Wearable intelligent sensor and AI linkage scoliosis real-time posture monitoring device

Abstract

The invention relates to the technical field of scoliosis monitoring, in particular to a wearable intelligent sensor and AI linked scoliosis real-time posture monitoring device which comprises a wearable part, a monitoring part and an AI data processing part, and the monitoring part comprises a flexible sensor array, a heart rate module, a data acquisition module, a microprocessor and a wireless communication module. The flexible sensor array is fixedly connected with the wearing part, the data acquisition module is electrically connected with the flexible sensor array, the microprocessor is electrically connected with the data acquisition module, the AI data processing part comprises a cloud server and a terminal application program, the cloud server communicates with the wireless communication module, the terminal application program communicates with the cloud server, and the wireless communication module communicates with the wireless communication module. Therefore, the spine data can be accurately monitored in real time, deep correlation analysis is carried out on the spine posture data and the heart rate data of the user based on the AI algorithm, comprehensive and accurate data support is provided for judging scoliosis, and the accuracy of spine monitoring is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of scoliosis monitoring technology, and in particular to a wearable smart sensor and AI-linked real-time scoliosis posture monitoring device. Background Technology

[0002] Scoliosis is a common spinal deformity, with a particularly high incidence among adolescents. Early detection and intervention are crucial for the treatment of scoliosis. Current wearable scoliosis devices lack initiative: most existing orthotics use a passive shaping correction method, requiring patients to wear the device long-term to restrict spinal movement through external force. However, this method often ignores the patient's ability to actively adjust their posture. Long-term reliance on orthotics may lead to muscle weakness and decreased behavioral compliance, especially in terms of limiting the patient's mobility and negatively impacting physical development.

[0003] To address the aforementioned issues, existing patent (CN119908887A) discloses an active reminder wearable device for scoliosis correction, comprising a monitoring controller and multiple reminders. The monitoring controller consists of an angle sensing module, a horizontal detection module, and a logic processing module, used to detect angle changes along the shoulder and back detection lines and the iliac crest detection line, and compare these angles with the target range. When a deviation is detected, the logic processing module sends a signal to the reminders via a communication module. The reminders provide feedback through vibration or low-frequency stimulation, inducing the wearer to actively adjust their posture. This device overcomes the shortcomings of passive correction, enhances the corrective effect, and combines static and dynamic correction functions, monitoring the patient's posture changes during movement in real time. Its lightweight design improves wearing comfort and compliance, making it suitable for patients with mild scoliosis and poor posture, especially children, helping to correct posture, strengthen muscles, and develop good posture habits, significantly improving treatment outcomes.

[0004] However, among the existing technologies mentioned above, wearable devices have problems such as insufficient sensor accuracy, limited data processing capabilities, and insufficient integration with AI technology in monitoring scoliosis, which cannot meet the high requirements of clinical and daily monitoring. Summary of the Invention

[0005] The purpose of this invention is to provide a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis, which solves the problems of insufficient sensor accuracy, limited data processing capabilities and insufficient integration with AI technology in existing wearable devices for monitoring scoliosis, thus failing to meet the high requirements of clinical and daily monitoring.

[0006] To achieve the above objectives, the present invention employs a wearable smart sensor and AI-linked real-time scoliosis posture monitoring device, comprising a wearable part, a monitoring part, and an AI data processing part. The monitoring part is disposed on the outside of the wearable part, and the data transmitted by the monitoring part is transmitted to the AI ​​data processing part. The device is characterized in that... The monitoring unit includes a flexible sensor array, a heart rate module, a data acquisition module, a microprocessor, and a wireless communication module. The flexible sensor array is fixedly connected to the wearable part and located on the outside of the wearable part. The heart rate module is fixedly connected to the wearable part and located on the side of the wearable part away from the flexible sensor array. The data acquisition module is electrically connected to the flexible sensor array and located below the flexible sensor array. The microprocessor is electrically connected to the data acquisition module and located inside the data acquisition module. The wireless communication module is electrically connected to the microprocessor and located on the outside of the microprocessor. The AI ​​data processing unit includes a cloud server and a terminal application. The cloud server communicates with the wireless communication module, and the terminal application communicates with the cloud server.

