Intelligent detection and digital motion rehabilitation module of scoliosis orthosis

Abstract

The utility model discloses a scoliosis orthosis intelligent detection and digital motion rehabilitation module which comprises an orthosis attached to a human body structure and an ergonomic bandage structure connected with the orthosis and used for being worn and installed, and a plurality of sensor modules are arranged on the ergonomic bandage structure and / or the orthosis. The sensor module comprises a pressure sensor, and the sensor module is interconnected with the data processing unit and the control unit. The utility model discloses an intelligent detection and digital motion rehabilitation module for a scoliosis orthosis, which realizes real-time monitoring, remote guidance and personalized use scheme creation for a scoliosis orthosis user through the integration of an intelligent orthosis, a data processing platform and cloud service, thereby remarkably improving the use effect.

Description

Technical Field

[0001] This utility model relates to the field of medical equipment technology, and in particular to an intelligent detection and digital motion rehabilitation module for scoliosis orthotics, especially an intelligent monitoring system for the correction and rehabilitation of scoliosis in children and adolescents. Background Technology

[0002] Scoliosis is a common orthopedic condition affecting a large global population, particularly adolescents. Current treatment options primarily include physical therapy and orthotic braces. Statistics show that wearing braces for 23 hours daily yields a 93% success rate, while wearing them for 16 hours results in only a 62% success rate. Currently, orthotics have evolved from traditional handmade designs to those using laser profiling and CAD / CAM processing; some orthotics even utilize digital software design and 3D printing technology. However, traditional orthotics lack intelligent monitoring of corrective effects and reminders for wearing duration, making it difficult to ensure adherence and often preventing patients from achieving optimal corrective results. Furthermore, current rehabilitation exercise guidance typically relies on manual supervision, lacking precise movement guidance when patients self-rehabilitation at home, thus impacting treatment effectiveness.

[0003] Some international institutions (such as DuPont Children's Hospital in the United States) and domestic research institutions (such as West China Hospital of Sichuan University) have begun to explore integrating sensors into orthotics to monitor wearing conditions. However, these solutions have not yet been widely commercialized. Utility Model Content

[0004] This utility model overcomes the shortcomings of the prior art and provides a scoliosis orthosis intelligent detection and digital motion rehabilitation module. Through the integration of intelligent orthosis, data processing platform and cloud services, it realizes real-time monitoring of scoliosis, remote guidance and creation of personalized use plans, thereby significantly improving the use effect.

[0005] To achieve the above objectives, the technical solution adopted by this utility model is as follows: a smart detection and digital motion rehabilitation module for scoliosis orthotics, comprising: an orthotics that conforms to the human body structure, and an ergonomic strap structure connected to the orthotics for wear and installation. The ergonomic strap structure and / or the orthotics are provided with a plurality of sensor modules, which are interconnected with a data processing unit and a control unit. The sensor modules include a pressure sensor module, which is embedded in the installation area of ​​the orthotics and is attached to the orthotics.

[0006] In a preferred embodiment of this utility model, the pressure sensor module includes: an Internet of Things (IoT) control module interconnected with the data processing unit and the control unit; the IoT control module is connected to the pressure sensor, the USB-UART module, the Type-C interface, the LED indicator, and the battery management module; the battery management module includes a power management chip, and a charging management module and a power supply management module connected to the power management chip.

[0007] In a preferred embodiment of this invention, the IoT control module includes an IoT ESP32-C6 chip. The IO1 and IO2 ports of the ESP32-C6 chip are connected to both ends of a crystal oscillator, and both ends of the crystal oscillator are grounded via capacitors. Several output terminals of the ESP32-C6 chip are connected to pressure sensors, and the ESP32-C6 chip is also connected to a data download chip, a BOOT button, and an RST button.

[0008] In a preferred embodiment of this utility model, the power management chip is an AO3401A MOSFET chip. The input terminal of the power management chip is connected to the VBUS terminal through switch SW1 and inverting diode D1, and the non-inverting terminal of inverting diode D1 is grounded. The two ends of inverting diode D1 are connected to transistor Q1, and are connected to the VBAT terminal through transistor Q1. Capacitors C1 and C2 are connected in parallel between the input terminals of the power management chip and are grounded at the same point. The output terminal of the power management chip outputs a power supply of 3.3V.

