates foot pose analysis data for optical feature shoes based on the ground clearance, sole tilt angle, and motion trend representation data obtained from the above analysis. This data fully describes the position, posture, and motion state of the sole in three-dimensional space, realizing the conversion from raw optical signals to structured motion parameters. Throughout this embodiment, the comparison between the feedback ray reference data and the real-time feedback ray data supports the measurement of vertical distance; the comparison between the geometric contour reference information and the real-time geometric contour information supports the calculation of tilt angle; and the comparison between the relative position reference information and the real-time relative position information supports the determination of horizontal motion trend. The one-to-one correspondence between the three types of reference data and the real-time data ensures the multi-dimensional accuracy and physical consistency of pose analysis.

[0112] Reference Figure 8 , Figure 8 This illustration shows the hardware structure of an electronic device according to another embodiment. The electronic device may include: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 to execute the foot movement data recognition method of the embodiments of this application. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0113] This application also provides a computer program product, which includes a computer program. The processor of a computer device reads and executes the computer program, causing the computer device to perform the aforementioned foot movement data recognition method.

[0114] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “including,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.

[0115] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0116] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself.

[0117] In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0120] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium may include: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code.

[0121] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0122] The above is a detailed description of the embodiments of this disclosure. However, this disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this disclosure. All such equivalent modifications or substitutions are included within the scope defined by the claims of this disclosure.

Claims

1. A foot movement data recognition system, characterized in that, include: A signal acquisition and interaction module is installed under the ground tile screen of the target site; wherein, the signal acquisition and interaction module is used to emit detection light that can pass through the ground tile screen, and to receive feedback light formed by modulating the detection light. An optical feature shoe, wherein the sole of the optical feature shoe is made of a special optical material; wherein the special optical material is used to modulate the detection light to form the feedback light; A data processing module is connected to the signal acquisition and interaction module. The data processing module is used to perform motion data calculation based on the detection light and the feedback light to determine the foot motion characterization data matching the optical feature shoe during the movement process.

2. The footstep movement data recognition system according to claim 1, characterized in that, The special optical material forms a composite optical pattern on the sole of the optical feature shoe. The composite optical pattern includes an identity coding sub-pattern and a pose representation sub-pattern, and the identity coding sub-pattern and the pose representation sub-pattern have different optical properties.

3. The footstep movement data recognition system according to claim 2, characterized in that, The identity coding sub-pattern is arranged in the forefoot area and heel area of ​​the sole of the optical feature shoe; the pose representation sub-pattern is arranged in the waist area of ​​the sole of the optical feature shoe.

4. The foot movement data recognition system according to claim 3, characterized in that, The composite optical pattern further includes a directional characterization sub-pattern, which is disposed in the toe area of ​​the sole of the optical feature shoe.

5. The foot movement data recognition system according to claim 2, characterized in that, The signal acquisition and interaction module includes an LED display array and an optical sensor array. The LED display array is used to emit detection light that can pass through the ground tile screen, and the optical sensor array is used to receive feedback light formed by modulating the detection light. The identity coding sub-pattern includes an identity recognition area formed by arranging multiple coding graphics, and the graphic size of each coding graphic is an interactive adaptation size; wherein, the interactive adaptation size is jointly determined by the lamp bead arrangement density of the lamp bead display array and the sensor arrangement density of the optical sensing array.

6. The foot movement data recognition system according to claim 2, characterized in that, The foot movement data recognition system is equipped with multiple pairs of optical feature shoes; wherein, each pair of optical feature shoes is divided into a left feature shoe and a right feature shoe, and the composite optical pattern of the left feature shoe and the composite optical pattern of the right feature shoe have a matching pattern relationship.

7. A method for recognizing foot movement data, characterized in that, The method applied to the foot movement data recognition system of claim 1 includes: The control signal acquisition and interaction module emits detection light that passes through the site's floor tile screen; During the movement of the optical feature shoe, the detection light is modulated by a special optical material on the sole of the shoe to form a feedback light; The feedback light is received through the signal acquisition and interaction module; In the data processing module, motion data is calculated based on the detection light and the feedback light to determine the foot motion characterization data matching the optical feature shoe during the movement process.

8. The foot movement data recognition method according to claim 7, characterized in that, The step of calculating motion data based on the detected light and the feedback light to determine the foot motion representation data matching the optical feature shoe during movement includes: Obtain the target signal of the probe light corresponding to the probe light ray; The feedback light beam is amplified to obtain the intermediate signal of the feedback light; The intermediate feedback light signal is subjected to anti-interference processing to filter out environmental noise signals, thereby obtaining the target feedback light signal. Motion data is calculated based on the detection light target signal and the feedback light target signal to determine the foot motion characterization data of the optical feature shoe.

9. The foot movement data recognition method according to claim 8, characterized in that, The special optical material forms a composite optical pattern on the sole of the optical feature shoe. The composite optical pattern includes an identity coding sub-pattern and a pose representation sub-pattern, and the identity coding sub-pattern and the pose representation sub-pattern have different optical properties. The process of calculating motion data based on the probe light target signal and the feedback light target signal to determine the foot motion characterization data of the optical feature shoe includes: Obtain motion characterization benchmark data; The feedback light target signal is partitioned and analyzed to obtain the identity encoding light signal and the pose representation light signal; The identity-encoded optical signal is decoded to obtain the identity information corresponding to the optical feature shoe; Based on the motion representation reference data, the pose representation optical signal is analyzed to obtain foot pose analysis data. Based on the identity information and the foot pose analysis data, the foot movement representation data of the optical feature shoe is generated.

10. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the foot movement data recognition method as described in any one of claims 7 to 9.

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