A multi-signal synchronous acquisition system and method for posture control capability detection

Abstract

The present application relates to the technical field of posture control detection, and particularly relates to a multi-signal synchronous acquisition system and a signal acquisition method for posture control ability detection. The present application constructs a wireless synchronous acquisition system integrating central brain function signals, peripheral electromyography signals and human body pressure center signals, can synchronously acquire full-dimension biological signals of brain activation, muscle effort and gravity center shift in the process of posture control, can greatly improve the comprehensiveness and systematicness of posture control ability detection; adopts a full-wireless signal transmission architecture, can not only guarantee multi-signal synchronous acquisition accuracy, but also can guarantee that the subjects can flexibly complete various static and dynamic posture control tasks, effectively avoids the interference of equipment hardware on detection actions; and through multi-signal acquisition and processing algorithms, can realize automatic noise reduction, time sequence matching and coupling quantitative processing of multi-source signals, greatly improves data processing efficiency, has strong practical application value and popularization prospect.

Description

Technical Field

[0001] This invention relates to the field of posture control detection technology, and specifically to a multi-signal synchronous acquisition system and signal acquisition method for posture control capability detection. Background Technology

[0002] Postural control refers to a series of neuromuscular control activities undertaken to achieve body center of gravity stability and limb spatial positioning. Among these, anticipatory postural adjustment (APAs), compensatory postural adjustment (CPAs), and voluntary postural adjustment, all dominated by the central nervous system, are the three basic physiological mechanisms of postural control. In the central nervous system, postural control involves the coordination and communication of multiple brain regions, including the sensory cortex, motor cortex, premotor cortex, prefrontal cortex, cerebellum, and basal ganglia. Peripherally, postural control involves muscle activation and alterations in body dynamics.

[0003] Currently, commonly used methods for evaluating neuromuscular motor control include functional near-infrared spectroscopy (fNIRS), surface electromyography (sEMG), and center of pressure (COP) detection.

[0004] However, existing posture control detection methods have the following drawbacks: First, traditional posture control detection methods cannot simultaneously acquire posture control-related biosignals from both the central nervous system and peripheral skeletal muscles. Although near-infrared functional testing has been used to detect brain function signals related to posture control in the central nervous system, it cannot detect peripheral electromyography (EMG) signals or changes in the body's center of pressure. Furthermore, existing surface EMG and pressure plateau detection methods are limited to detecting posture control-related surface EMG and center of pressure signals, and cannot detect central nervous system brain function. Second, traditional posture control detection methods often use wired connections, resulting in cumbersome cable layouts that are prone to tangling and limiting movement. This hinders the ability to detect subjects' flexible posture control tasks, easily interferes with their limb movements, and leads to distorted data. Third, for posture control detection, there is currently a lack of synchronous wireless acquisition devices suitable for both central and peripheral biosignals under dynamic and static tasks. Various signal acquisition devices operate independently, timestamps cannot be unified, signal timing is misaligned, and synchronization is poor. This makes it impossible to accurately correspond to the coupling relationship between brain activation, muscle exertion, and center of gravity changes during posture adjustment, resulting in insufficient detection accuracy and data integrity. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a multi-signal synchronous acquisition system and method for posture control ability detection, which can realize the wireless synchronous acquisition of central brain function signals, peripheral electromyographic signals and human pressure center signals, and can be adapted to various dynamic and static posture control tasks, improving detection flexibility, data synchronization accuracy and detection accuracy.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: I. A multi-signal synchronous acquisition system for posture control capability detection This invention provides a multi-signal synchronous acquisition system for detecting posture control ability, mainly comprising: a pressure platform detection device 1, a surface electromyography detection device 2, a near-infrared brain signal detection device 3, a multi-signal synchronization box 4, and a host computer 5; The pressure platform detection device 1 is placed on the ground under the subject's feet to collect the changes in the center of pressure signal of the subject's body in real time; the surface electromyography detection device 2 is installed on a preset muscle group on the subject's leg and is in close contact with the skin of the subject's leg to collect the changes in the electromyography signal on the surface of the subject's leg muscle group in real time; the measuring end of the near-infrared brain signal detection device 3 is worn on the subject's head to collect the changes in the subject's brain function signal in real time. The multi-signal synchronization box 4 is wirelessly connected to the pressure platform detection device 1, the surface electromyography detection device 2, and the near-infrared brain signal detection device 3 via wireless synchronization interface 1 41, wireless synchronization interface 2 42, and wireless synchronization interface 3 43, respectively, and the multi-signal synchronization box 4 is electrically connected to the host computer 5 via a communication cable.