[0009] In a preferred embodiment of this utility model, the charging management module includes a TP4056 chip connected to the VBUS terminal. The TP4056 chip is also connected to the VBUS terminal through an LED indicator group. The VBAT pin of the TP4056 chip is grounded, and the VBAT pin is connected to the interface U4 as the VBAT terminal.

[0010] In a preferred embodiment of this utility model, the USB-UART module includes a US-CP2102N chip. The US-CP2102N chip is connected to the VBUS terminal through a resistor R10, and the VDD terminal of the US-CP2102N chip is connected to the 3.3V power supply of the battery management module and grounded through a capacitor C12. The TXD and RXD terminals of the US-CP2102N chip are respectively connected to the RX and TX terminals of the IoT ESP32-C6 chip.

[0011] In a preferred embodiment of this utility model, the Type-C interface includes a KH-TYPE-C chip connected to the VBUS terminal, and the KH-TYPE-C chip also leads out a USB interface terminal.

[0012] This utility model solves the defects existing in the background technology and has the following beneficial effects:

[0013] This utility model discloses an intelligent detection and digital motion rehabilitation module for scoliosis orthotics. By integrating an intelligent orthotics, a data processing platform, and cloud services, it enables real-time monitoring, remote guidance, and creation of personalized usage plans for scoliosis, thereby significantly improving the effectiveness of use. Attached Figure Description

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Figure 1 This is a schematic diagram of a circuit board for an intelligent detection and digital motion rehabilitation module for a scoliosis orthosis, which is a preferred embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of an ESP chip for an intelligent detection and digital motion rehabilitation module of a scoliosis orthosis, which is a preferred embodiment of the present invention.

[0017] Figure 3 A circuit diagram of a preferred embodiment of the present invention for an intelligent detection and digital motion rehabilitation module for a scoliosis orthosis.

[0018] Figure 4 This is an enlarged circuit diagram of the battery management module in the pressure sensor circuit diagram of a scoliosis orthosis intelligent detection and digital motion rehabilitation module, which is a preferred embodiment of this utility model.

[0019] Figure 5 This is an enlarged circuit diagram of the Internet of Things control module in the pressure sensor circuit diagram of a scoliosis orthosis intelligent detection and digital motion rehabilitation module, which is a preferred embodiment of the present invention.

[0020] Figure 6 The circuit diagram of the USB-UART module and the amplification circuit of the Type-C interface in the pressure sensor circuit diagram of the intelligent detection and digital motion rehabilitation module of the scoliosis orthosis according to a preferred embodiment of the present utility model.

[0021] Figure 7 The above is an enlarged circuit diagram of the sensor module in the pressure sensor circuit diagram of the intelligent detection and digital motion rehabilitation module of the scoliosis orthosis, which is a preferred embodiment of the present invention.

[0022] Figure 8 The LED indicator, button, and magnified circuit diagram of the automatic download module are shown in the circuit diagram of the pressure sensor of the intelligent detection and digital motion rehabilitation module of the scoliosis orthosis according to a preferred embodiment of the present invention.

[0023] Figure 9Photograph of a scoliosis orthosis with sensors, which is a preferred embodiment of the present invention;

[0024] Figure 10 This is a flowchart of the fuzzy neural network control algorithm for an intelligent detection and digital motion rehabilitation module for a scoliosis orthosis, which is a preferred embodiment of this utility model. Detailed Implementation

[0025] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. These drawings are simplified schematic diagrams, which are only used to illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention. Example 1

[0026] like Figures 1-10 As shown, a smart detection and digital motion rehabilitation module for scoliosis orthotics includes: an orthotics that conforms to the human body structure, and an ergonomic strap structure connected to the orthotics for wear and installation. The ergonomic strap structure includes a back-correcting body that conforms to the human body, and several ergonomic straps connected to the back-correcting body. Connectors are provided on the ergonomic straps for positioning the ergonomic strap structure; the connectors are Velcro or straps. Several sensor modules are provided on the ergonomic strap structure and / or the orthotics. The sensor modules are interconnected with a data processing unit and a control unit. The sensor modules include pressure sensor modules, which are embedded in the installation area of ​​the orthotics and are attached to the orthotics by adhesive components. The pressure sensors of the pressure sensor modules are attached to the detection area, and the force detection surface is in contact with the human body detection area. Further, the adhesive components include adhesive on the outer shell of the pressure sensor module and the ergonomic strap structure.