[0007] The wearable part includes an elastic bodysuit and an open zipper. The flexible sensor array is provided at the rear end of the elastic bodysuit, and an anti-slip layer is provided on the inner side of the elastic bodysuit. The open zipper is fixedly connected to the elastic bodysuit and is located at the front end of the elastic bodysuit.

[0008] The flexible sensor array includes a bending sensor and a flexible pressure sensor. The bending sensor is fixedly connected to the rear end of the elastic bodysuit and is located on the outside of the elastic bodysuit. The flexible pressure sensor is fixedly connected to the elastic bodysuit and is located at the position where the elastic bodysuit fits the human body.

[0009] The flexible sensor array further includes a triaxial accelerometer and a gyroscope. The triaxial accelerometer is fixedly connected to the bending sensor and located outside the bending sensor. The gyroscope is fixedly connected to the elastic bodysuit and located outside the triaxial accelerometer.

[0010] The heart rate module includes a heart rate sensor and a first signal amplifier. The heart rate sensor is fixedly connected to the elastic bodysuit and is located on the side of the elastic bodysuit away from the flexible sensor array. The first signal amplifier is electrically connected to the heart rate sensor and is located outside the heart rate sensor.

[0011] The heart rate module further includes a heart rate signal processing chip, the input of which is electrically connected to the first signal amplifier.

[0012] The data acquisition module includes a housing, a rechargeable lithium battery, and a charging management chip. The housing is fixedly connected to the elastic bodysuit and located below the gyroscope. The rechargeable lithium battery is fixedly connected to the housing and located inside the housing. The charging management chip is electrically connected to the rechargeable lithium battery and located outside the rechargeable lithium battery.

[0013] The data acquisition module further includes a second signal amplifier and an analog data converter. The second signal amplifier is fixedly connected to the elastic bodysuit and located between the bending sensor and the flexible pressure sensor. The analog data converter is electrically connected to the second signal amplifier and located above the charging management chip.

[0014] The data acquisition module further includes a multi-channel data fusion chip. The first channel of the multi-channel data fusion chip is electrically connected to the analog data converter, the second channel of the multi-channel data fusion chip is electrically connected to the heart rate signal processing chip, and the output terminal of the multi-channel data fusion chip is electrically connected to the microprocessor.

[0015] The wireless communication module includes a Bluetooth module and a Wi-Fi module. The Bluetooth module is electrically connected to the microprocessor and is located outside the microprocessor. The Wi-Fi module is electrically connected to the microprocessor and is located outside the Bluetooth module.

[0016] This invention discloses a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis. In practical use, the wearable part is fitted snugly against the human body. The flexible sensor array collects real-time data on the spine's bending angle, pressure distribution, acceleration, and rotational angular velocity. The heart rate module detects changes in the absorption of light by hemoglobin in the blood, thereby monitoring the heart rate in real time. The data acquisition module synchronously collects and integrates the data from the flexible sensor array and the heart rate module. The integrated data is then transmitted to a microprocessor for processing. After processing, the microprocessor packages the data and transmits it to a cloud server via a wireless communication module. The cloud server uses AI algorithms to correlate and analyze spinal posture data with heart rate data. For example, it can determine whether the heart rate changes abnormally when the spine is in an abnormal posture, thereby more accurately assessing the impact of scoliosis on overall health. The terminal application is used on mobile phones or tablets, allowing users to view their spinal posture data, health reports, and warning information in real time. At the same time, the terminal application can receive personalized rehabilitation suggestions and training plans pushed by the cloud server. This can solve the problems of insufficient sensor accuracy, limited data processing capabilities, and insufficient integration with AI technology in the monitoring of scoliosis. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis according to the present invention.