[0007] Preferably, the multi-signal synchronization box 4 is provided with a clock synchronization module, a signal triggering module and a data buffer module; The clock synchronization module is used to unify the acquisition clocks of the pressure platform detection device 1, surface electromyography detection device 2, and near-infrared brain signal detection device 3. The signal triggering module is used to synchronously send acquisition triggering commands to the three sets of detection devices. The data caching module is used to cache multi-source acquisition signals in real time.

[0008] Preferably, the pressure platform detection device 1 integrates a capacitive pressure sensor array to acquire pressure data under various static and dynamic posture tasks.

[0009] Preferably, the surface electromyography (EMG) detection device 2 adopts a flexible electrode sheet structure, which fits in contact with the curved surface of the subject's leg, and is used to collect the activation sequence and force intensity signals of the subject's leg muscle groups during posture adjustment.

[0010] Preferably, the detection probe of the near-infrared brain signal detection device 3 is arranged corresponding to the motor cortex, sensory cortex and cerebellar motor association brain regions, and is used to capture brain region activation state data of the central nervous system during posture adjustment.

[0011] II. A Multi-Signal Synchronous Acquisition Method for Posture Control Ability Detection Based on the same inventive concept, this invention also provides a multi-signal synchronous acquisition method for posture control capability detection, employing the multi-signal synchronous acquisition system described above, and including the following steps: S1, Device Wearing and Deployment: Place the pressure platform detection device horizontally on a flat surface, and have the subject stand barefoot in the center of the pressure platform; clean the skin of the subject's leg detection area, removing oil and keratin, and attach and fix the electrode pads of the surface electromyography detection device to the preset muscle groups on the subject's legs; wear the near-infrared brain signal detection device on the subject's head, adjust and fix the probe position to ensure that the probe corresponds to the preset brain region; S2, System clock synchronization configuration: Start the clock synchronization module of the multi-signal synchronization box, establish wireless communication links between the multi-signal synchronization box and the three sets of acquisition devices, and unify the acquisition clocks of the pressure platform detection device, surface electromyography detection device, and near-infrared brain signal detection device through the device clock synchronization calibration algorithm to complete the timing alignment of multiple devices; S3, Task mode selection and command issuance: The host computer selects a static posture detection task or a dynamic posture detection task according to the different posture control capability detection requirements; and transmits the task command and synchronous acquisition trigger signal to the multi-signal synchronization box; S4, Multi-signal synchronous acquisition: After the subject confirms that he is ready, the host computer outputs a task prompt to guide the subject to perform the corresponding posture control action; at the same time, the multi-signal synchronization box synchronously triggers the three sets of detection devices to start real-time signal acquisition. S5, Signal Preprocessing and Synchronization Coupling: The multi-signal synchronization box performs adaptive noise reduction filtering on the three types of raw signals in the buffer, and performs time-series matching and integration of the three types of signals based on a unified timestamp and a multi-signal synchronization coupling quantization algorithm to generate a multi-dimensional attitude control signal dataset with a synchronization time axis, and uploads the integrated complete data to the host computer. S6, Data Storage and Repeat Detection: The host computer receives and stores multi-signal datasets and synchronously displays signal change curves; after a single task is completed, the data is automatically saved and the acquisition process for different posture tasks is repeated.

[0012] Preferably, in step S2, the device clock synchronization calibration algorithm has the following formula: T s =T0+△T1+△T2+△T3 In the formula, T sTo standardize the acquisition clock, T0 is the reference clock of the multi-signal synchronization box, △T1 is the clock deviation correction value of the pressure platform equipment, △T2 is the clock deviation correction value of the surface electromyography equipment, and △T3 is the clock deviation correction value of the near-infrared brain signal equipment. By using this formula, the clocks of all equipment are unified to the reference clock, ensuring the accuracy of synchronous acquisition.

[0013] Preferably, in step S4, the pressure platform detection device continuously collects the COP signal of the human body's center of gravity coordinates and pressure distribution changes, the surface electromyography detection device collects the sEMG voltage signal of muscle group activation, and the near-infrared brain signal detection device collects the fNIRS signal of changes in brain region blood oxygen concentration. The three sets of signals are wirelessly transmitted in real time to the multi-signal synchronization box buffer.

[0014] Preferably, in step S5, the adaptive noise reduction filtering preprocessing is performed using the following formula: X out (t)=α×X in (t)+(1-α)×X out (t-1) In the formula, X out (t) represents the denoised signal value at time t, X in (t) represents the original acquired signal value at time t, X out (t-1) represents the noise-reduced signal value at time t-1, and α is the filtering coefficient.

[0015] Preferably, in step S5, the multi-signal synchronization coupling quantization algorithm, The formula is as follows: S(t)=w1×C(t)+w2×M(t)+w3×B(t) In the formula, S(t) is the quantized value of the posture control integrated signal at time t, C(t) is the normalized value of the COP pressure center signal at time t, M(t) is the normalized value of the sEMG electromyography signal at time t, B(t) is the normalized value of the fNIRS cerebral oxygenation signal at time t, and w1, w2, and w3 are the corresponding signal weight coefficients, and w1 + w2 + w3 = 1.

