Intelligent shoes and interaction system thereof

Abstract

The invention relates to an intelligent shoe and an interaction system thereof, the intelligent shoe comprises a plantar pressure sensor, an inertial measurement unit and a controller, and the plantar pressure sensor is used for collecting pressure distribution data of each area of the plantar of a target object. The inertial measurement unit is used for collecting inertial data in the movement process of the target object. The gait mode of the target object is predicted according to the pressure distribution data, transmitted by the plantar pressure sensor, of all areas of the plantar of the target object and the inertial data, transmitted by the inertial measurement unit, of the target object, so that under the condition that the predicted gait mode is an abnormal gait mode, the gait abnormity alarm mode of the intelligent shoe is triggered, and the intelligent shoe is started. Therefore, the intelligent shoe can perform early warning on the falling risk of the target object in real time, and the falling risk of the target object is reduced.

Description

Technical Field

[0001] The present invention relates to the field of damage identification technology, and in particular to a smart shoe and an interactive system thereof. Background Art

[0002] In today's medical field, patients are increasingly demanding assistive devices during daily care and rehabilitation. As a product closely related to patients' daily activities, the functional perfection of medical shoes directly affects their recovery and quality of life.

[0003] Traditional medical shoes have a relatively simple function, primarily providing basic foot support and protection. While fall warning technology is not yet widely available, the risk of falls significantly increases when patients are active in settings like hospitals and rehabilitation centers due to decreased physical function and illness. According to statistics, the incidence of falls among hospitalized patients can reach 3% to 5%, causing not only physical harm but also serious complications such as fractures and head injuries, prolonging hospital stays and increasing medical costs. The lack of an effective fall warning system makes it difficult for medical staff to intervene promptly and effectively prevent falls. Summary of the Invention

[0004] In response to the above technical problems, the present invention provides a smart shoe and an interactive system thereof, which can predict the gait pattern of a target object. When the gait pattern is predicted to be an abnormal gait pattern, the gait abnormality alarm mode of the smart shoe is triggered, thereby enabling the smart shoe to provide real-time early warning of the target object's fall risk, thereby reducing the target object's fall risk.

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

[0006] A smart shoe, comprising:

[0007] A plantar pressure sensor is used to collect pressure distribution data of various areas on the sole of the target object;

[0008] an inertial measurement unit, configured to collect inertial data of the target object during its motion;

[0009] The controller is configured to receive pressure distribution data of various areas of the sole of the foot transmitted by the sole pressure sensor and inertial data transmitted by the inertial measurement unit; predict the gait pattern of the target object based on the pressure distribution data of various areas of the sole of the foot and the inertial data; and trigger a gait abnormality alarm mode of the smart shoe if the gait pattern is abnormal.

[0010] In one possible implementation manner, the smart shoe further comprises: a temperature and humidity sensor for collecting the temperature and humidity of the air in the smart shoe;

[0011] The controller is further configured to receive the temperature and humidity transmitted by the temperature and humidity sensor; and trigger the temperature and humidity abnormality alarm mode of the smart shoe when either the temperature or the humidity does not meet a preset temperature and humidity condition.

[0012] In one possible implementation manner, the smart shoe further comprises: a flexible pressure sensor for collecting pressure data between the target object's ankle and the smart shoe;

[0013] The controller is further configured to receive pressure data transmitted by the flexible pressure sensor; determine a rate of change of the pressure data based on the pressure data within a preset time period; and determine a degree of foot edema of the target object based on the rate of change of the pressure data.

[0014] In one possible implementation manner, the smart shoe further comprises: an optical heart rate sensor for detecting heart rate data of the target object;

[0015] The controller is further configured to receive heart rate data transmitted by the optical heart rate sensor; and trigger an abnormal heart rate alarm mode of the smart shoe when the heart rate data is not within a preset heart rate range.