[0027] Specifically, the pressure sensor module includes: an IoT control module interconnected with the data processing unit and the control unit, the IoT control module being connected to the pressure sensor, the USB-UART module, the Type-C interface, the LED indicator and the battery management module respectively; the battery management module includes a power management chip, as well as a charging management module and a power supply management module connected to the power management chip.

[0028] Specifically, the IoT control module includes an IoT ESP32-C6 chip. The IO1 and IO2 ports of the ESP32-C6 chip are connected to both ends of a crystal oscillator, which is also grounded via capacitors. Several outputs of the ESP32-C6 chip are connected to pressure sensors, and the ESP32-C6 chip is also connected to a data download chip, a BOOT button, and an RST button.

[0029] Specifically, the power management chip uses an AO3401A MOSFET chip. The input terminal of the power management chip is connected to the VBUS terminal through switch SW1 and inverting diode D1. The non-inverting terminal of inverting diode D1 is grounded, and the two ends of inverting diode D1 are connected to transistor Q1, which is connected to the VBAT terminal through transistor Q1. Capacitors C1 and C2 are connected in parallel between the input terminals of the power management chip and are grounded at the same point. The output terminal of the power management chip outputs a 3.3V power supply.

[0030] Specifically, the charging management module includes a TP4056 chip connected to the VBUS terminal. The TP4056 chip is also connected to the VBUS terminal via an LED indicator group. The VBAT pin of the TP4056 chip is grounded, and the VBAT pin is connected to the interface U4 as the VBAT terminal.

[0031] Specifically, the USB-UART module includes a US-CP2102N chip. The US-CP2102N chip is connected to the VBUS terminal through resistor R10, and the VDD terminal of the US-CP2102N chip is connected to the 3.3V power supply of the battery management module and grounded through capacitor C12. The TXD and RXD terminals of the US-CP2102N chip are connected to the RX and TX terminals of the IoT ESP32-C6 chip, respectively.

[0032] Specifically, the Type-C interface includes a KH-TYPE-C chip that connects to the VBUS end, and the KH-TYPE-C chip also leads out to the USB interface end. Example 2

[0033] Based on Example 1, such as Figures 1-10 As shown, the ergonomic strap structure can be personalized according to the patient's body characteristics. The pressure sensor adopts an ultra-thin flexible force sensor from existing technology, installed near the main orthopedic pressure area of ​​the orthosis without affecting the original force design. Furthermore, a preferred option is an ultra-thin flexible force sensor with a thickness of 0.3mm and a maximum pressure of 20N, currently available in existing technology. However, this is not the only option; in other embodiments, other pressure sensor models can be selected according to actual usage requirements; the specific selection of pressure sensor models will not be listed here. The battery is USB rechargeable. The ergonomic strap structure can directly adopt existing ergonomic strap structures with multiple detachable units, which can be removed or replaced to meet the user's personalized needs. The lightweight design of each component and the adhesive-mounted sensor allow for direct installation within the ergonomic strap structure, facilitating rehabilitation exercises in medical gymnastics, or modularly embedding it into existing scoliosis orthoses for monitoring wearing time and tightness.

[0034] Furthermore, such as Figure 9The diagram illustrates an orthotic structure that conforms to the human body structure in this embodiment. The circuit board of the orthotic structure is mounted on the orthotic, and the pressure sensor is located inside the orthotic. In this embodiment, the orthotic adopts a sleeve-type structure that is easy to put on and take off and conforms to the human body structure. The sleeve-type structure also has several through holes. These through holes facilitate weight reduction, easy gripping and putting on / taking off, and adjustment of the sensor and circuit board positions, as well as installation and removal. Furthermore, the outer periphery and edges of the orthotic are designed with a streamlined structure; and clearance grooves are provided at the points of contact with the human joints to facilitate flexible movement during use and improve wearing comfort. Example 3

[0035] Based on Example 1 or Example 2, such as Figures 1-10 As shown, the IoT control module includes an IoT ESP32-C6 chip. The IO1 and IO2 ports of the ESP32-C6 chip are connected to both ends of a crystal oscillator, which is also grounded via capacitors. Several outputs of the ESP32-C6 chip are connected to pressure sensors, and the chip is also connected to a data download chip, a BOOT button, and an RST button.