[0019] Figure 2 This is a rear view of a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis according to the present invention.

[0020] Figure 3 This is the invention Figure 2 Enlarged view of the local structure at point A.

[0021] Figure 4 This is a perspective view of a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis according to the present invention.

[0022] Figure 5 This is a schematic diagram of the monitoring unit of the present invention.

[0023] Figure 6 This is the main view of the data acquisition module of the present invention.

[0024] 1-Wearable unit, 101-Elastic bodysuit, 102-Open zipper, 103-Anti-slip layer, 2-Monitoring unit, 200-Flexible sensor array, 201-Bending sensor, 202-Flexible pressure sensor, 203-Triaxial accelerometer, 204-Gyroscope, 210-Heart rate module, 211-Heart rate sensor, 212-First signal amplifier, 213-Heart rate signal processing chip, 220-Microprocessor, 230-Wireless communication module, 231-Bluetooth module, 232-Wi-Fi module, 240-Data acquisition module, 241-Outer shell, 242-Rechargeable lithium battery, 243-Charging management chip, 244-Second signal amplifier, 245-Analog data converter, 246-Multi-channel data fusion chip. Detailed Implementation

[0025] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0026] Please see Figures 1 to 6 ,in Figure 1 This is a schematic diagram of the structure of a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis according to the present invention. Figure 2 This is a rear view of a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis according to the present invention. Figure 3 This is the invention Figure 2 Enlarged view of the local structure at point A. Figure 4 This is a perspective view of a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis according to the present invention. Figure 5 This is a schematic diagram of the monitoring unit of the present invention. Figure 6 This is the main view of the data acquisition module of the present invention.

[0027] This invention provides a wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis, comprising a wearable part 1, a monitoring part 2, and an AI data processing part. The wearable part 1 includes an elastic compression garment 101 and an open zipper 102. The monitoring part 2 includes a flexible sensor array 200, a heart rate module 210, a data acquisition module 240, a microprocessor 220, and a wireless communication module 230. The flexible sensor array 200 includes a bending sensor 201, a flexible pressure sensor 202, a three-axis accelerometer 203, and a gyroscope 204. The heart rate module 210 includes a heart rate sensor 211, a first signal amplifier 212, and... The heart rate signal processing chip 213, the data acquisition module 240 including the housing 241, the rechargeable lithium battery 242, the charging management chip 243, the second signal amplifier 244, the analog data converter 245 and the multi-channel data fusion chip 246, the wireless communication module 230 including the Bluetooth module 231 and the Wi-Fi module 232, and the AI ​​data processing unit including the cloud server and the terminal application, can effectively solve the problems of insufficient sensor accuracy, limited data processing capabilities and insufficient integration with AI technology in wearable devices for monitoring scoliosis, which cannot meet the high requirements of clinical and daily monitoring.

