An intelligent positioning system and method for facilitating the adjustment of a patient's standing posture

Abstract

The present invention discloses an intelligent positioning system and method for facilitating the adjustment of a patient's standing posture, which relates to the field of intelligent medical technology and includes the following steps: initial posture assessment and skeleton modeling, calculation of the center of gravity and support point distribution, detection of spinal curvature and inclination, intelligent laser-assisted posture adjustment, dynamic muscle activation feedback monitoring, alignment correction of the pelvis and lower limbs, posture balance prediction and guidance, correction of upper limb posture and scapular stability, real-time adjustment driven by biofeedback, and long-term posture optimization and habit reshaping, and a system is constructed based on the above method. The intelligent positioning system and method for facilitating the adjustment of a patient's standing posture, through the setting of combining multi-modal data acquisition, a dynamic feedback mechanism, and biomechanical analysis, realizes the precise detection, real-time adjustment, and long-term optimization of the patient's standing posture problems. It effectively solves the problems of low static assessment accuracy, lack of dynamic feedback, and poor long-term effects in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical technology, and particularly to an intelligent positioning system and method for facilitating the adjustment of a patient's standing posture. Background Technique

[0002] Human standing posture is one of the extremely important postures in daily life. Poor standing postures not only affect appearance but also cause long-term damage to parts such as the spine, muscles, and joints, thereby triggering chronic pain, bone degeneration, and other health problems. Most of the existing standing posture adjustment techniques rely on traditional static observation and subjective evaluation. For example, through the naked eye observation of a physical examination doctor, using simple measuring tools (such as a level) or relying on the patient's self-perception. However, these methods have limitations such as low accuracy, lack of real-time feedback, and difficulty in dynamic adjustment. Especially when facing complex three-dimensional standing posture problems (such as scoliosis and pelvic tilt), traditional methods are difficult to provide effective guidance. In addition, although some intelligent auxiliary devices introduce sensor and machine learning technologies, they mostly focus on data collection and visual analysis, lacking dynamic feedback and personalized correction capabilities, resulting in great difficulty for patients to adjust and low efficiency in forming habits.

[0003] In response to the above deficiencies, new technologies such as depth cameras, surface electromyography (EMG) sensors, laser projection, and augmented reality (AR) have been developed in recent years. However, most of these technologies are used independently and have not formed a systematic standing posture adjustment scheme. Especially when detecting complex biomechanical parameters such as three-dimensional curvature of the spine, center of gravity distribution, and muscle activation patterns, there is a lack of a complete system that can integrate multi-dimensional data, provide real-time feedback, and offer intuitive guidance. In addition, the existing methods provide weak support for the long-term habit formation of patients, easily leading to problems where short-term correction is effective but the long-term effect is not ideal. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent positioning system and method for facilitating the adjustment of a patient's standing posture to solve the problems raised in the above background technique.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In the first aspect, an embodiment of the present invention provides an intelligent positioning method for facilitating the adjustment of a patient's standing posture, including the following steps:

[0007] S1. Initial posture evaluation and skeleton modeling;

[0008] S2. Calculation of the center of gravity and support point distribution;

[0009] S3. Detection of spinal curvature and inclination;

[0010] S4. Intelligent laser-assisted posture adjustment;

[0011] S5, Dynamic Muscle Activation Feedback Monitoring;

[0012] S6, Pelvis and Lower Limb Alignment Correction;

[0013] S7, Posture Balance Prediction and Guidance;

[0014] S8, Upper Limb Posture and Scapula Stability Correction;

[0015] S9, Biofeedback-Driven Real-Time Adjustment;

[0016] S10, Long-Term Posture Optimization and Habit Remodeling.

[0017] To further optimize this technical solution, in step S1, the overall standing posture data of the patient is collected by a high-precision 3D depth camera or a laser scanner, and a human skeleton model is established;

[0018] The multi-view capture technology is used to determine the bone joint positions, joint angles, and body posture distributions;

[0019] Combined with the human anatomy database, the stability of the current standing posture and the deviation from the standard standing posture are inferred through machine learning algorithms, providing benchmark data for subsequent adjustments.