[0016] In one possible implementation manner, the smart shoe further comprises: an infrared sensor for detecting blood oxygen saturation and blood flow velocity of the foot of the target object;

[0017] The controller is further configured to receive the blood oxygen saturation and blood flow velocity of the foot transmitted by the infrared sensor; and trigger a blood circulation abnormality alarm mode of the smart shoe when either the blood oxygen saturation or the blood flow velocity does not meet preset blood oxygen saturation and blood flow velocity conditions.

[0018] In one possible implementation manner, the controller is further configured to perform health classification using the pressure data, the heart rate data, the blood oxygen saturation of the foot, the blood flow velocity, the temperature and the humidity to determine the preliminary health status of the target object.

[0019] In one possible implementation mode, it is characterized in that the smart shoe further comprises: a disinfection module for disinfecting the environment inside the smart shoe;

[0020] The controller is further configured to start the disinfection module when the pressure transmitted by the plantar pressure sensor is zero.

[0021] The present invention also provides an interactive system for smart shoes, comprising: a server and the smart shoes mentioned in the above solution;

[0022] The smart shoe is used to send the gait pattern and inertial data obtained by the controller to the server;

[0023] The server is configured to receive the gait pattern and inertia data of the target object uploaded by the smart shoe; determine the gait data of the target object based on the inertia data, and generate a gait training strategy for the target object based at least on the gait pattern and the gait data.

[0024] In one possible implementation manner, the server is further configured to, upon receiving the health indicators of the target object uploaded by the smart shoes, predict the health trend of the target object based on the health indicators; wherein at least one of the following health indicators includes: pressure data, heart rate data, blood oxygen saturation of the foot, blood flow velocity, temperature and humidity.

[0025] In one possible implementation, generating a gait training strategy for the target subject based at least on the gait pattern and the gait data includes:

[0026] Generate a gait training strategy for the target subject based on the health index, the gait data, and the gait pattern

[0027] The present invention has the following advantages due to the adoption of the above technical solution:

[0028] The gait pattern of the target object is predicted based on the pressure distribution data of each area of the sole of the target object transmitted by the sole pressure sensor and the inertial data of the target object transmitted by the inertial measurement unit. When the gait pattern is predicted to be an abnormal gait pattern, the gait abnormality alarm mode of the smart shoe is triggered, so that the smart shoe can provide real-time warning of the target object's fall risk, thereby reducing the target object's fall risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of the positions of components in a smart shoe according to one embodiment of the present invention;

[0030] Reference numerals:

[0031] 1. Plantar pressure sensor; 2. Inertial measurement unit; 3. Temperature and humidity sensor; 4. Optical heart rate sensor; 5. Infrared sensor; 6. Disinfection module. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by ordinary persons in this field based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0033] Unless otherwise defined, the technical or scientific terms used in the present invention shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second", "third", "fourth" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "include" or "comprise" mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0034] Traditional medical shoes have relatively simple functions, mainly providing basic foot support and protection. Although relevant technologies have not yet been popularized in terms of fall warning, considering that when patients are in hospitals, rehabilitation centers and other places, the risk of falling increases significantly due to decreased physical function and disease factors. According to statistics, the incidence of falls in hospitalized patients can reach 3% to 5%, which not only causes physical injuries, but also may cause serious complications such as fractures and craniocerebral injuries, prolonging hospitalization time and increasing medical costs. The lack of an effective fall warning system makes it difficult for medical staff to intervene in time and effectively prevent fall incidents. In response to the above technical problems, the present invention provides a smart shoe and its interactive system, which can predict the gait pattern of the target object. When the gait pattern is predicted to be an abnormal gait pattern, the gait abnormality alarm mode of the smart shoe is triggered, so that the smart shoe can provide a real-time warning of the risk of falling to the target object, thereby reducing the risk of falling for the target object. The technical solution of the present invention is described in detail below with reference to specific examples.

