AIGC intelligent medical detection auxiliary system based on insole sensing data

Abstract

The invention discloses an AIGC intelligent medical detection auxiliary system based on insole sensing data. The AIGC intelligent medical detection auxiliary system comprises a multi-mode sensor module, a data processing module, a data uploading module, a cloud server and an intelligent display terminal. The multi-modal sensor module is used for collecting physiological data of affected limbs of a lower limb postoperative patient; the data processing module is used for processing the collected physiological data of the affected limb; the data uploading module is used for uploading the processed physiological data of the affected limb to the cloud server; an AIGC model is deployed in the cloud server so as to analyze the processed physiological data of the affected limb based on an artificial intelligence self-learning algorithm, and dynamic and personalized treatment parameters are output to the patient according to historical treatment data so as to form a dynamic and personalized treatment scheme and treatment suggestions; and the intelligent display terminal is used for displaying the processed physiological data of the affected limb and the dynamic personalized treatment scheme and treatment suggestions. The method has the advantages that the method is helpful for helping the postoperative affected limb of the patient after the lower limb operation to recover as soon as possible, medical care and nurses can be conveniently assisted to dynamically monitor the physiological data of the postoperative affected limb of the patient after the lower limb operation, and the efficiency of treating the patient can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to an AIGC intelligent medical detection auxiliary system based on insole sensor data. Background Art

[0002] In major hospitals, daily care of the affected limb after lower limb surgery is particularly important for patients. Especially during the rehabilitation care process, the pressure of the affected limb on the ground after surgery needs to be effectively monitored and supervised at different time periods. Currently, medical staff write medical instructions based on the patient's weight to inform patients after lower limb surgery how much pressure their affected limb needs to touch the ground during different recovery time periods, and the relevant landing pressure should not be too large or too small.

[0003] Specifically, for patients after lower limb surgery, the effects of premature weight-bearing walking include:

[0004] Ligament injury: The reconstructed ligaments and bones have not yet healed firmly. Bearing weight too early will cause the ligaments to bear excessive stress, causing the ligaments to loosen, stretch, or even break, greatly reducing the effectiveness of the surgery, prolonging the recovery period, and increasing the risk of reoperation.

[0005] Graft fixation failure: Excessive weight bearing may cause the fixed position of the graft in the bone tunnel to change, affecting its healing with the bone. In severe cases, it may cause the graft to loosen, shift, or even fall out of the bone tunnel.

[0006] Bone tunnel enlargement: Premature weight-bearing will subject the bone tunnel to greater stress, which may cause the bone cortex of the bone tunnel to become thinner and enlarged, affecting ligament healing and joint stability.

[0007] Decreased joint stability: When the ligaments have not fully recovered their normal function, premature weight bearing will cause the knee joint to bear greater shear force under weight bearing, resulting in joint instability, symptoms such as weak legs and joint shaking, and increasing the risk of joint injury again.

[0008] Articular cartilage and meniscus injury: The knee joint bears greater pressure when bearing weight. Bearing weight too early will cause excessive squeezing and wear of the articular cartilage and meniscus that have not yet adapted to the greater pressure, leading to cartilage damage, meniscus rupture and other problems, causing symptoms such as pain, swelling, and clicking, and may even develop into traumatic arthritis.

[0009] Furthermore, for patients after lower limb surgery, the effects of delayed weight-bearing walking include:

[0010] Muscle atrophy: If you avoid weight-bearing for a long time, the muscles around the knee joint, such as the quadriceps and hamstrings, will gradually atrophy due to lack of exercise, and the muscle strength will decrease. They will not be able to provide sufficient support and stability for the knee joint, affecting the recovery of joint function.

[0011] Joint stiffness: Excessive avoidance of weight bearing will cause the knee joint to be in a relatively static state for a long time, resulting in contracture of tissues such as the joint capsule and ligaments, reduced joint mobility, stiffness, and limited flexion and extension functions, affecting activities in daily life such as walking, squatting, and going up and down stairs.

[0012] Osteoporosis: Due to the lack of weight-bearing stimulation, the bone density of the lower limb bones will gradually decrease, resulting in osteoporosis, making the bones fragile and increasing the risk of fractures.

[0013] Poor blood circulation: Long-term non-weight-bearing will affect the blood circulation of the lower limbs, causing blood stasis in the lower limbs, easily forming deep vein thrombosis, and the metabolic products in the joints cannot be discharged in time, which is not conducive to joint recovery.