[0036] Specifically, the power management chip uses an AO3401A MOSFET chip. The input terminal of the power management chip is connected to the VBUS terminal through switch SW1 and inverting diode D1. The non-inverting terminal of inverting diode D1 is grounded, and the two ends of inverting diode D1 are connected to transistor Q1, which is connected to the VBAT terminal through transistor Q1. Capacitors C1 and C2 are connected in parallel between the input terminals of the power management chip and are grounded at the same point. The output terminal of the power management chip outputs a 3.3V power supply.

[0037] Specifically, the charging management module includes a TP4056 chip connected to the VBUS terminal. The TP4056 chip is also connected to the VBUS terminal via an LED indicator group. The VBAT pin of the TP4056 chip is grounded, and the VBAT pin is connected to the interface U4 as the VBAT terminal.

[0038] Specifically, the USB-UART module includes a US-CP2102N chip. The US-CP2102N chip is connected to the VBUS terminal through resistor R10, and the VDD terminal of the US-CP2102N chip is connected to the 3.3V power supply of the battery management module and grounded through capacitor C12. The TXD and RXD terminals of the US-CP2102N chip are connected to the RX and TX terminals of the IoT ESP32-C6 chip, respectively.

[0039] Specifically, the Type-C interface includes a KH-TYPE-C chip that connects to the VBUS end, and the KH-TYPE-C chip also leads out to the USB interface end.

[0040] Specifically, the battery management module provides stable power management and charging functions for the system. The VBUS port connects to the USB-C interface and uses a 1N5819 Schottky diode for reverse current protection, ensuring system safety during charging. LED1 and LED2 are charging indicator lights, displaying charging and full charge status respectively, allowing users to monitor battery status in real time. The AMS1117-3.3V voltage regulator chip converts the VBUS voltage to the system's 3.3V power supply, ensuring stable operation of all system modules. The ESP32-C6 is the system's main control chip, integrating Wi-Fi and Bluetooth dual-mode modules, supporting real-time data transmission and remote communication. It primarily connects to pressure sensors to receive and process real-time data from various sensors. The I / O ports control sensors, LEDs, and other peripheral modules via GPIO, ensuring rapid system response and data acquisition. Example 4

[0041] Based on any of the embodiments in Examples 1 to 3, such as Figures 1-10 As shown, a scoliosis orthosis intelligent detection and digital motion rehabilitation module includes: an orthosis that conforms to the human body structure, and an ergonomic strap structure connected to the orthosis for wear and installation. The ergonomic strap structure and / or the orthosis are provided with a number of sensor modules, including pressure sensors. The sensor modules are interconnected with a data processing unit and a control unit.

[0042] Specifically, the data processing unit includes a Kalman filter algorithm, which is used to process sensor data acquired by the sensor module in real time. The control unit includes an intelligent control system based on a fuzzy neural network. Based on typical real three-dimensional data of scoliosis, a biomechanical finite element model of scoliosis is established. This model contains biomechanical data, including the mechanical data of the spine, muscles, and ligaments. Based on this biomechanical data, the influencing factors of scoliosis are analyzed using biomechanical data analysis methods.

[0043] Specifically, biomechanical data analysis methods include:

[0044] The back load and corresponding body response were tested based on a skeletal dynamics model under typical and training conditions. Typical conditions included walking on flat ground, running, and sitting still, while training conditions included the condition during medical gymnastics rehabilitation.

[0045] Import the corresponding orthodontic model into the skeletal dynamics model, set the force application conditions for the orthodontic model, and simulate the simulation state of the coupling between different types of scoliosis orthodontics and the human body.

[0046] Calculate the force, momentum, and kinematic parameters during the simulated process to perform dynamic simulation;

[0047] Constraints are introduced on the joints of the skeletal dynamics model to simulate the biomechanical behavior of the human body in static states, such as sleeping and sitting postures, and to perform static simulation.

[0048] Specifically, the establishment of the skeletal dynamics model includes: establishing a corrective strap support system based on the kinematic analysis of the human spinal center, establishing a human-machine closed-chain mechanical analysis model, calculating the force of the orthosis on the human joints through interactive forces, and evaluating the compatibility of the orthosis with the human body.

[0049] Furthermore, the state equations and measurement equations of the Kalman filter algorithm include: S k =AS k-1 +w k-1 ;c k =HS k +v k ; where: S k S is the predicted value of the system model at time k; k-1 Here, A represents the analytical value of the system model at time k-1; A is the system model transition matrix; w k-1 For observation noise; c k v is the measured value of the system model at time k; H is the measurement matrix of the system model; k For measuring noise.