[0035] Reference Figure 1 As shown, the present invention relates to a smart shoe, comprising:

[0036] Plantar pressure sensor 1, used to collect pressure distribution data of various areas on the sole of the target object;

[0037] An inertial measurement unit 2, used to collect inertial data of the target object during its motion;

[0038] The controller is configured to receive the pressure distribution data of various areas of the sole transmitted by the sole pressure sensor 1 and the inertial data transmitted by the inertial measurement unit 2; predict the gait pattern of the target object based on the pressure distribution data of various areas of the sole and the inertial data; and trigger the gait abnormality alarm mode of the smart shoe if the gait pattern is abnormal.

[0039] For example, the target object can be a designated user or any user wearing smart shoes, which is not limited here. The plantar pressure sensor 1 is in the form of an array (such as an array of plantar pressure sensors 1) and is respectively placed between the insole and the sole of the smart shoe, so that the plantar pressure data of the target object can be collected more comprehensively. Specifically, ...

[0040] Illustratively, the inertial measurement unit 2 includes an accelerometer, a gyroscope, and a magnetometer. The accelerometer is used to measure the acceleration of an object in three axes; the gyroscope is used to measure the angular velocity of an object in three axes; and the magnetometer is used to measure the magnetic field strength of an object in three axes. The inertial measurement unit 2 is specifically integrated and arranged at a position corresponding to the shoe body and the instep.

[0041] Exemplarily, the controller pre-stores a gait pattern prediction model trained using a large language model (LLM). It should be noted that a large language model is a deep learning model trained on massive amounts of text data. Large language models possess three key capabilities: contextual learning, command following, and chain of thought reasoning. Contextual learning: LLMs learn from large amounts of text data to understand and utilize contextual information. This means the model can interpret subsequent content based on previous content, thereby better understanding and generating coherent language. For example, in a dialogue system, LLMs can generate more accurate responses based on previous conversation history, taking into account context and user intent. Command following: LLMs can follow human-provided instructions to perform specific tasks or generate specific types of output. By providing the model with explicit instructions as prompts, it can generate results consistent with the instructions. Prompts are textual descriptions that drive the large model to express itself. Prompts can be a phrase, a sentence, a paragraph, or even a complete question. They are typically created by human operators to clarify and convey specific intent or task requirements. The purpose of prompts is to guide the large language model to perform specific actions or generate specific types of output. This can include answering questions, completing tasks, writing articles, generating code, and more. Prompts clearly guide the expected behavior of large language models to ensure that the generated results meet expectations. Prompts can include contextual information so that the model can understand and generate relevant content. Context can include previous conversation history, background knowledge, or domain-specific constraints. By providing relevant context, prompts help the model better understand input and generate output.

[0042] In this embodiment, pressure distribution data (such as toe area pressure, forefoot area pressure, midfoot area pressure, and rearfoot area pressure) of various areas of the soles of multiple subjects who have fallen are pre-acquired from open source data, as well as inertial data (such as acceleration, angular velocity, and magnetic field strength) transmitted by the inertial measurement unit 2 as training data for the large language model, and prompts are set. For example, based on the pressure distribution data and inertial data of various areas of the soles of the feet, the gait pattern of the subject is determined in the following categories: center of gravity shift gait pattern, speed drop gait pattern, and normal gait pattern (i.e., the speed meets the speed interval and the center of gravity meets the center of gravity interval). The prompts and training data are then input into the large language model for training. The predicted gait pattern is compared with the standard pattern corresponding to the training data. The large language model is optimized based on the comparison results until the output predicted gait pattern is the same as the standard pattern, completing the training of the large language model and obtaining a gait pattern prediction model.