[0014] Proprioception is reduced: During activities such as weight-bearing walking, the proprioceptors in the joints can sense the position and movement of the joints, helping to maintain the body's balance and coordination. Excessive weight avoidance will reduce proprioception, affect the body's balance and movement coordination, and increase the possibility of re-injury.

[0015] In summary, that is to say, for patients who have undergone lower limb surgery, appropriate landing pressure is required when their affected limbs are in different recovery periods. If the landing pressure is too high, it may cause ligament injury, graft fixation failure, bone tunnel enlargement, decreased joint stability, and articular cartilage and meniscus damage. If the landing pressure is too low, it may easily lead to muscle atrophy, joint stiffness, osteoporosis, poor blood circulation, and decreased proprioception.

[0016] Therefore, for the monitoring and supervision of the landing pressure of the affected limbs of patients after lower limb surgery, the existing technology cannot achieve dynamic control, nor can it provide dynamic treatment plans and feedback on treatment suggestions, which makes it impossible to effectively improve the rehabilitation effect of the affected limbs of patients after lower limb surgery and the treatment efficiency of medical staff on the affected limbs of patients after lower limb surgery.

[0017] In this regard, the inventor of this patent combined clinical experience, thought about the problems encountered in clinical work, read a large amount of scientific research materials and literature, and through searching and novelty, gradually conceived and designed this application to solve related technical problems. Summary of the Invention

[0018] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present invention aims to provide an AIGC intelligent medical detection auxiliary system based on insole sensor data.

[0019] To achieve one of the above objectives, an AIGC intelligent medical detection auxiliary system based on insole sensor data according to an embodiment of the present invention includes:

[0020] A multimodal sensor module is used to collect physiological data of the affected limb of patients after lower limb surgery, wherein the physiological data of the affected limb includes at least plantar pressure distribution data, gait data and dynamic weight data;

[0021] a data processing module connected to the multimodal sensor module and configured to process the collected physiological data of the affected limb;

[0022] A data uploading module, connected to the data processing module, for uploading the processed physiological data of the affected limb to the cloud server;

[0023] A cloud server, connected to the data upload module, deploying an AIGC model to analyze the received processed physiological data of the affected limb using an artificial intelligence-based self-learning algorithm, and output dynamic personalized treatment parameters for the patient based on historical treatment data to form a dynamic personalized treatment plan and treatment recommendations;

[0024] An intelligent display terminal is connected to the cloud server and is used to display the processed physiological data of the affected limb and the dynamic personalized treatment plan and treatment suggestions.

[0025] In addition, the AIGC intelligent medical detection auxiliary system based on insole sensor data according to the above embodiment of the present invention may also have the following additional technical features:

[0026] According to one embodiment of the present invention, the multimodal sensor module includes:

[0027] insole;

[0028] A plurality of high-precision piezoelectric film sensors, wherein the plurality of high-precision piezoelectric film sensors are uniformly embedded in the insole in a pressure matrix manner to collect physiological data of the affected limb;

[0029] A wireless transmission module is connected to the plurality of high-precision piezoelectric film sensors and is used for wirelessly transmitting the collected physiological data of the affected limb to the data processing module.

[0030] According to one embodiment of the present invention, the multimodal sensor module further includes a power supply module for supplying power to the plurality of high-precision piezoelectric film sensors;

[0031] The power supply module is connected to a plurality of the high-precision piezoelectric film sensors.

[0032] According to one embodiment of the present invention, the power supply module is an ultra-thin battery and is embedded in the front portion of the insole.

[0033] According to one embodiment of the present invention, the data processing module includes:

[0034] a wireless receiving module, connected to the wireless sending module, for receiving the physiological data of the affected limb sent by the wireless sending module;

[0035] a preprocessing module connected to the wireless receiving module, for preprocessing the received physiological data of the affected limb to remove duplicate, erroneous, substandard pressure and incomplete data records;

[0036] The data fusion module is connected to the preprocessing module and is used to perform multi-parameter fusion on the preprocessed physiological data of the affected limb, so as to finally form the processed physiological data of the affected limb.