[0016] Compared with the prior art, the present invention has the following main advantages: 1. This invention constructs a wireless synchronous acquisition system that integrates central brain function signals, peripheral electromyographic signals, and human pressure center signals. It can simultaneously acquire full-dimensional biological signals of brain region activation, muscle exertion, and center of gravity shift during posture control, fully covering the entire process of central regulation and peripheral execution of posture adjustment. It solves the industry pain point that existing technologies cannot detect central and peripheral physiological signals in a coordinated manner, and can significantly improve the comprehensiveness and systematicness of posture control ability detection.

[0017] 2. This invention adopts a fully wireless signal transmission architecture, which can not only ensure the accuracy of multi-signal synchronous acquisition, but also ensure that the subject can flexibly complete various static and dynamic posture control tasks, effectively avoid the interference of device hardware on the detection action, significantly improve the authenticity and accuracy of the detection data, and adapt to the posture detection needs of different scenarios and different action modes.

[0018] 3. This invention, through multi-signal acquisition and processing algorithms, can achieve automatic noise reduction, timing matching, and coupled quantization processing of multi-source signals, greatly improving data processing efficiency. It can provide accurate, comprehensive, and timing-consistent raw data support for posture control capability detection, and has strong practical application value and promotion prospects. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the multi-signal synchronous acquisition system for posture control capability detection in an embodiment of the present invention; Figure 2 This is a physical diagram of the multi-signal synchronous acquisition system in an embodiment of the present invention; Figure 3 This is an overall flowchart of the multi-signal synchronous acquisition method in an embodiment of the present invention.

[0020] In the diagram: 1-Pressure platform detection device, 2-Surface electromyography detection device, 3-Near-infrared brain signal detection device, 4-Multi-signal synchronization box, 41-Wireless synchronization interface one, 42-Wireless synchronization interface two, 43-Wireless synchronization interface three, 5-Host computer. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0023] In this invention, unless otherwise expressly specified and limited, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.

[0024] Example 1: This example provides a multi-signal synchronous acquisition system for posture control capability detection, such as... Figures 1-2 As shown, it includes: a pressure platform detection device 1, a surface electromyography detection device 2, a near-infrared brain signal detection device 3, a multi-signal synchronization box 4, and a host computer 5; The pressure platform detection device 1 is placed on the ground under the subject's feet to collect the changes in the center of pressure signal of the subject's body in real time; the surface electromyography detection device 2 is installed on a preset muscle group on the subject's leg and is in close contact with the skin of the subject's leg to collect the changes in the electromyography signal on the surface of the subject's leg muscle group in real time; the measuring end of the near-infrared brain signal detection device 3 is worn on the subject's head to collect the changes in the subject's brain function signal in real time. The multi-signal synchronization box 4 is wirelessly connected to the pressure platform detection device 1, the surface electromyography detection device 2, and the near-infrared brain signal detection device 3 via wireless synchronization interface 1 41, wireless synchronization interface 2 42, and wireless synchronization interface 3 43, respectively, and the multi-signal synchronization box 4 is electrically connected to the host computer 5 via a communication cable.

[0025] In this embodiment, the multi-signal synchronization box 4 is equipped with a clock synchronization module, a signal triggering module and a data buffer module; The clock synchronization module is used to unify the acquisition clocks of the pressure platform detection device 1, surface electromyography detection device 2, and near-infrared brain signal detection device 3. The signal triggering module is used to synchronously send acquisition triggering commands to the three sets of detection devices. The data caching module is used to cache multi-source acquisition signals in real time.

[0026] In this embodiment, the pressure platform detection device 1 integrates a capacitive pressure sensor array to collect pressure data under various static and dynamic posture tasks.

[0027] In this embodiment, the surface electromyography detection device 2 adopts a flexible electrode sheet structure, which fits in contact with the curved surface of the subject's leg, and is used to collect the activation sequence and force intensity signals of the subject's leg muscle groups during posture adjustment.

[0028] In this embodiment, the detection probe of the near-infrared brain signal detection device 3 is arranged corresponding to the motor cortex, sensory cortex and cerebellum motor association brain regions, and is used to capture brain region activation state data of the central nervous system during posture adjustment.