[0043] Therefore, after the controller obtains the pressure distribution data of each area of the sole of the foot transmitted by the sole pressure sensor 1 and the inertial data transmitted by the inertial measurement unit 2, the pressure distribution data of each area of the sole of the foot and the inertial data can be preprocessed first. The preprocessing includes outlier removal, low-pass filtering, etc. The preprocessed data and prompts are input into the gait pattern prediction model, and the corresponding gait pattern is output. In the case where the output gait pattern is a center of gravity shift gait pattern or a gait pattern with a sudden decrease in pace, the gait pattern is considered to be an abnormal gait pattern, triggering the gait abnormality alarm mode of the smart shoe. This is to remind the target object and the surrounding medical staff that the target object is at risk of falling, thereby reducing the risk of falling of the target object. Specifically, the gait abnormality alarm mode uses a combination of sound and vibration to alarm.

[0044] In the technical solution of the present application, the gait pattern of the target object is predicted based on the pressure distribution data of each area of the sole of the target object transmitted by the sole pressure sensor 1 and the inertial data of the target object transmitted by the inertial measurement unit 2. In this way, when the gait pattern is predicted to be an abnormal gait pattern, the gait abnormality alarm mode of the smart shoe is triggered, so that the smart shoe can provide real-time warning of the risk of falling for the target object, thereby reducing the risk of falling for the target object.

[0045] In one embodiment, the smart shoe further comprises: a temperature and humidity sensor 3 for collecting the temperature and humidity of the air in the smart shoe;

[0046] The controller is further configured to receive the temperature and humidity transmitted by the temperature and humidity sensor 3 ; and trigger the temperature and humidity abnormality alarm mode of the smart shoe when either the temperature or the humidity does not meet a preset temperature and humidity condition.

[0047] For example, the preset temperature and humidity conditions include the temperature meeting a preset temperature range and the humidity meeting a preset humidity range. The preset temperature range and the preset humidity range are set based on actual needs. For example, different preset temperature ranges and preset humidity ranges can be set for different seasons to ensure the rationality of the temperature and humidity alarms.

[0048] In this embodiment, the temperature and humidity sensor 3 is installed inside the shoe near the foot, such as on the tongue or insole, to monitor the temperature and humidity of the air inside the shoe in real time. After the temperature and humidity sensor 3 collects the temperature and humidity of the air inside the smart shoe, it transmits the temperature and humidity data to the controller in real time. If the controller detects that the temperature and humidity fall within the preset temperature and humidity ranges, respectively, the smart shoe's temperature and humidity are normal. If the controller detects that the temperature or humidity exceeds the preset temperature or humidity ranges, it triggers the smart shoe's abnormal temperature and humidity alarm mode. The abnormal temperature and humidity alarm mode uses an audible alarm, which differs from the abnormal gait alarm mode. This allows for timely detection of abnormal temperature and humidity in the smart shoe.

[0049] In one embodiment, the smart shoe further comprises: a flexible pressure sensor for collecting pressure data between the target object's ankle and the smart shoe;

[0050] The controller is further configured to receive pressure data transmitted by the flexible pressure sensor; determine a rate of change of the pressure data based on the pressure data within a preset time period; and determine a degree of foot edema of the target object based on the rate of change of the pressure data.

[0051] Exemplarily, the preset time period is a time interval set based on actual conditions and is not limited herein. For example, a 12-hour time period may be considered. Specifically, since the ankles of the feet are prone to edema, flexible pressure sensors are arranged in an array around the ankles of the target subject. In this embodiment, after the flexible pressure sensor array collects pressure data at the ankles, it transmits the pressure data to a controller in real time. The controller subtracts the pressure data at the end of the preset time period from the pressure data at the start time, and calculates the ratio of the difference to the duration of the preset time period to obtain the rate of change of the pressure data. Corresponding foot edema levels are pre-set based on different pressure change rate intervals, such as foot edema level 0 (i.e., no edema), foot edema level 1 (i.e., mild edema), foot edema level 2 (i.e., moderate edema), and foot edema level 3 (i.e., severe edema). After determining the rate of change of the pressure data, the corresponding pressure change rate interval is determined, thereby determining the corresponding foot edema level. This facilitates subsequent analysis of the target subject's health status.