[0037] According to one embodiment of the present invention, the AIGC model includes:

[0038] A self-learning algorithm module is used to self-update and optimize model parameters through a neural network algorithm based on historical treatment data and the processed physiological data of the affected limb, and to evaluate the patient's current health status to obtain an evaluation result;

[0039] a treatment parameter output module, connected to the self-learning algorithm module, for automatically calculating and outputting dynamic personalized treatment parameters for the patient based on the evaluation results generated by the self-learning algorithm module;

[0040] The treatment plan and treatment suggestion generation module is connected to the treatment parameter generation module and is used to automatically generate a dynamic personalized treatment plan and treatment suggestion for the patient based on the output dynamic personalized treatment parameters for the patient.

[0041] According to one embodiment of the present invention, the intelligent display terminal includes a monitoring terminal for medical staff and a smart phone for patients;

[0042] The monitoring terminal and the smart phone are both used to display the processed physiological data of the affected limb, and are both used to display the dynamic personalized treatment plan and treatment suggestions.

[0043] According to one embodiment of the present invention, the historical treatment data is obtained from a large medical database.

[0044] According to one embodiment of the present invention, the medical big data database includes at least a patient information database storing information of patients undergoing different lower limb surgeries, a patient electronic medical record database storing electronic medical records of patients undergoing different lower limb surgeries, and a patient laboratory test database storing condition test data of patients undergoing different lower limb surgeries.

[0045] According to one embodiment of the present invention, the lower limb surgery at least includes knee ligament rupture surgery, meniscus suture surgery and ankle joint surgery.

[0046] The beneficial effects of the present invention are:

[0047] First, after lower limb surgery, patients can refer to the processed physiological data of the affected limb displayed on the intelligent display terminal, and compare it with the dynamic personalized treatment plan and treatment suggestions to check in real time whether the value of the force applied to the ground by the affected limb after surgery is within an appropriate range when walking or running. If it is not within the appropriate range, the value of the force applied to the ground by the affected limb after surgery can be adjusted according to the corresponding dynamic personalized treatment plan and treatment suggestions until it is adjusted to the appropriate range. In this way, it can help the affected limb of patients after lower limb surgery to recover as soon as possible, and can also better reduce the frequency of excessive force applied to the ground by the affected limb of patients after lower limb surgery when walking or running, so as to prevent recurrence of bone or muscle injuries of the feet of patients after lower limb surgery.

[0048] Second, in combination with the AIGC model, medical staff consult the intelligent display terminal, and the dynamic personalized treatment plan and treatment suggestions are automatically generated. This can save manpower by eliminating the need for medical staff to manually write them, and it can also facilitate auxiliary medical staff and nurses to dynamically monitor the physiological data of the affected limbs of patients after lower limb surgery, thereby greatly improving the efficiency of treating patients.

[0049] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0051] Figure 1 Schematic diagram of the overall block diagram of the AIGC intelligent medical detection auxiliary system based on insole sensor data of the present invention;

[0052] Figure 2 is an overall block diagram of the multimodal sensor module in an embodiment of the present invention;

[0053] Figure 3 is an overall block diagram of the data processing module in an embodiment of the present invention;

[0054] Figure 4 1 is a schematic block diagram of the AIGC model according to an embodiment of the present invention;

[0055] Figure 5is a schematic block diagram of the intelligent display terminal in an embodiment of the present invention;

[0056] Figure 6 1 is a schematic diagram of an overall block diagram of the medical big data database in an embodiment of the present invention;

[0057] Reference numerals:

[0058] AIGC intelligent medical detection assistance system 1000 based on insole sensor data;

[0059] Multimodal sensor module 10;

[0060] Insoles 101;

[0061] High-precision piezoelectric film sensor 102;

[0062] Wireless sending module 103;

[0063] Power supply module 104;

[0064] Data processing module 20;

[0065] Wireless receiving module 201;

[0066] Pre-processing module 202;

[0067] Data fusion module 203;

[0068] Data upload module 30;

[0069] Cloud server 40;

[0070] AIGC model 50;

[0071] Self-learning algorithm module 501;

[0072] Treatment parameter output module 502;

[0073] Treatment plan and treatment suggestion generating module 503;

[0074] Intelligent display terminal 60;

[0075] Monitoring terminal 601;

[0076] Smartphone 602;

[0077] Medical big database 70;

[0078] Patient information database 701;

[0079] patient electronic medical record database 702;

[0080] Patient laboratory test database 703;

[0081] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0082] The following describes in detail embodiments of the present invention. Examples of the embodiments are shown in the accompanying drawings. The same or similar reference numerals throughout the specification represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but are not to be construed as limiting the present invention. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0083] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "circumferential", "radial", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings of the specification, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0084] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0085] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0086] In the present invention, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Furthermore, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0087] The following describes in detail an AIGC intelligent medical detection assistance system 1000 based on insole sensor data according to an embodiment of the present invention with reference to the accompanying drawings.