[0028] In this specific embodiment, the monitoring unit 2 includes a flexible sensor array 200, a heart rate module 210, a data acquisition module 240, a microprocessor 220, and a wireless communication module 230. The flexible sensor array 200 is fixedly connected to the wearable unit 1 and located on the outside of the wearable unit 1. The heart rate module 210 is fixedly connected to the wearable unit 1 and located on the side of the wearable unit 1 away from the flexible sensor array 200. The data acquisition module 240 is electrically connected to the flexible sensor array 200 and located below the flexible sensor array 200. The microprocessor 220 is electrically connected to the data acquisition module 240 and located inside the data acquisition module 240. The wireless communication module 230... The communication module 230 is electrically connected to the microprocessor 220 and is located outside the microprocessor 220; the AI ​​data processing unit includes a cloud server and a terminal application. The cloud server communicates with the wireless communication module 230, and the terminal application communicates with the cloud server. The wearable device 1 is worn close to the human body. The flexible sensor array 200 collects data on the bending angle, pressure distribution, acceleration, and rotational angular velocity of the spine in real time. The heart rate module 210 detects changes in the absorption of light by hemoglobin in the blood, thereby monitoring the heart rate in real time. The data acquisition module 240 synchronously collects and integrates the data collected by the flexible sensor array 200 and the heart rate module 210. The data is transmitted to the microprocessor 220 for processing. After processing, the microprocessor 220 packages the data and transmits it to the cloud server via the wireless communication module 230. The cloud server uses a deep learning algorithm based on Long Short-Term Memory (LSTM) networks to analyze spinal posture and heart rate data. LSTM networks can effectively process time-series data and capture the changing characteristics of spinal posture and heart rate data over time. During training, a large amount of spinal posture and heart rate data from scoliosis patients and healthy individuals are used as training samples to train the LSTM memory network. The spinal posture data and heart rate data are correlated and analyzed to enable the network to learn normal spinal posture and postures of different degrees of scoliosis. The trained LSTM memory network model accurately identifies the user's spinal posture, determines the presence, degree, and trend of scoliosis, and combines principal component analysis (PC) technology to reduce the dimensionality of the data while maintaining its main features, thereby improving data processing efficiency and model running speed. The cloud server uses AI algorithms to correlate spinal posture data with heart rate data. Normal samples are daily activity data from healthy individuals without scoliosis and with a Cobb angle <5°, labeled as "normal" for their "posture-heart rate" correlation. At rest, the spine is upright, and the heart rate is 60-80 beats / minute; during exercise, the spine naturally curves, and the heart rate increases linearly with activity intensity.Abnormal samples: Monitoring data from scoliosis patients with a Cobb angle ≥10° are labeled as "abnormal" based on their "posture-heart rate" correlation, further subdivided into: Type 1: Significantly increased heart rate with increasing scoliosis angle, e.g., when the scoliosis angle is >15°, the heart rate increases by >10 beats / minute from baseline; Type 2: Persistent abnormal spinal posture, e.g., tilt >20° for >5 minutes, with decreased heart rate variability (HRV <20ms); Type 3: Irregular fluctuations in heart rate during spinal torsion, e.g., RR interval standard deviation >50ms. This allows for more accurate assessment of the impact of scoliosis on overall health by determining whether heart rate changes abnormally when the spine is in a certain abnormal posture. The terminal application is for mobile phones or tablets, enabling users to view their spinal posture data, health reports, and warning information in real time. Simultaneously, the terminal application can receive personalized rehabilitation suggestions and training plans pushed by the cloud server, thus addressing the issues of insufficient sensor accuracy, limited data processing capabilities, and insufficient integration with AI technology in scoliosis monitoring.

[0029] The wearable part 1 includes an elastic bodysuit 101 and an open zipper 102. The flexible sensor array 200 is provided at the rear end of the elastic bodysuit 101, and an anti-slip layer 103 is provided on the inner side of the elastic bodysuit 101. The open zipper 102 is fixedly connected to the elastic bodysuit 101 and is located at the front end of the elastic bodysuit 101. The elastic bodysuit 101 is worn on the user's body. The anti-slip layer 103 ensures the stable wearing of the elastic bodysuit 101, and the open zipper 102 facilitates convenient wearing and removal by the user.

[0030] Secondly, the flexible sensor array 200 includes a bending sensor 201 and a flexible pressure sensor 202. The bending sensor 201 is fixedly connected to the rear end of the elastic compression garment 101 and is located on the outside of the elastic compression garment 101. The flexible pressure sensor 202 is fixedly connected to the elastic compression garment 101 and is located at the position where the elastic compression garment 101 fits against the human body. The bending sensor 201 is a flexible bending deformation sensor. Multiple pairs of the bending sensors 201 are symmetrically arranged on the back of the elastic compression garment 101, which can accurately capture changes in the bending angle of the spine, identify left and right bending movements of the spine, and measure the degree of curvature of the spine in the coronal and sagittal planes, providing key data for judging scoliosis. The flexible pressure sensor 202 is uniformly and symmetrically arranged in the inner lining of the elastic compression garment 101 and is in contact with the patient. It can monitor the pressure distribution of the skin around the spine in real time, reflect the pressure difference on the spine through pressure changes, and help judge the uneven force state when scoliosis occurs.