[0052] Furthermore, when it is determined that the target object has level 3 foot edema, the foot edema alarm mode of the smart shoe can be triggered. The foot edema alarm mode is in the form of information, that is, a prompt message is sent to the target object.

[0053] In one embodiment, the smart shoe further comprises: an optical heart rate sensor 4 for detecting heart rate data of the target object;

[0054] The controller is further configured to receive heart rate data transmitted by the optical heart rate sensor 4 ; and trigger an abnormal heart rate alarm mode of the smart shoe when the heart rate data is not within a preset heart rate range.

[0055] For example, the preset heart rate interval is a heart rate interval set based on medical knowledge. In this embodiment, the optical heart rate sensor 4 is integrated into the insole at the arch of the foot and uses the principle of photoelectric reflection to measure heart rate by detecting changes in blood absorption of light. In this way, when the target subject wears the shoes, the sensor contacts the skin and can collect heart rate data in real time. When the controller obtains the heart rate data transmitted by the optical heart rate sensor 4, it compares it with the preset heart rate interval. If it is within the preset heart rate interval, it indicates that the target subject's heart rate is normal. If it is not within the preset heart rate interval, it indicates that the target subject's heart rate is abnormal, triggering the abnormal heart rate alarm mode of the smart shoe. The abnormal heart rate alarm mode is in the form of information, that is, a prompt message is sent to the target subject. This promptly reminds the target subject to take appropriate measures.

[0056] In one embodiment, the smart shoe further comprises: an infrared sensor 5 for detecting the blood oxygen saturation and blood flow velocity of the foot of the target object;

[0057] The controller is further configured to receive the blood oxygen saturation and blood flow velocity of the foot transmitted by the infrared sensor 5; and trigger the blood circulation abnormality alarm mode of the smart shoe when either the blood oxygen saturation or the blood flow velocity does not meet the preset blood oxygen saturation and blood flow velocity conditions.

[0058] Exemplarily, the preset blood oxygen saturation and blood flow velocity conditions include blood oxygen saturation greater than a preset blood oxygen saturation threshold and blood flow velocity greater than a preset blood flow velocity threshold, wherein the preset blood oxygen saturation threshold and the preset blood flow velocity threshold are set based on medical knowledge and are not limited here.

[0059] In this embodiment, infrared sensor 5 is located on the inside of the shoe to detect blood oxygen saturation and blood flow velocity in the foot. The infrared sensor 5 operates based on the absorption characteristics of human tissue for infrared light of different wavelengths. It transmits and receives infrared light to calculate blood oxygen content and blood flow velocity. The infrared sensor 5 transmits the collected blood oxygen saturation and blood flow velocity to the controller, enabling real-time monitoring of blood circulation.

[0060] Furthermore, the controller compares the blood oxygen saturation with a preset blood oxygen saturation threshold and the blood flow velocity with a preset blood flow velocity threshold. If the blood oxygen saturation and blood flow velocity both meet the preset blood oxygen saturation threshold and the preset blood flow velocity threshold, respectively, the target subject's blood circulation is normal. If the blood oxygen saturation is greater than the preset blood oxygen saturation threshold or the blood flow velocity is greater than the preset blood flow velocity threshold, the target subject's blood circulation is abnormal, triggering the smart shoe's abnormal blood circulation alarm mode. The abnormal blood circulation alarm mode sends a prompt message to the target subject, prompting the target subject to take appropriate measures in a timely manner.

[0061] In one embodiment, the controller is further used to classify foot health using the pressure data, the heart rate data, the blood oxygen saturation of the foot, the blood flow velocity, the temperature and the humidity, determine the foot health status of the target object, and generate a foot disease early warning strategy based on the foot health status.