[0088] Figure 1 Schematic diagram of the overall block diagram of the AIGC intelligent medical detection auxiliary system based on insole sensor data of the present invention; Figure 2 is an overall block diagram of the multimodal sensor module in an embodiment of the present invention; Figure 3 is an overall block diagram of the data processing module in an embodiment of the present invention; Figure 4 1 is a schematic block diagram of the AIGC model according to an embodiment of the present invention; Figure 5 is a schematic block diagram of the intelligent display terminal in an embodiment of the present invention; Figure 6 1 is a schematic diagram of an overall block diagram of the medical big data database in an embodiment of the present invention;

[0089] Reference Figures 1 to 6 As shown, the AIGC intelligent medical detection auxiliary system 1000 based on insole sensor data provided according to an embodiment of the present invention includes a multimodal sensor module 10, a data processing module 20, a data upload module 30, a cloud server 40, and an intelligent display terminal 60 provided on the medical side and the patient side:

[0090] Specifically, in the technical solution of the present application, the multimodal sensor module 10 is used to collect physiological data of the affected limb of the patient after lower limb surgery, and the physiological data of the affected limb includes at least plantar pressure distribution data of the affected limb of the patient after lower limb surgery, gait data of the affected limb after lower limb surgery, and dynamic weight data of the affected limb after lower limb surgery;

[0091] Specifically, in the present technical solution, the data processing module 20 is connected to the multimodal sensor module 10 to process the collected physiological data of the affected limb, that is, to process the collected plantar pressure distribution data of the affected limb of the patient after lower limb surgery, the gait data of the affected limb of the patient after lower limb surgery, and the dynamic weight data of the affected limb of the patient after lower limb surgery;

[0092] Furthermore, in this technical solution, the data uploading module 30 is connected to the data processing module 20 to upload the processed physiological data of the affected limb to the cloud server 40;

[0093] At the same time, in this technical solution, the cloud server 40 is connected to the data upload module 30, in which an AIGC model 50 is deployed to analyze the received processed physiological data of the affected limb with a self-learning algorithm based on artificial intelligence, and output dynamic personalized treatment parameters for the patient based on historical treatment data to form a dynamic personalized treatment plan and treatment recommendations;

[0094] Based on this, in the present technical solution, the intelligent display terminal 60 is connected to the cloud server 40 to display the processed physiological data of the affected limb and the dynamic personalized treatment plan and treatment recommendations.

[0095] Based on the above, it can be clearly seen that when this application is implemented, it is mainly used as an AIGC intelligent medical detection auxiliary system 1000 based on insole sensor data.

[0096] Specifically, when applying this application, this application is mainly used to perform dynamic medical testing of the lower limb physiological data of patients after lower limb surgery during the postoperative rehabilitation process of lower limb surgery, and combine with the AIGC model 50 to analyze the detected results to finally form a dynamic personalized treatment plan and treatment recommendations, and display the processed physiological data of the affected limb through the smart display terminal 60 provided at the medical end and the patient end, and also display the dynamic personalized treatment plan and treatment recommendations through the smart display terminal 60 provided at the medical end and the patient end. In this way, during the postoperative rehabilitation process of lower limb surgery, patients after lower limb surgery can use this application to provide patients with the dynamic personalized treatment plan and treatment recommendations, so as to facilitate the rapid recovery of patients after lower limb surgery.

[0097] Obviously, the application of this application will have the following technical effects:

[0098] On the one hand, after lower limb surgery, patients can refer to the processed physiological data of the affected limb displayed on the intelligent display terminal 60, and compare it with the dynamic personalized treatment plan and treatment suggestions to check in real time whether the value of the force applied to the ground by the affected limb after lower limb surgery when walking or running is within the appropriate range. If it is not within the appropriate range, the value of the force applied to the ground by the affected limb after lower limb surgery can be adjusted according to the corresponding dynamic personalized treatment plan and treatment suggestions until it is adjusted to the appropriate range. In this way, it can help the affected limb of patients after lower limb surgery to recover as soon as possible, and can also better reduce the frequency of excessive or insufficient ground force applied by the affected limb of patients after lower limb surgery when walking or running.