[0031] Meanwhile, the flexible sensor array 200 also includes a triaxial accelerometer 203 and a gyroscope 204. The triaxial accelerometer 203 is fixedly connected to the bending sensor 201 and located outside the bending sensor 201. The gyroscope 204 is fixedly connected to the elastic bodysuit 101 and located outside the triaxial accelerometer 203. Each bending sensor 201 has a triaxial accelerometer 203 located outside it. When the user is moving dynamically, the accelerometer 203 can stably measure the acceleration changes of the device in three-dimensional space and fuse the data with the bending sensor 201 to improve the accuracy of monitoring the spinal posture under dynamic conditions. There are three gyroscopes 204, which are respectively installed at the upper, middle and lower ends of the back of the elastic bodysuit 101. By measuring the Coriolis force, the gyroscopes can accurately sense the rotational angular velocity of the device and monitor the torsional angle of the spine to improve the accuracy of spinal rotation deformity judgment.

[0032] Additionally, the heart rate module 210 includes a heart rate sensor 211 and a first signal amplifier 212. The heart rate sensor 211 is fixedly connected to the elastic bodysuit 101 and located on the side of the elastic bodysuit 101 away from the flexible sensor array 200. The first signal amplifier 212 is electrically connected to the heart rate sensor 211 and located outside the heart rate sensor 211. The heart rate sensor 211 is photoelectric and is positioned at the front of the elastic bodysuit 101 near the heart. It emits light of a specific wavelength to detect changes in the absorption of light by hemoglobin in the blood, thereby monitoring the heart rate in real time. The first signal amplifier 212 amplifies the signal output by the heart rate sensor 211 to ensure the accuracy of the data.

[0033] Furthermore, the heart rate module 210 also includes a heart rate signal processing chip 213. The input terminal of the heart rate signal processing chip 213 is electrically connected to the first signal amplifier 212. The heart rate signal processing chip 213 processes the amplified heart rate data to remove the influence of factors such as motion interference and ambient light interference on the heart rate signal, and extracts a clear and stable heart rate waveform and heart rate data.

[0034] Secondly, the data acquisition module 240 includes a housing 241, a rechargeable lithium battery 242, and a charging management chip 243. The housing 241 is fixedly connected to the elastic bodysuit 101 and located below the gyroscope 204. The rechargeable lithium battery 242 is fixedly connected to the housing 241 and located inside the housing 241. The charging management chip 243 is electrically connected to the rechargeable lithium battery 242 and located outside the rechargeable lithium battery 242. The housing is fitted at the lower end of the back of the elastic bodysuit 101. The anti-slip layer 103 of the elastic bodysuit 101 effectively prevents the housing 241 from sliding downwards. The rechargeable lithium battery 242 provides a stable power supply for the entire device. The charging management chip 243 manages the rechargeable lithium battery 242, monitors the battery status of the rechargeable lithium battery 242, and prevents the rechargeable lithium battery 242 from overcharging, effectively maintaining the lifespan of the rechargeable lithium battery 242.

[0035] In addition, the data acquisition module 240 also includes a second signal amplifier 244 and an analog-to-digital converter 245. The second signal amplifier 244 is fixedly connected to the elastic bodysuit 101 and located between the bending sensor 201 and the flexible pressure sensor 202. The analog-to-digital converter 245 is electrically connected to the second signal amplifier 244 and located above the charging management chip 243. Multiple second signal amplifiers 244 are disposed between and electrically connected to the bending sensor 201 and the flexible pressure sensor 202. The second signal amplifier 244 amplifies the signals from the bending sensor 201 and the flexible pressure sensor 202 to improve signal quality and facilitate subsequent analog-to-digital conversion and data processing. The analog-to-digital converter 245 is disposed inside the housing 241 and is used to process the data transmitted by the bending sensor 201 and the flexible pressure sensor 202, converting the analog signals from the bending sensor 201 and the pressure sensor into digital signals to ensure the accuracy and stability of data acquisition.