[0020] To further optimize this technical solution, in step S2, based on the patient's human skeleton model and combined with the data of the sole pressure sensors, the projection position of the center of gravity on the sole support surface is calculated, and the uniformity and asymmetry of the sole force during standing are determined.

[0021] To further optimize this technical solution, in step S3, through an intelligent posture analysis algorithm, the three-dimensional curvature of the patient's spine is detected, including the physiological curvatures in the anteroposterior direction, including thoracic kyphosis and lumbar lordosis, and the lateral curvature in the left-right direction.

[0022] To further optimize this technical solution, an intelligent posture analysis model is constructed in the intelligent posture analysis algorithm, based on the spatial curvature distribution of the spine, and optimized by introducing multi-modal sensor data;

[0023] The intelligent posture analysis model includes a three-dimensional spine curvature formula, a spine lateral inclination formula, and a bending anomaly index;

[0024] The three-dimensional spine curvature formula is used to describe the degree of curvature of the spine at different positions;

[0025] The spine lateral inclination formula is used to directly reflect whether there is an overall left-right lateral deviation problem in the spine;

[0026] The bending anomaly index is used to comprehensively reflect the abnormal distribution of the entire spine.

[0027] To further optimize this technical solution, in the intelligent posture analysis model:

[0028] Assume that the spine consists of multiple key points, labeled as cervical vertebra C1 to coccyx S5, and the three-dimensional coordinates of each key point are collected through sensor data, denoted as , where represents the key point serial number;

[0029] The three-dimensional curvature of the spine The formula is as follows:

[0030] ;

[0031] Among them,

[0032] is the spine length parameter, the cumulative distance from the cervical vertebra to the coccyx;

[0033] is the unit tangent vector of the spine, is the first derivative of the tangent vector;

[0034] is the unit normal vector of the spine, is the first derivative of the normal vector;

[0035] represents the modulus of the vector;

[0036] The curvature of the standard spine has a known normal range in specific regions, including the thoracic kyphosis region;

[0037] The formula for the spinal scoliosis angle includes:

[0038] The spinal scoliosis angle describes the inclination of the spine in the left-right direction and is defined as:

[0039] ;

[0040] Among them,

[0041] are the horizontal and vertical coordinates of the cervical vertebra vertex respectively;

[0042] are the horizontal and vertical coordinates of the coccyx bottom;

[0043] In the curvature anomaly index:

[0044] Define an anomaly index to quantify the anomaly degree of a certain section of the spine:

[0045] ;

[0046] Among them, is the standard curvature value corresponding to each key point.

[0047] To further optimize this technical solution, in step S4, by arranging an intelligent laser projector in the patient's standing area, the human skeleton model of the target standing posture is projected onto the patient's body surface in the form of a grid. The patient observes the deviation of the grid lines and actively adjusts the body until it aligns with the projection.

[0048] To further optimize this technical solution, in step S5, through the surface electromyogram (EMG) sensor, the activation patterns of each major muscle group during the patient's posture adjustment process are monitored to determine whether there are compensatory movements or incorrect muscle force application patterns.

[0049] To further optimize this technical solution, in steps S6 and S8, through a real-time posture feedback algorithm, the pelvic tilt and rotation are detected to guide the patient to achieve pelvic alignment by fine-tuning the foot stance and leg muscle force application, and the position of the patient's upper limbs and the stability of the scapulae are monitored to adjust incorrect shoulder postures to avoid the influence of poor standing postures.

[0050] An intelligent positioning system for facilitating the adjustment of a patient's standing posture is constructed based on the above-mentioned intelligent positioning method for facilitating the adjustment of a patient's standing posture. The system includes a data acquisition and modeling module, a posture analysis and positioning module, a dynamic calibration and guidance module, and a memory reinforcement module;

[0051] The data acquisition and modeling module is used to collect the standing posture data of the patient and generate a high-precision human skeleton model, providing a basis for analysis and correction;

[0052] The posture analysis and positioning module is used to analyze the patient's standing posture data in real time and identify abnormal spinal curvatures, center of gravity shifts, and other posture problems;

[0053] The dynamic calibration and guidance module is used to guide the patient to gradually adjust the standing posture to the correct position through intuitive visualization or dynamic prompts;

[0054] The memory reinforcement module is used to help the patient form a neuromuscular memory of the correct standing posture through long-term training to achieve continuous posture optimization.