[0062] Exemplarily, the foot health status includes multiple pre-classified abnormal foot health states. For example, one abnormal indicator corresponds to a primary abnormal foot health state, 2-3 abnormal indicators correspond to an intermediate abnormal foot health state, and more than 3 abnormal indicators correspond to an advanced abnormal foot health state. Different foot disease warning strategy information is sent to the target object according to the primary abnormal foot health state, the intermediate abnormal foot health state, and the advanced abnormal foot health state. For example, the foot disease warning strategy for the primary abnormal foot health state is that there are minor problems with foot health, and it is recommended to adjust lifestyle habits; the foot disease warning strategy for the intermediate abnormal foot health state is that diabetic foot ulcers may occur, and it is recommended to observe abnormal indicators; the foot disease warning strategy for the advanced abnormal foot health state is that the probability of diabetic foot ulcers is high, and it is recommended to seek medical treatment in time.

[0063] In this embodiment, corresponding thresholds or intervals are set in the controller for heart rate data, blood oxygen saturation of the foot, blood flow velocity, temperature and humidity. Therefore, when the heart rate data, blood oxygen saturation of the foot, blood flow velocity, temperature and humidity are obtained, the indicator is compared with its corresponding threshold or interval to determine whether there is an abnormality in the indicator, thereby determining the foot health status of the target object. A corresponding foot disease early warning strategy is sent to the target object based on the foot health status of the target object. In this way, by monitoring the above-mentioned multiple indicators, it is possible to pay attention to foot health from multiple dimensions, and to provide early warning of the risk of diabetic foot ulcers, so that patients can receive timely treatment, avoid worsening of the disease, reduce the incidence of diabetic foot ulcers, effectively protect the health of patients' feet, and reduce serious consequences such as amputation caused by diabetic foot.

[0064] In one embodiment, the smart shoe further includes: a disinfection module 6 for disinfecting the environment inside the smart shoe; and a controller for activating the disinfection module 6 when the pressure collected by the plantar pressure sensor 1 is zero.

[0065] Illustratively, disinfection module 6 is a UV-C disinfection module 6. It is installed on the inner sole of the shoe, typically beneath the insole. When the controller detects that the pressure transmitted by the sole pressure sensor 1 is zero, indicating that the subject has removed their shoes, the UV-C disinfection module 6 is activated. This module emits ultraviolet light, which can destroy the DNA structure of microorganisms, thereby effectively killing common pathogens in the shoe and effectively preventing the spread of pathogens. During the disinfection process, the UV-C lamp irradiates according to a preset program to ensure that every area of the shoe is fully disinfected.

[0066] The present invention further provides an interactive system for smart shoes, comprising: a server and the smart shoes disclosed in any of the above embodiments;

[0067] The smart shoe is used to send the gait pattern and inertial data obtained by the controller to the server;

[0068] The server is configured to receive the gait pattern and inertia data of the target object uploaded by the smart shoe; determine the gait data of the target object based on the inertia data, and generate a gait training strategy for the target object based at least on the gait pattern and the gait data.

[0069] For example, the server can be a cloud server or a local server. The server and the smart shoe communicate via a wireless communication module. In this embodiment, the wireless communication module uses Bluetooth. At the same time, the wireless communication module also supports connection with the target subject's mobile device (such as a smartphone), allowing the target subject to view their health data and receive warning information at any time.

[0070] Exemplarily, gait data includes: cadence, stride, and symmetry. Specifically, three-axis acceleration data is collected from the accelerometer, and the acceleration data is filtered (such as low-pass filtering) to remove high-frequency noise. The peaks in the acceleration signal are detected, which usually correspond to the moment when the foot touches the ground. The time intervals between adjacent peaks are calculated, and then the number of steps per minute is calculated to obtain the cadence. Data is collected from the accelerometer and gyroscope, and the acceleration data is integrated to obtain the speed and displacement. Then, by analyzing the displacement data, the length of each step is calculated to obtain the stride. Data is collected from the accelerometer and gyroscope, and the acceleration data is filtered and peak detected. Then, the parameters such as the cadence and stride of the left and right feet are compared to calculate the symmetry index (i.e., symmetry). In this way, personalized gait data can be analyzed based on the inertial data of different objects.