[0036] Furthermore, the data acquisition module 240 also includes a multi-channel data fusion chip 246. The first channel of the multi-channel data fusion chip 246 is electrically connected to the analog data converter 245, the second channel of the multi-channel data fusion chip 246 is electrically connected to the heart rate signal processing chip 213, and the output terminal of the multi-channel data fusion chip 246 is electrically connected to the microprocessor 220. The multi-channel data fusion chip 246 fuses heart rate data with spinal posture-related data such as bending angle, pressure distribution, acceleration, and rotational angular velocity to achieve synchronous acquisition and integration of multi-channel data, ensuring the consistency of different types of data in time, reducing the processing burden of the microprocessor 220, providing data in a unified format for subsequent comprehensive analysis of AI algorithms, and outputting the acquired data to the microprocessor 220.

[0037] Meanwhile, the wireless communication module 230 includes a Bluetooth module 231 and a Wi-Fi module 232. The Bluetooth module 231 is electrically connected to the microprocessor 220 and is located outside the microprocessor 220. The Wi-Fi module 232 is electrically connected to the microprocessor 220 and is located outside the Bluetooth module 231. The microprocessor 220 transmits the integrated data to the cloud server through the Bluetooth module 231 or the Wi-Fi module 232 for analysis, and then transmits it to the user's terminal application.

[0038] This embodiment of a wearable smart sensor and AI-linked real-time scoliosis posture monitoring device, through the configuration of the wearable unit 1, the monitoring unit 2, and the AI ​​data processing unit, involves the following components: an elastic compression garment 101 is worn by the user; an anti-slip layer 103 ensures stable wear of the elastic compression garment 101; an open zipper 102 facilitates easy donning and doffing; a rechargeable lithium battery 242 provides stable power to the device; a bending sensor 201 captures changes in the bending angle of the user's spine; and a triaxial accelerometer 203 measures the changes in acceleration in three-dimensional space during the user's dynamic activities, fusing the data with that from the bending sensor 201. To improve the accuracy of spinal posture monitoring under dynamic conditions, the flexible pressure sensor 202 monitors the pressure distribution of the skin around the spine, assisting in determining the stress state during scoliosis. The gyroscope 204 accurately senses the rotational angular velocity of the device to monitor the torsional angle of the spine. The second signal amplifier 244 amplifies the signals from the bending sensor 201 and the flexible pressure sensor 202. The processed signals are then transmitted to the analog-to-digital converter 245. The analog-to-digital converter 245 converts the analog signals from the bending sensor 201 and the pressure sensor into digital signals and transmits them to the first channel of the multi-channel data fusion chip 246. The heart rate sensor 211 monitors the user's heart rate. The first signal amplifier 212 amplifies the signal output by the heart rate sensor 211. The heart rate signal processing chip 213 preprocesses the heart rate data, extracting a clear and stable heart rate waveform and transmitting the heart rate data to the second channel of the multi-channel data fusion chip 246. The multi-channel data fusion chip 246 fuses the data and transmits the integrated data to the microprocessor 220. The microprocessor 220 processes the data, packages it, and transmits it to the cloud server via the Bluetooth module 231 or the Wi-Fi module 232. The cloud server then uses an LSTM memory network model... This system performs correlation analysis on posture data and heart rate data to determine whether there are corresponding abnormal changes in heart rate when the spine is in an abnormal posture. This allows for a more accurate assessment of the impact of scoliosis on overall health. The analyzed data is then fed back to the user's terminal application. Based on the analysis results, the terminal application displays the user's spinal health status in real time. When abnormal spinal posture is detected, an alert is issued to remind the user to adjust their posture. This enables real-time and accurate monitoring of spinal data. Furthermore, based on AI algorithms, the system performs deep correlation analysis on the user's spinal posture data and heart rate data, providing comprehensive and accurate data support for scoliosis diagnosis and effectively improving the accuracy of spinal monitoring.