[0055] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of an intelligent positioning system and method for facilitating the adjustment of a patient's standing posture as described in the first aspect of the present invention are implemented.

[0056] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of an intelligent positioning system and method for facilitating the adjustment of a patient's standing posture as described in the first aspect of the present invention are implemented.

[0057] Compared with the prior art, the present invention provides an intelligent positioning system and method for facilitating the adjustment of a patient's standing posture, having the following beneficial effects:

[0058] The intelligent positioning system and method for facilitating the adjustment of a patient's standing posture, through the setting of combining multi-modal data collection, a dynamic feedback mechanism, and biomechanical analysis, realizes the accurate detection, real-time adjustment, and long-term optimization of the patient's standing posture problems. It effectively solves the problems of low static evaluation accuracy, lack of dynamic feedback, and poor long-term effect in the prior art, and provides an intuitive, scientific, and easy-to-operate standing posture correction solution for patients.

[0059] At the same time, through neuromuscular memory training, it ensures that the patient forms habitual muscle memory of the correct standing posture during the adjustment process, thereby greatly improving the long-term effect of standing posture correction and improving the health level and quality of life. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0061] Figure 1 It is a flowchart of a method for an intelligent positioning system for facilitating the adjustment of a patient's standing posture proposed by the present invention;

[0062] Figure 2 It is a flowchart of an intelligent posture analysis model in a method for an intelligent positioning method for facilitating the adjustment of a patient's standing posture proposed by the present invention;

[0063] Figure 3 It is a structural diagram of an intelligent positioning system for facilitating the adjustment of a patient's standing posture proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0065] In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0066] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures, or characteristics that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0067] Embodiment 1:

[0068] Referring to Figures 1 to 2 , which is the first embodiment of the present invention. This embodiment provides an intelligent positioning method for facilitating the adjustment of a patient's standing posture, including the following steps:

[0069] S1. Initial posture assessment and skeleton modeling

[0070] In this embodiment, the overall standing posture data of the patient is collected through a high-precision 3D depth camera or a laser scanner, and a human skeleton model is established;

[0071] The multi-view capture technology is used to determine the bone joint positions, joint angles, and postural distributions;

[0072] Combined with the human anatomy database, the stability of the current standing posture and the deviation from the standard standing posture are inferred through machine learning algorithms, providing benchmark data for subsequent adjustments.

[0073] Specifically, dynamic modeling is achieved through multi-point tracking, which can identify the natural standing posture of the patient in the daily state, rather than the "corrected" state after deliberate adjustment. The model also needs to be updated in real time dynamically to adapt to the patient's posture changes during subsequent adjustments.

[0074] S2. Calculation of the center of gravity and the distribution of support points

[0075] In this embodiment, through the patient's human skeleton model and combined with the data of the sole pressure sensors, the projection position of the center of gravity on the sole support surface is calculated, and at the same time, the uniformity and asymmetry of the force on the soles during standing are determined.

[0076] The stability of a person standing highly depends on whether the center of gravity is within the support surface, especially for patients with standing posture problems. By capturing the force distribution on different areas of the feet through pressure sensors, the projection of the center of gravity can be accurately located to determine if it has shifted. In addition, using biomechanical theory to analyze the distribution of support points, such as whether one foot bears too much weight or the distribution of the two feet is uneven, helps to discover hidden standing posture problems. These data will be used to guide subsequent adjustments to more scientifically redistribute the center of gravity and the force on the feet.

[0077] S3, Spinal Curvature and Tilt Detection

[0078] In this embodiment, through an intelligent posture analysis algorithm, the three-dimensional curvature of the patient's spine is detected, including the physiological curvatures in the front-back direction, including thoracic kyphosis and lumbar lordosis, and the lateral curvature in the left-right direction.