[0050] Furthermore, suppose w k-1 and v k They are Gaussian white noises that are independent of each other and have zero variances Q and R, respectively.

[0051] Specifically, the ergonomic strap structure is combined with spinal biomechanical parameters to adjust different correction states according to different scoliosis conditions; the data processing unit uses the Kalman filter algorithm to derive the correct measurement value by estimating, measuring, and correcting the sensor noise of the sensor module; the Kalman filter algorithm stabilizes the data and reduces signal disturbance of the measurement system when it is in standby mode.

[0052] Furthermore, the fuzzy neural network uses a fuzzy control algorithm to fuzzify the pressure signal and inputs it into the fuzzy inference engine based on empirical decision criteria. The fuzzy inference engine identifies the data and transmits it to the knowledge base for storage. The output quantity is then converted into executable commands through the output quantity declarification module to achieve intelligent control.

[0053] Specifically, fuzzy logic systems mimic macroscopic human intelligent behavior, while neural networks mimic microscopic human intelligent behavior. Combining the knowledge representation capabilities of fuzzy logic with the self-learning capabilities of neural networks, fuzzy neural network control systems can effectively handle precise control problems in complex systems.

[0054] The control algorithm of the fuzzy neural network includes: e k =y r -y k ;Δe k =e k -e k-1 ; where e k The output value at time k is the error between the measured value and the set value, e k y represents the change in error. r It is the input quantity of the fuzzy neural network, y k It is the regulation and control variable of the fuzzy neural network feedback.

[0055] Specifically, the control unit automatically collects pressure data and combines it with information such as the patient's age, gender, height, weight, spinal morphology, and scoliosis degree for standardized processing. Through standardized data formats and databases, the data can be quickly stored and retrieved. Ultimately, the machine learning-based scoliosis status assessment system can quickly and accurately diagnose and treat scoliosis, and is used in a cloud-based data analysis system.

[0056] Example 5

[0057] Based on any of the embodiments in Examples 1 to 4, such as Figures 1-10 As shown, the method of using the intelligent detection and digital motion rehabilitation module for scoliosis orthotics, implemented using the intelligent detection and digital motion rehabilitation module for scoliosis orthotics in Example 1, includes the following steps:

[0058] Step 1: Acquire ergonomic data collected by the sensor module and preprocess the collected ergonomic data. The preprocessing includes deduplication and outlier removal, and then obtain the preprocessed ergonomic data.

[0059] Step 2: Extract features from the preprocessed ergonomic data, including key features reflecting the scoliosis status.

[0060] Step 3: Train the model based on the extracted features: Use a Support Vector Machine (SVM) to train the feature data; verify the accuracy and stability of the model, compare different models, and select the optimal model;

[0061] Step 4: Optimize the system based on the trained model. System optimization includes algorithm optimization and model structure optimization.

[0062] The orthosis is equipped with an ESP32-C6 chip, which collects wearing data in real time via pressure sensors. The intelligent detection and digital motion rehabilitation module of the scoliosis orthosis processes the data using a Kalman filter algorithm to remove interference and ensure accuracy. It iteratively derives the correct measurement value through a process of estimation, measurement, and correction. The Kalman filter algorithm makes the data more stable, solving the problem of signal disturbance during standby. The internal system of the orthosis uses a fuzzy control algorithm to fuzzify the pressure signal and input it into a fuzzy inference engine based on empirical decision criteria. The fuzzy inference engine identifies the data and stores it in a knowledge base. The output declarative module converts it into executable commands to achieve intelligent control. The fuzzy logic system mimics macroscopic human intelligent behavior, while the neural network mimics microscopic human intelligent behavior.

[0063] The intelligent detection and digital motion rehabilitation module of the scoliosis orthosis collects pressure data and combines it with information such as the patient's age, gender, height, weight, spinal morphology, and degree of scoliosis for standardized processing. Through standardized data formats and databases, the data can be quickly stored and retrieved. Ultimately, the machine learning-based scoliosis status assessment system can be used quickly and accurately, and is also integrated into a cloud-based data analysis system.