[0099] On the other hand, in combination with the AIGC model 50, medical staff consult the intelligent display terminal 60, and the dynamic personalized treatment plan and treatment suggestions are automatically generated. While saving manpower by eliminating the need for medical staff to manually write them, it also facilitates auxiliary medical staff and nurses to dynamically monitor the physiological data of the affected limbs of patients after lower limb surgery, thereby greatly improving the efficiency of treating patients.

[0100] Furthermore, through the above-mentioned optimized design, the overall structure of the present application is highly practical and has good use effect.

[0101] Furthermore, in specific implementation, Figure 1 and Figure 2 As shown, according to one embodiment of the present invention, the multimodal sensor module 10 specifically includes an insole 101, a plurality of high-precision piezoelectric film sensors 102 and a wireless transmission module 103:

[0102] The insole 101 can be designed into a plurality of different sizes according to actual use requirements, so as to be suitable for use in shoes worn by patients with different foot sizes after lower limb surgery.

[0103] Based on this, the multiple high-precision piezoelectric film sensors 102 of the present application are evenly embedded in the insole 101 in a pressure matrix, which are mainly used to collect physiological data of the affected limb, that is, to collect plantar pressure distribution data of the affected limb of the patient after lower limb surgery, gait data of the affected limb after lower limb surgery, and dynamic weight data of the affected limb after lower limb surgery;

[0104] Based on this, the wireless transmission module 103 described in this application is connected to the multiple high-precision piezoelectric film sensors 102 , and is mainly used to wirelessly transmit the collected physiological data of the affected limb to the data processing module 20 .

[0105] In the present application, the wireless transmission module 103 is preferably a Bluetooth module to reduce power consumption and enable the present application to be used for a long time.

[0106] Furthermore, in this technical solution, continue to compare Figure 1 and Figure 2 As shown, according to one embodiment of the present invention, the multimodal sensor module 10 further includes a power supply module 104 for supplying power to the plurality of high-precision piezoelectric film sensors 102;

[0107] Among them, the power supply module 104 is connected to multiple high-precision piezoelectric film sensors 102. In this way, the power supply module 104 can be used to power multiple high-precision piezoelectric film sensors 102 so that they can all work normally and can be used continuously to collect plantar pressure distribution data of the affected limbs of patients after lower limb surgery, gait data of the affected limbs after lower limb surgery, and dynamic weight data of the affected limbs after lower limb surgery.

[0108] Preferably, in this technical solution, according to one embodiment of the present invention, the power supply module 104 is an ultra-thin battery and is embedded in the front part of the insole 101. In this way, the insole 101 described in this application has its own power supply, so that it can work without an external power supply when used.

[0109] It should be noted here that for patients, when their feet touch the ground, their heels usually touch the ground in advance, and will provide greater support for their affected limbs after surgery. Therefore, this application embeds the power supply module 104 in the front part of the insole 101, so that the force of stepping on it is smaller and it is not easy to be damaged.

[0110] Furthermore, in the specific implementation, Figure 1 and Figure 3 As shown, according to one embodiment of the present invention, the data processing module 20 includes a wireless receiving module 201, a pre-processing module 202 and a data fusion module 203;

[0111] The wireless receiving module 201 is connected to the wireless transmitting module 103 and is mainly used to receive the physiological data of the affected limb sent by the wireless transmitting module 103. When the wireless transmitting module 103 is preferably a Bluetooth module, the wireless receiving module 201 is also preferably a Bluetooth module.

[0112] Moreover, in the present application, the pre-processing module 202 is connected to the wireless receiving module 201 and is mainly used to pre-process the received physiological data of the affected limb, mainly including removing duplicate, erroneous, substandard pressure and incomplete data records;

[0113] In the present application, the data fusion module 203 is connected to the preprocessing module 202, which mainly adopts a data fusion algorithm to collaboratively analyze multiple preprocessed physiological data of the affected limb and perform multi-parameter fusion to finally form the processed physiological data of the affected limb.

[0114] Furthermore, in the specific implementation, Figure 1 and Figure 4As shown, according to one embodiment of the present invention, the AIGC model 50 includes a self-learning algorithm module 501 and a treatment parameter output module 502, and also includes a treatment plan and treatment suggestion generation module 503.