[0079] Poor standing postures are usually accompanied by abnormal spinal curvatures. Therefore, detecting the natural shape of the spine is the key to positioning. In this embodiment, non-contact ultrasonic measurement combined with a depth camera is used to achieve precise modeling. By comparing with the standard spinal curvature, abnormal points are identified and their impact on the overall standing posture is calculated. In addition, through the precise measurement of the tilt angle and rotation angle, it can quickly determine whether the patient has compensatory movements (such as tilting of the shoulders or pelvis), thereby further optimizing the adjustment strategy.

[0080] Furthermore, in the intelligent posture analysis algorithm, an intelligent posture analysis model is constructed based on the spatial curvature distribution of the spine and optimized by introducing multi-modal sensor data;

[0081] The intelligent posture analysis model includes a three-dimensional spinal curvature formula, a spinal lateral tilt angle formula, and a bending abnormality index;

[0082] The three-dimensional spinal curvature formula is used to describe the degree of curvature of the spine at different positions;

[0083] The spinal lateral tilt angle formula is used to directly reflect whether there is an overall left-right lateral deviation problem in the spine;

[0084] The bending abnormality index is used to comprehensively reflect the abnormal distribution of the entire spine.

[0085] In the intelligent posture analysis model:

[0086] Assume that the spine consists of multiple key points, labeled from cervical vertebra C1 to coccyx S5. The three-dimensional coordinates of each key point are collected through sensor data and denoted as , where represents the key point serial number;

[0087] The three-dimensional spinal curvature The formula is as follows:

[0088] ;

[0089] Among them,

[0090] is the spinal length parameter, the cumulative distance from the cervical vertebra to the coccyx;

[0091] is the unit tangent vector of the spine, is the first derivative of the tangent vector;

[0092] is the unit normal vector of the spine, is the first derivative of the normal vector;

[0093] represents the modulus of the vector;

[0094] The curvature of the standard spine has a known normal range in specific regions, including the thoracic kyphosis region;

[0095] The spinal inclination angle formula includes:

[0096] Spinal inclination angle describes the inclination of the spine in the left - right direction and is defined as:

[0097] ;

[0098] Among them,

[0099] are the transverse and longitudinal coordinates of the cervical vertebra vertex respectively;

[0100] are the transverse and longitudinal coordinates of the coccyx bottom;

[0101] In the curvature anomaly index:

[0102] Define an anomaly index , used to quantify the anomaly degree of a certain segment of the spine:

[0103] ;

[0104] Among them, is the standard curvature value corresponding to each key point.

[0105] When this model is used, it includes:

[0106] Data acquisition and model input

[0107] Obtain the three - dimensional coordinates of each key point of the spine through a depth camera and a non - contact ultrasonic device . Input these coordinates into the formula to calculate the curvature , inclination angle and curvature anomaly index .

[0108] Curvature Calculation and Deviation Analysis

[0109] Using the curvature formula , calculate the local curvature in different spinal segments (such as cervical, thoracic, and lumbar vertebrae). Compare the results with the standard spinal curvature range to generate a deviation heat map for each spinal segment. Through this segmented analysis, problem areas can be quickly located, such as whether there is excessive kyphosis in the thoracic vertebrae.

[0110] Tilt Detection and Dynamic Compensation

[0111] Through the roll angle formula , evaluate the degree of lateral deviation of the overall spine. If the angle deviation is greater than a preset threshold (such as 5 degrees), the system will mark it as abnormal and send an adjustment prompt to the patient, such as "slightly raise the right shoulder".

[0112] Optimization of the Bending Abnormality Index

[0113] Through the bending abnormality index , quantitatively describe the degree of abnormality of the patient's current standing posture. The system will provide a personalized adjustment plan based on this index. For example, if it is significantly higher in the thoracic segment, the system can recommend that the patient adjust the shoulder posture or perform dynamic laser grid correction in this area.

[0114] Dynamic Feedback and Real-Time Correction

[0115] Utilize the curvature and tilt angle data calculated by the formula to drive the dynamic feedback system. Real-time display the abnormal area and guide the patient to gradually adjust the standing posture through a visualization interface. For example, after the bending abnormality index drops to a certain threshold, the patient will receive positive feedback to ensure that the adjustment meets the standard.