[0064] Working principle:

[0065] like Figures 1-10 As shown, this utility model discloses an intelligent detection and digital motion rehabilitation module for scoliosis orthotics, addressing the problems of low orthodontic compliance and lack of standardized training guidance in existing technologies. Through the integration of an intelligent orthodontic device, a data processing platform, and cloud services, it enables real-time monitoring, remote guidance, and personalized usage plan creation for individuals with scoliosis, thereby significantly improving the effectiveness of use.

[0066] This invention improves orthotic adherence by recording and providing pressure feedback. Based on the wearing time and the specific pressure parameters recorded and fed back, personalized training programs can be developed to guide scientific wearing and improve performance. A cloud-based data analysis system allows caregivers to remotely track wearing status and progress, and optimize usage plans. The modular ergonomic design enhances wearing comfort, especially reducing discomfort at night, thus increasing adherence.

[0067] Based on the preferred embodiments of this utility model, and through the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of this utility model. The technical scope of this utility model is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A scoliosis orthosis intelligent detection and digital motion rehabilitation module, comprising: An orthosis which is in conformity with the structure of a human body, and an ergonomic band structure which is connected with the orthosis and is used for wearing and installing, the ergonomic band structure and / or the orthosis is provided with a plurality of sensor modules, the sensor modules are interconnected with a data processing unit and a control unit; characterized in that the sensor modules comprise a pressure sensor module, the pressure sensor module is embedded in an installation area of the orthosis, and the pressure sensor module is pasted on the orthosis.

2. The scoliosis orthosis intelligent detection and digital motion rehabilitation module of claim 1, wherein: The pressure sensor module comprises an Internet of Things control module which is interconnected with the data processing unit and the control unit, the Internet of Things control module is connected with the pressure sensor, a USB-UART module, a Type-C interface, an LED indicator and a battery management module respectively. The battery management module comprises a power management chip, and a charging management module and a power supply management module which are connected with the power management chip.

3. The scoliosis orthosis intelligent detection and digital motion rehabilitation module of claim 2, wherein: The Internet of Things control module comprises an Internet of Things ESP32-C6 chip, IO1 and IO2 ports of the Internet of Things ESP32-C6 chip are connected with two ends of a crystal oscillator, the two ends of the crystal oscillator are also grounded through capacitors respectively, a plurality of output ends of the Internet of Things ESP32-C6 chip are connected with the pressure sensor respectively, and the Internet of Things ESP32-C6 chip is also connected with a data download chip and a BOOT button and a RST button.

4. The scoliosis orthosis smart detection and digital motion rehabilitation module of claim 3, wherein: The power management chip adopts an AO3401A MOSFET chip, an input end of the power management chip is connected with a VBUS end through a switch SW1 and a reverse diode D1, a positive end of the reverse diode D1 is grounded, two ends of the reverse diode D1 are connected with a triode Q1, and the triode Q1 is connected with a VBAT end, capacitors C1 and C2 are connected in parallel between the input end of the power management chip and are grounded at a common point; an output end of the power management chip outputs a power supply 3.3V.

5. The scoliosis orthosis smart detection and digital motion rehabilitation module of claim 4, wherein: The charging management module comprises a TP4056 chip which is connected with the VBUS end, the TP4056 chip is also connected with the VBUS end through an LED indicator group, a VBAT pin of the TP4056 chip is grounded, and the VBAT pin is connected with an interface U4 as a VBAT end.

6. The scoliosis orthosis intelligent detection and digital motion rehabilitation module of claim 5, wherein: The USB-UART module comprises a US-CP2102N chip, the US-CP2102N chip is connected with the VBUS end through a resistor R10, a VDD end of the US-CP2102N chip is connected with the power supply 3.3V of the battery management module and is grounded through a capacitor C12, and a TXD end and an RXD end of the US-CP2102N chip are connected with an RX end and a TX end of the Internet of Things ESP32-C6 chip of the Internet of Things control module respectively.

7. The scoliosis orthosis smart detection and digital motion rehabilitation module of claim 6, wherein: The Type-C interface comprises a KH-TYPE-C chip which is connected with the VBUS end, and the KH-TYPE-C chip further leads out a USB interface end.

8. The scoliosis orthosis smart detection and digital motion rehabilitation module of claim 7, wherein: The sensor module further comprises a pressure sensor which is interconnected with the data processing unit and the control unit and is connected with the Internet of Things ESP32-C6 chip of the Internet of Things control module.

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