[0115] The self-learning algorithm module 501 is used to self-update and optimize model parameters through a neural network algorithm based on historical treatment data and the processed physiological data of the affected limb, and evaluate the patient's current health status to obtain an evaluation result;

[0116] Based on this, the treatment parameter output module 502 of the present application is connected to the self-learning algorithm module 501, which is mainly used to automatically calculate and output dynamic personalized treatment parameters for the patient based on the evaluation results generated by the self-learning algorithm module 501;

[0117] Based on this, the treatment plan and treatment recommendation generation module 503 described in this application is connected to the treatment parameter generation module, which is mainly used to automatically generate dynamic personalized treatment plans and treatment recommendations for patients based on the output dynamic personalized treatment parameters for patients.

[0118] On this basis, the control Figure 1 and Figure 5 As shown, according to one embodiment of the present invention, the intelligent display terminal 60 includes a monitoring terminal 601 for medical staff and a smart phone 602 or smart tablet for patients;

[0119] The monitoring terminal 601 and the smart phone 602 or smart tablet are both used to display the processed physiological data of the affected limb, and are both used to display the dynamic personalized treatment plan and treatment recommendations.

[0120] In this way, it can be made clear that:

[0121] On the one hand, patients who have undergone lower limb surgery can check the smart phone 602 or smart tablet they carry with them to view the current plantar pressure distribution data, gait data and dynamic weight data of their affected limbs, and compare them with the displayed dynamic personalized treatment plan and treatment suggestions to know whether the current value of the force exerted on the ground by their affected limbs when walking or running is within the appropriate range. When it is not within the appropriate range, the value of the force exerted on the ground by the current affected limbs when walking or running can be adjusted according to the corresponding dynamic personalized treatment plan and treatment suggestions until it is adjusted to the appropriate range. In this way, it can indeed help patients who have undergone lower limb surgery to recover their affected limbs as soon as possible after surgery, and it can also effectively reduce the frequency of excessive or insufficient ground force exerted by patients who have undergone lower limb surgery after surgery when walking or running.

[0122] It should be noted here that before using this application, you need to enter the basic information of the patient after lower limb surgery, including name, gender, ID number, fasting weight, height, surgical site and surgery name, etc., and then you can apply this application to the relevant lower limb surgery patients.

[0123] For example, for a patient who has undergone meniscus suture surgery, his current fasting weight is 60 kg.

[0124] For example, within one week after surgery, the doctor's order is that the pressure load threshold of the patient's affected limb's sole on the ground is 20% of his body weight, that is, 12 kg. During this period, the patient is required to keep the pressure load of the patient's affected limb's sole on the ground at no more than 12 kg during walking. If it is equal to or exceeds 12 kg, the monitoring terminal 601 and the smart phone 602 will both display and give an "overloaded" prompt to provide the dynamic personalized treatment plan and treatment suggestions, reminding the patient to pay attention to the need to reduce the pressure load of the patient's affected limb's sole on the ground during walking, until the application of this application detects that the pressure load of the patient's affected limb's sole is lower than the set 12 kg.

[0125] For another example, within the second week after surgery, the doctor's order is that the pressure load threshold of the patient's affected limb's sole on the ground is 30% of his body weight, that is, 18 kg. During this time period, the patient is required to keep the pressure load of the patient's affected limb's sole on the ground at no more than 18 kg during walking. If it is equal to or exceeds 18 kg, the monitoring terminal 601 and the smart phone 602 will both display and give an "overloaded" prompt to provide the dynamic personalized treatment plan and treatment suggestions, reminding the patient to pay attention to the need to reduce the pressure load of the patient's affected limb's sole on the ground during walking, until the application of this application detects that the pressure load of the patient's affected limb's sole is lower than the set 18 kg.

[0126] For example, within the third week after surgery, the doctor's order is that the pressure load threshold of the patient's affected limb's sole on the ground is 40% of his body weight, that is, 24 kg. During this time period, the patient is required to keep the pressure load of the patient's affected limb's sole on the ground at no more than 24 kg during walking. If it is equal to or exceeds 24 kg, the monitoring terminal 601 and the smart phone 602 will both display and give an "overloaded" prompt to provide the dynamic personalized treatment plan and treatment suggestions, reminding the patient to pay attention to the need to reduce the pressure load of the patient's affected limb's sole on the ground during walking, until the application of this application detects that the pressure load of the patient's affected limb's sole is lower than the set 24 kg.