[0116] S4, Intelligent Laser-Assisted Posture Adjustment

[0117] In this embodiment, by arranging an intelligent laser projector in the patient's standing area, project the human skeleton model of the target standing posture onto the patient's body surface in the form of a grid. The patient observes the deviation of the grid lines and actively adjusts the body until it aligns with the projection.

[0118] In this way, the difficulty of understanding abstract digital feedback is avoided, and the efficiency of the patient's participation in posture adjustment is improved. It is especially suitable for small standing posture errors that require precise correction.

[0119] S5, Dynamic Muscle Activation Feedback Monitoring

[0120] In this embodiment, monitor the activation patterns of each major muscle group during the patient's posture adjustment process through surface electromyography (EMG) sensors to determine whether there are compensatory movements or incorrect muscle force application patterns.

[0121] Muscle activation feedback is instructive in stance adjustment because incorrect force application patterns may cause excessive burden on other muscles or increased joint pressure. During this process, sensors capture the electrical activity signals of relevant muscles in real time, and through machine learning algorithms, determine whether the muscle force application meets the requirements of the standard stance. If abnormalities are detected, the system will issue a prompt to guide the patient to adjust the way of muscle force application and avoid incorrect correction.

[0122] S6, Pelvis and Lower Limb Alignment Correction

[0123] In this embodiment, the pelvic tilt and rotation are detected to guide the patient to achieve pelvic alignment by fine-tuning the foot stance and leg muscle force application. Based on the position data of the pelvis, the alignment status with the hip joint, knee joint, and ankle is calculated. The patient is guided to make the pelvis in a more natural horizontal state by changing the plantar pressure distribution or adjusting the hip angle. In particular, this adjustment should be carried out step by step to prevent the patient from experiencing discomfort or further problems due to large-scale correction.

[0124] S7, Posture Balance Prediction and Guidance

[0125] In this embodiment, using a deep learning model, based on the patient's current skeleton data and the center of gravity projection trajectory, the possible balance changes after stance adjustment are predicted. In cooperation with a wearable vibration device, such as a feedback device worn on the waist or shoulder, the patient is reminded to fine-tune the stance position. The strength of the vibration signal is determined by the degree of imbalance to prevent the patient from being in an unstable state for a long time.

[0126] S8, Upper Limb Posture and Scapula Stability Correction

[0127] In this embodiment, in steps S6 and S8, through a real-time posture feedback algorithm, the pelvic tilt and rotation are detected to guide the patient to achieve pelvic alignment by fine-tuning the foot stance and leg muscle force application, and the patient's upper limb position and scapula stability are monitored, and incorrect shoulder postures are adjusted to avoid the influence of poor stance.

[0128] Adjustment suggestions may include shoulder elevation, backward pull, etc., and at the same time, an elastic stretching device is used for auxiliary correction. This method not only improves the stance but also reduces the risk of long-term excessive burden on the shoulders.

[0129] S9, Biofeedback-Driven Real-Time Adjustment

[0130] In this embodiment, combined with a real-time feedback interface, the difference between the patient's current stance and the target stance is displayed, and the patient is dynamically guided to complete stance fine-tuning through a graphical interface.

[0131] The differences are dynamically displayed on the screen or wearable device through augmented reality (AR) technology. The patient makes fine adjustments by comparing the real-time displayed skeleton model with the target standing posture model. Meanwhile, the system can provide specific adjustment suggestions through colors, graphics or texts, such as "slightly raise the left shoulder" or "move the chin forward by 1 cm". This kind of instant feedback enables the patient to actively participate in the adjustment process, thereby improving the effect.

[0132] S10. Long-term posture optimization and habit reshaping

[0133] In this embodiment, through repeated corrective training, the patient gradually develops the muscle memory of the correct standing posture. The ultimate goal of posture adjustment is to help the patient form a habitual standing posture rather than relying on external devices. Through the repeated corrective process, combined with neuromuscular training, the patient's muscles and central nervous system form the memory of the correct standing posture. A wearable device can be used for daily standing posture monitoring and give phased feedback to prevent the recurrence of bad habits.