[0039] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis, comprising a wearable part, a monitoring part, and an AI data processing part, wherein the monitoring part is disposed on the outside of the wearable part, and the data of the monitoring part is transmitted to the AI ​​data processing part, characterized in that, The monitoring unit includes a flexible sensor array, a heart rate module, a data acquisition module, a microprocessor, and a wireless communication module. The flexible sensor array is fixedly connected to the wearable part and located on the outside of the wearable part. The heart rate module is fixedly connected to the wearable part and located on the side of the wearable part away from the flexible sensor array. The data acquisition module is electrically connected to the flexible sensor array and located below the flexible sensor array. The microprocessor is electrically connected to the data acquisition module and located inside the data acquisition module. The wireless communication module is electrically connected to the microprocessor and located on the outside of the microprocessor. The AI ​​data processing unit includes a cloud server and a terminal application. The cloud server communicates with the wireless communication module, and the terminal application communicates with the cloud server.

2. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 1, characterized in that, The wearable part includes an elastic bodysuit and an open zipper. The flexible sensor array is provided at the rear end of the elastic bodysuit, and an anti-slip layer is provided on the inner side of the elastic bodysuit. The open zipper is fixedly connected to the elastic bodysuit and is located at the front end of the elastic bodysuit.

3. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 2, characterized in that, The flexible sensor array includes a bending sensor and a flexible pressure sensor. The bending sensor is fixedly connected to the rear end of the elastic bodysuit and is located on the outside of the elastic bodysuit. The flexible pressure sensor is fixedly connected to the elastic bodysuit and is located at the position where the elastic bodysuit fits the human body.

4. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 3, characterized in that, The flexible sensor array also includes a triaxial accelerometer and a gyroscope. The triaxial accelerometer is fixedly connected to the bending sensor and located outside the bending sensor. The gyroscope is fixedly connected to the elastic bodysuit and located outside the triaxial accelerometer.

5. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 4, characterized in that, The heart rate module includes a heart rate sensor and a first signal amplifier. The heart rate sensor is fixedly connected to the elastic bodysuit and is located on the side of the elastic bodysuit away from the flexible sensor array. The first signal amplifier is electrically connected to the heart rate sensor and is located outside the heart rate sensor.

6. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 5, characterized in that, The heart rate module also includes a heart rate signal processing chip, the input of which is electrically connected to the first signal amplifier.

7. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 6, characterized in that, The data acquisition module includes a housing, a rechargeable lithium battery, and a charging management chip. The housing is fixedly connected to the elastic bodysuit and located below the gyroscope. The rechargeable lithium battery is fixedly connected to the housing and located inside the housing. The charging management chip is electrically connected to the rechargeable lithium battery and located outside the rechargeable lithium battery.

8. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 7, characterized in that, The data acquisition module further includes a second signal amplifier and an analog data converter. The second signal amplifier is fixedly connected to the elastic bodysuit and located between the bending sensor and the flexible pressure sensor. The analog data converter is electrically connected to the second signal amplifier and located above the charging management chip.

9. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 8, characterized in that, The data acquisition module further includes a multi-channel data fusion chip. The first channel of the multi-channel data fusion chip is electrically connected to the analog data converter, the second channel of the multi-channel data fusion chip is electrically connected to the heart rate signal processing chip, and the output terminal of the multi-channel data fusion chip is electrically connected to the microprocessor.

10. The wearable smart sensor and AI-linked real-time posture monitoring device for scoliosis as described in claim 9, characterized in that, The wireless communication module includes a Bluetooth module and a Wi-Fi module. The Bluetooth module is electrically connected to the microprocessor and is located outside the microprocessor. The Wi-Fi module is electrically connected to the microprocessor and is located outside the Bluetooth module.

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