[0127] In this way, by applying this application, according to the different rehabilitation time periods of the patient's affected limb, the AIGC model can be used according to the historical treatment data, such as the retrieved same surgical cases of patients with the same weight or close to the same weight, and with reference to the treatment plans and treatment suggestions given in different rehabilitation time periods after surgery, to automatically provide dynamic and personalized treatment plans and treatment suggestions that are suitable for the current patient's different rehabilitation time periods. As the rehabilitation time progresses, based on the patient's fasting weight, the pressure load threshold of the relevant patient's affected limb stepping on the ground is gradually increased, so that the current patient can be better assisted in the rapid rehabilitation of the affected limb after surgery.

[0128] On the other hand, in combination with the AIGC model 50, medical staff consult the monitoring terminal 601, and the dynamic personalized treatment plan and treatment suggestions are automatically generated. While saving manpower by eliminating the need for medical staff to manually write them, it also facilitates auxiliary medical staff and nurses to dynamically monitor the physiological data of the affected limbs of patients after lower limb surgery, thereby greatly improving the efficiency of treating patients.

[0129] Furthermore, in the specific implementation, Figure 1 and Figure 6 As shown, according to one embodiment of the present invention, the historical treatment data is obtained from a large medical database 70 .

[0130] Specifically, in this application, according to one embodiment of the present invention, the medical big data database 70 includes at least a patient information database 701 storing information of patients undergoing different lower limb surgeries, a patient electronic medical record database 702 storing electronic medical records of patients undergoing different lower limb surgeries, and a patient laboratory test database 703 storing condition test data of patients undergoing different lower limb surgeries.

[0131] Based on this, the historical treatment data described in this application is taken from the patient information database 701, the patient electronic medical record database 702 and the patient laboratory test database 703, making the data access authoritative, reliable and referenceable.

[0132] It should be supplemented that, in specific implementation, according to one embodiment of the present invention, the lower limb surgery at least includes knee ligament rupture surgery, meniscus suture surgery and ankle joint surgery.

[0133] It is further necessary to explain that in the specific implementation, Figure 1 and Figure 2As shown, according to one embodiment of the present invention, a touch pressure switch 105 connected between the power supply module 104 and the multiple high-precision piezoelectric film sensors 102 is also embedded in the rear part of the insole 101. When the touch pressure switch 105 is pressed down by the sole of the affected limb of the patient after lower limb surgery, the touch pressure switch 105 will be closed. In this way, it is convenient for the power supply module 104 to be connected to the multiple high-precision piezoelectric film sensors 102 and power the multiple high-precision piezoelectric film sensors 102, so that the multiple high-precision piezoelectric film sensors 102 enter a working state, and are used for collecting the plantar pressure distribution data of the affected limb of the patient after lower limb surgery, the gait data of the affected limb after lower limb surgery, and the dynamic weight data of the affected limb after lower limb surgery.

[0134] On the contrary, when the patient lifts the affected limb after lower limb surgery so that the sole of the affected limb leaves the ground, the touch switch 105 will be turned on, so that the power supply module 104 can be disconnected from the multiple high-precision piezoelectric film sensors 102, and the multiple high-precision piezoelectric film sensors 102 can be put into a dormant state to save power.

[0135] It should be further explained that, in a specific implementation, according to one embodiment of the present invention, the insole 101 described in this application is preferably made of soft rubber material, and the multiple high-precision piezoelectric film sensors 102, the wireless transmission module 103, the power supply module 104 and the touch-pressure switch 105 embedded therein are not exposed, so that the overall structure thereof has good waterproofness and is safe and reliable to use.

[0136] Other embodiments and the like are not described here as examples.

[0137] To summarize, the AIGC intelligent medical detection assistance system 1000 based on insole sensor data provided by the present application, when implemented, can refer to the processed physiological data of the affected limb displayed on the intelligent display terminal 60 after lower limb surgery, and compare it with the dynamic personalized treatment plan and treatment suggestions, to check in real time whether the value of the force applied to the ground by the affected limb after lower limb surgery when walking or running is within an appropriate range. If it is not within the appropriate range, the value of the force applied to the ground by the affected limb after lower limb surgery can be adjusted according to the corresponding dynamic personalized treatment plan and treatment suggestions until it is adjusted to the appropriate range. In this way, it can help the affected limb of the patient after lower limb surgery to recover as soon as possible, and can also better reduce the frequency of excessive or insufficient ground force applied by the affected limb of the patient after lower limb surgery when walking or running.