[0071] For example, prompts are set for the input gait pattern, and gait training strategies for muscle strength and balance ability are generated in combination with gait data. The analyzed gait data, gait pattern, and prompts are input into the strategy analysis model, and the strategy analysis model outputs the corresponding gait training strategy based on the prompts. In this way, personalized gait training strategies can be generated based on personalized gait data, thereby improving the rehabilitation effect of the target subject. Among them, the strategy analysis model is obtained by pre-training a large language model based on a large number of gait training strategies. That is, the strategy analysis model contains a large number of gait training strategies.

[0072] In one embodiment, the server is further configured to predict the health trend of the target object based on the health indicators received from the target object uploaded by the smart shoes; wherein at least one of the following health indicators includes: pressure data, heart rate data, blood oxygen saturation of the foot, blood flow velocity, temperature and humidity.

[0073] Exemplarily, the historical health indicators of different objects are used as training data, and the health levels corresponding to the historical health indicators are used as labels. The neural network model is trained using the training data, and the predicted results are compared with the labels. The neural network model is optimized according to the comparison results until the predicted results are the same as the labels, and the trained neural network model is output. In this embodiment, after obtaining the health indicators, the server stores them to facilitate subsequent calls to various indicators. Then, the health indicators are input into the trained neural network model to generate the corresponding predicted health level, and the last predicted health level is compared with the current predicted health level to determine the health trend of the target object, thereby effectively monitoring the physical condition of the target object and reminding the target object in a timely manner.

[0074] In one embodiment, generating a gait training strategy for the target subject based at least on the gait pattern and the gait data includes:

[0075] A gait training strategy for the target object is generated according to the health indicator, the gait data, and the gait pattern.

[0076] For example, prompts are set to target the input gait pattern, combining gait data and health indicators to generate a gait training strategy for muscle strength and balance ability. The analyzed gait data, gait pattern, health indicators, and prompts are input into the strategy analysis model, which then outputs a corresponding gait training strategy based on the prompts. In this way, a more comprehensive and personalized gait training strategy can be generated based on personalized gait data and health indicators, thereby improving the target patient's rehabilitation effect.

[0077] It should be understood that the above controller can be implemented in the form of a processor calling software. For example, the controller includes a processor, which is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or a microprocessor, and the memory can be a memory within the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits. The functions of some or all units can be realized by designing the hardware circuits. The hardware circuit can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units can be realized by designing the logical relationships between the components within the circuit. For another example, the hardware circuit can be implemented by a PLD. For example, an FPGA can include a large number of logic gate circuits. The connection relationships between the logic gate circuits are configured through a configuration file to realize the functions of some or all of the above units. All units of the above device can be implemented entirely in the form of a processor calling software, or entirely in the form of hardware circuits, or partially in the form of a processor calling software, with the remaining parts implemented in the form of hardware circuits.

[0078] It should be noted that in the embodiments of the present application, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a CPU, microprocessor, GPU, or DSP; in another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, DPU, etc.

[0079] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0080] In addition, the various units in the above apparatus may be fully or partially integrated together, or may be implemented independently. In one implementation, these units are integrated together and implemented in the form of a system-on-chip (SOC). The SOC may include at least one processor for implementing any of the above methods or implementing the functions of the various units of the apparatus. The at least one processor may be of different types, such as a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0081] For the sake of simplicity, the aforementioned method embodiments are described as a series of action combinations. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0082] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similarities between the various embodiments can be referred to in conjunction with each other. For device embodiments, since they are generally similar to method embodiments, their description is relatively simple, and for relevant details, reference can be made to the description of the method embodiments.

[0083] The steps in the methods of each embodiment of the present application can be adjusted in sequence, merged, and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0084] The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be merged, divided, and deleted according to actual needs.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or submodules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0086] The modules or submodules described as separate components may or may not be physically separate, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules may be selected to achieve the purpose of this embodiment according to actual needs.