[0134] Embodiment Two:

[0135] Refer to Figure 3 , which is the second embodiment of the present invention. This embodiment provides an intelligent positioning system for facilitating the adjustment of the patient's standing posture, which is constructed based on the intelligent positioning method for facilitating the adjustment of the patient's standing posture described in Embodiment One. The system includes a data acquisition and modeling module, a posture analysis and positioning module, a dynamic calibration and guidance module, and a memory enhancement module.

[0136] The data acquisition and modeling module is used to collect the standing posture data of the patient and generate a high-precision human skeleton model, providing a basis for analysis and correction, including:

[0137] Input device: Use a depth camera and a non-contact ultrasonic device to capture the three-dimensional data of the patient's spine and overall standing posture.

[0138] Data processing: Use multi-view fusion technology to generate an accurate human skeleton model, including bone points, joint positions and key spine nodes.

[0139] Modeling algorithm: Implement the algorithm interface for calculating the three-dimensional curvature of the spine and detecting the tilt angle, and input the model data.

[0140] The posture analysis and positioning module is used to analyze the patient's standing posture data in real time, identify abnormal spine curvature, center of gravity shift and other posture problems, including:

[0141] Posture analysis: Analyze the difference between the patient's standing posture and the standard standing posture according to the formulated model.

[0142] Abnormality localization: Generate a heat map of the abnormal area, marking the problem location (such as excessive thoracic curvature or pelvic tilt) and degree (such as mild, moderate, severe).

[0143] Feedback signal generation: Transmit the detection results to other modules and provide correction solutions.

[0144] The dynamic calibration guidance module is used to guide the patient to gradually adjust the standing posture to the correct position through intuitive visualization or dynamic prompts, including:

[0145] Laser grid guidance: Use dynamic laser projection technology to visually project the human skeleton model of the target standing posture onto the patient to intuitively guide the patient to align the standing posture.

[0146] Real-time feedback interface: Combine AR technology to display the comparison between the current posture and the target posture on the screen or head-mounted device, and use colors, marks, etc. to indicate the adjustment direction.

[0147] Wearable vibration prompt: Equip vibration devices on the waist or shoulders to trigger vibration prompts of different intensities according to the degree of posture deviation.

[0148] The memory enhancement module is used to help the patient form the neuromuscular memory of the correct standing posture through long-term training to achieve continuous posture optimization, including:

[0149] Personalized training plan: Design a phased training plan based on the initial evaluation data (such as the curvature abnormality index).

[0150] Feedback loop mechanism: Real-time monitor the adjustment effect of the patient and dynamically adjust the training goals and difficulties according to the progress.

[0151] Habituation assessment: Evaluate whether the neuromuscular memory of the patient is solidified by regularly measuring the posture data, and provide phased rewards or prompts.

[0152] Embodiment 3:

[0153] This embodiment also provides a computer device, which is applicable to a situation of an intelligent positioning system and method for facilitating the adjustment of the patient's standing posture, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent positioning method for facilitating the adjustment of the patient's standing posture as proposed in the above embodiment.

[0154] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by the processor, it implements the intelligent positioning method for facilitating the adjustment of the patient's standing posture as proposed in the above embodiment.

[0155] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0156] If a function is implemented in the form of 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 the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which are various media that can store program codes.