[0138] Furthermore, in combination with the AIGC model 50, medical staff consult the intelligent display terminal 60, and the dynamic personalized treatment plan and treatment recommendations are automatically generated. This not only saves medical staff from having to manually write them, thereby saving manpower, but also facilitates auxiliary medical staff and nurses to dynamically monitor the physiological data of the affected limbs of patients after lower limb surgery, thereby greatly improving the efficiency of treating patients.

[0139] Furthermore, the AIGC intelligent medical detection auxiliary system 1000 based on insole sensor data provided by this application is indeed extremely practical and has excellent performance, which makes this application bound to have great market promotion value. This application will also be very popular and will surely be effectively popularized.

[0140] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0141] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. An AIGC intelligent medical detection auxiliary system based on insole sensor data, characterized in that: include: A multimodal sensor module is used to collect physiological data of the affected limb of patients after lower limb surgery, wherein the physiological data of the affected limb includes at least plantar pressure distribution data, gait data and dynamic weight data; a data processing module connected to the multimodal sensor module and configured to process the collected physiological data of the affected limb; A data uploading module, connected to the data processing module, for uploading the processed physiological data of the affected limb to the cloud server; A cloud server, connected to the data upload module, deploying an AIGC model to analyze the received processed physiological data of the affected limb using an artificial intelligence-based self-learning algorithm, and output dynamic personalized treatment parameters for the patient based on historical treatment data to form a dynamic personalized treatment plan and treatment recommendations; An intelligent display terminal is connected to the cloud server and is used to display the processed physiological data of the affected limb and the dynamic personalized treatment plan and treatment suggestions.

2. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 1 is characterized in that: The multimodal sensor module comprises: insole; A plurality of high-precision piezoelectric film sensors, wherein the plurality of high-precision piezoelectric film sensors are uniformly embedded in the insole in a pressure matrix manner to collect physiological data of the affected limb; A wireless transmission module is connected to the plurality of high-precision piezoelectric film sensors and is used for wirelessly transmitting the collected physiological data of the affected limb to the data processing module.

3. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 2 is characterized in that: The multimodal sensor module further includes a power supply module for supplying power to the plurality of high-precision piezoelectric film sensors; The power supply module is connected to a plurality of the high-precision piezoelectric film sensors.

4. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 3 is characterized in that: The power supply module is an ultra-thin battery and is embedded in the front part of the insole.

5. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 2 is characterized in that: The data processing module includes: a wireless receiving module, connected to the wireless sending module, for receiving the physiological data of the affected limb sent by the wireless sending module; a preprocessing module connected to the wireless receiving module, for preprocessing the received physiological data of the affected limb to remove duplicate, erroneous, substandard pressure and incomplete data records; The data fusion module is connected to the preprocessing module and is used to perform multi-parameter fusion on the preprocessed physiological data of the affected limb, so as to finally form the processed physiological data of the affected limb.

6. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 1 is characterized in that: The AIGC model includes: A self-learning algorithm module is used to self-update and optimize model parameters through a neural network algorithm based on historical treatment data and the processed physiological data of the affected limb, and to evaluate the patient's current health status to obtain an evaluation result; a treatment parameter output module, connected to the self-learning algorithm module, for automatically calculating and outputting dynamic personalized treatment parameters for the patient based on the evaluation results generated by the self-learning algorithm module; The treatment plan and treatment suggestion generation module is connected to the treatment parameter generation module and is used to automatically generate a dynamic personalized treatment plan and treatment suggestion for the patient based on the output dynamic personalized treatment parameters for the patient.

7. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 1 is characterized in that: The intelligent display terminal includes a monitoring terminal for medical staff and a smart phone for patients; The monitoring terminal and the smart phone are both used to display the processed physiological data of the affected limb, and are both used to display the dynamic personalized treatment plan and treatment suggestions.

8. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to any one of claims 1 to 7, characterized in that: The historical treatment data are obtained from a large medical database.

9. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 8 is characterized in that: The medical big database at least includes a patient information database storing information of patients undergoing different lower limb surgeries, a patient electronic medical record database storing electronic medical records of patients undergoing different lower limb surgeries, and a patient laboratory test database storing condition test data of patients undergoing different lower limb surgeries.

10. The AIGC intelligent medical detection auxiliary system based on insole sensor data according to claim 9 is characterized in that: The lower limb surgery at least includes knee ligament rupture surgery, meniscus suture surgery and ankle joint surgery.

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