[0087] In addition, each functional module or submodule in each embodiment of the present application may be integrated into a processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into a single module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or software functional modules or submodules.

[0088] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0089] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, software units executed by a processor, or a combination of the two. The software units may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A smart shoe, characterized in that: include: A plantar pressure sensor is used to collect pressure distribution data of various areas on the sole of the target object; an inertial measurement unit, configured to collect inertial data of the target object during its motion; The controller is configured to receive pressure distribution data of various areas of the sole of the foot transmitted by the sole pressure sensor and inertial data transmitted by the inertial measurement unit; predict the gait pattern of the target object based on the pressure distribution data of various areas of the sole of the foot and the inertial data; and trigger a gait abnormality alarm mode of the smart shoe if the gait pattern is abnormal.

2. The smart shoe according to claim 1, characterized in that: The smart shoe further comprises: a temperature and humidity sensor for collecting the temperature and humidity of the air in the smart shoe; The controller is further configured to receive the temperature and humidity transmitted by the temperature and humidity sensor; and trigger the temperature and humidity abnormality alarm mode of the smart shoe when either the temperature or the humidity does not meet a preset temperature and humidity condition.

3. The smart shoe according to claim 2, characterized in that: The smart shoe further comprises: a flexible pressure sensor for collecting pressure data between the target object's ankle and the smart shoe; The controller is further configured to receive pressure data transmitted by the flexible pressure sensor; determine a rate of change of the pressure data based on the pressure data within a preset time period; and determine a degree of foot edema of the target object based on the rate of change of the pressure data.

4. The smart shoe according to claim 3, characterized in that: The smart shoe further includes: an optical heart rate sensor for detecting heart rate data of the target object; The controller is further configured to receive heart rate data transmitted by the optical heart rate sensor; and trigger an abnormal heart rate alarm mode of the smart shoe when the heart rate data is not within a preset heart rate range.

5. The smart shoe according to claim 4, characterized in that: The smart shoe further includes: an infrared sensor for detecting the blood oxygen saturation and blood flow velocity of the foot of the target object; The controller is further configured to receive the blood oxygen saturation and blood flow velocity of the foot transmitted by the infrared sensor; and trigger a blood circulation abnormality alarm mode of the smart shoe when either the blood oxygen saturation or the blood flow velocity does not meet preset blood oxygen saturation and blood flow velocity conditions.

6. The smart shoe according to claim 5, characterized in that: The controller is further configured to perform health classification using the pressure data, the heart rate data, the blood oxygen saturation of the foot, the blood flow velocity, the temperature, and the humidity to determine the preliminary health status of the target object.

7. The smart shoe according to any one of claims 1 to 6, characterized in that: The smart shoe further comprises: a disinfection module for disinfecting the environment inside the smart shoe; The controller is further configured to start the disinfection module when the pressure transmitted by the plantar pressure sensor is zero.

8. An interactive system for smart shoes, characterized in that: include: A server and a smart shoe according to any one of claims 1 to 7; The smart shoe is used to send the gait pattern and inertial data obtained by the controller to the server; The server is configured to receive the gait pattern and inertia data of the target object uploaded by the smart shoe; determine the gait data of the target object based on the inertia data, and generate a gait training strategy for the target object based at least on the gait pattern and the gait data.

9. The interactive system of smart shoes according to claim 8, characterized in that: The server is further configured to predict the health trend of the target object based on the health indicators received from the smart shoes; wherein at least one of the following health indicators includes: pressure data, heart rate data, blood oxygen saturation of the foot, blood flow velocity, temperature and humidity.

10. The interactive system of smart shoes according to claim 9, characterized in that: Generating a gait training strategy for the target subject based at least on the gait pattern and the gait data, including: A gait training strategy for the target object is generated according to the health indicator, the gait data, and the gait pattern.

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