[0157] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0158] More specific examples (a non-exhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer diskettes (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0159] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0160] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An intelligent positioning method for facilitating adjustment of a patient's standing posture, characterized in that: The following steps are involved: S1, initial posture evaluation and skeleton modeling; The patient's overall standing posture data is collected through a high-precision 3D depth camera or laser scanner, and a human skeleton model is established; Use multi-view capture technology to determine skeletal joint positions, joint angles, and body posture distribution; Combined with the human anatomy database, the stability of the current standing posture and its deviation from the standard standing posture are inferred through machine learning algorithms to provide benchmark data for subsequent adjustments; S2, calculation of center of gravity and support point distribution; The patient's human skeleton model is combined with the data from the sole pressure sensor to calculate the projection position of the center of gravity on the sole support surface, and the uniformity and asymmetry of the sole force when standing are determined. S3, spinal curvature and inclination detection; Through intelligent posture analysis algorithms, the three-dimensional curvature of the patient's spine is detected, including the physiological curvature in the anterior-posterior direction, including thoracic kyphosis and lumbar lordosis, as well as the scoliosis in the left-right direction; In the intelligent posture analysis algorithm, an intelligent posture analysis model is constructed based on the spatial curvature distribution of the spine and multimodal sensor data is introduced for optimization; The intelligent posture analysis model includes the three-dimensional curvature formula of the spine, the scoliosis angle formula, and the curvature abnormality index; The three-dimensional curvature formula of the spine is used to describe the curvature of the spine at different positions; The scoliosis angle formula is used to directly reflect whether there is an overall left-right lateral deviation problem in the spine; The curvature abnormality index is used to comprehensively reflect the abnormal distribution of the entire spine; S4, intelligent laser-assisted posture adjustment; By arranging an intelligent laser projector in the patient's standing area, the human skeleton model of the target standing posture is projected onto the patient's body surface in the form of a grid. The patient observes the deviation of the grid line and actively adjusts the body until it is aligned with the projection. S5, dynamic muscle activation feedback monitoring; The surface electromyography (EMG) sensor is used to monitor the activation patterns of the main muscle groups during the patient's posture adjustment process to determine whether there are compensatory movements or incorrect muscle force patterns. S6, pelvic and lower limb alignment correction; S7, posture balance prediction and guidance; S8, upper limb posture and scapula stability correction; Through the real-time posture feedback algorithm, the pelvic tilt and rotation are detected, and the patient is guided to achieve pelvic alignment by fine-tuning the foot position and leg muscle force. The patient's upper limb position and scapular stability are also monitored to adjust incorrect shoulder posture to avoid the impact of poor standing posture. S9, real-time adjustment driven by biofeedback; S10. Long-term posture optimization and habit reshaping.

2. The intelligent positioning method for facilitating adjustment of a patient's standing posture according to claim 1, characterized in that: In the intelligent posture analysis model: Assume that the spine is composed of multiple key points, marked as cervical vertebra C1 to coccygeal vertebra S5, and the three-dimensional coordinates of each key point are collected through sensor data and recorded as ,in Indicates the key point number; Three-dimensional curvature of the spine The formula is as follows: ； in, It is the length parameter of the spine, the cumulative distance from the cervical vertebrae to the coccyx; is the unit tangent vector of the spine, is the first derivative of the tangent vector; is the unit normal vector of the spine, is the first derivative of the normal vector; Represents the magnitude of a vector; The curvature of a standard spine has known normal ranges in specific regions, including the thoracic kyphotic region; The scoliosis angle formula includes: Scoliosis angle Describes the tilt of the spine in the left-right direction and is defined as: ； in, are the transverse and longitudinal coordinates of the cervical vertebrae apex, respectively; are the transverse and longitudinal coordinates of the base of the coccyx; In the bend anomaly index: Defining an anomaly index , which is used to quantify the degree of abnormality in a certain segment of the spine: ； in, is the standard curvature value corresponding to each key point.

3. An intelligent positioning system for facilitating adjustment of a patient's standing posture, constructed based on an intelligent positioning method for facilitating adjustment of a patient's standing posture as described in any one of claims 1-2, characterized in that: The system includes a data acquisition modeling module, a posture analysis and positioning module, a dynamic calibration guidance module, and a memory enhancement module; Data acquisition and modeling module, used to collect the patient's standing posture data and generate a high-precision human skeleton model to provide a basis for analysis and correction; Posture analysis and positioning module, used to analyze the patient's standing posture data in real time to identify abnormal spinal curvature, center of gravity shift and other posture problems; Dynamic calibration guidance module, used to guide patients to gradually adjust their standing posture to the correct position through intuitive visualization or dynamic prompts; The memory enhancement module is used to help patients form neuromuscular memory of correct standing posture through long-term training and achieve continuous posture optimization.

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