[0029] Example 2: This example provides a multi-signal synchronous acquisition system for posture control ability detection. The pressure platform detection device is placed on the ground under the subject's feet to collect the changes in the center of pressure signal of the subject's body in real time. It can be adapted to the pressure data acquisition of static and dynamic posture tasks such as standing, stepping, and center of gravity shift. The surface electromyography (EMG) detection device is attached to the key joint muscles of the subject's legs and is closely attached to the skin of the subject's legs. It is used to collect the changes in the surface EMG signals of the subject's leg joint muscles in real time and capture the muscle activation sequence and force intensity data during posture adjustment. The measuring end of the near-infrared brain signal detection device is worn on the subject's head, and detection probes are arranged corresponding to the brain regions related to brain movement and perception. It is used to collect the changes in the subject's brain function blood oxygen signal in real time and reflect the activation state of the brain regions for posture regulation of the central nervous system. The multi-signal synchronization box is equipped with three independent wireless synchronization interfaces. Wireless synchronization interface one, wireless synchronization interface two, and wireless synchronization interface three are respectively used to wirelessly connect to the pressure platform detection device, surface electromyography detection device, and near-infrared brain signal detection device. This wireless transmission method avoids the limitations of wired cables. Furthermore, the multi-signal synchronization box is electrically connected to the host computer via a high-speed communication cable to achieve command transmission and data aggregation and uploading. The multi-signal synchronization box integrates a clock synchronization module, a signal triggering module, and a data buffering module to unify the acquisition clocks of the three types of acquisition devices, synchronously issue acquisition triggering commands, and buffer multiple sets of real-time acquired signal data.

[0030] The system's operation involves the following steps: Subjects receive posture prompts from a host computer, such as static standing, dynamic center of gravity adjustment, and limb fine-tuning, and then perform corresponding posture actions. Simultaneously, the host computer sends acquisition commands to a multi-signal synchronization box. The multi-signal synchronization box, through a unified clock, synchronously triggers the pressure platform detection device, surface electromyography detection device, and near-infrared brain signal detection device to simultaneously start signal acquisition. This enables the synchronous acquisition of posture-related central brain function signals, peripheral electromyography, and pressure biological signals under various dynamic and static tasks. Furthermore, all three signals are transmitted wirelessly, allowing for flexible equipment deployment without cable interference. This makes it more flexible for subjects to perform posture tasks and effectively ensures the authenticity of the test data.

[0031] Example 3, based on the same inventive concept, also provides a multi-signal synchronous acquisition method for posture control capability detection, employing the multi-signal synchronous acquisition system described above, such as... Figure 3 As shown, it includes the following steps: Step S1, Equipment Wearing and Deployment: Perform power-on self-tests and parameter calibrations on the pressure platform detection device, surface electromyography (SEMG) detection device, and near-infrared brain signal detection device in advance, and clear the initial data of the devices; place the pressure platform detection device horizontally on a flat ground, and have the subject stand barefoot in the center area of ​​the pressure platform; clean the skin of the subject's leg detection area, removing oil and keratin, and attach and fix the electrode pads of the SEMG detection device to the key posture control muscle groups such as the quadriceps femoris and gastrocnemius muscles; wear the near-infrared brain signal detection device on the subject's head, adjust and fix the probe position to ensure that the probe corresponds to the preset brain region without loosening or displacement.

[0032] Step S2, System clock synchronization configuration: Start the clock synchronization module of the multi-signal synchronization box, establish wireless communication links between the multi-signal synchronization box and the three sets of acquisition devices, and unify the acquisition clocks of the pressure platform detection device, surface electromyography detection device, and near-infrared brain signal detection device through the device clock synchronization calibration algorithm, calibrate the clock error of each device to within 1ms, complete the timing alignment of multiple devices, and eliminate the time deviation of independent device acquisition.

[0033] The device clock synchronization calibration algorithm is defined by the following formula: T s =T0+△T1+△T2+△T3 In the formula, T s To standardize the acquisition clock, T0 is the reference clock of the multi-signal synchronization box, △T1 is the clock deviation correction value of the pressure platform equipment, △T2 is the clock deviation correction value of the surface electromyography equipment, and △T3 is the clock deviation correction value of the near-infrared brain signal equipment. By using this formula, the clocks of all equipment are unified to the reference clock, ensuring the accuracy of synchronous acquisition.

[0034] Step S3, Task mode selection and instruction issuance: The operator selects a static posture detection task or a dynamic posture detection task according to the detection requirements through the host computer, and sets parameters such as acquisition duration, acquisition frequency, and signal threshold; the host computer transmits the task instruction and synchronous acquisition trigger signal to the multi-signal synchronization box, and waits for the subject's posture to be ready.

[0035] Step S4, Multi-signal Synchronous Acquisition: After the subject confirms preparation, the host computer outputs task prompts to guide the subject to perform the corresponding posture control actions; at the same time, the multi-signal synchronization box synchronously triggers three sets of detection devices to start real-time signal acquisition. The pressure platform detection device continuously collects the COP signal of the body's center of gravity coordinates and pressure distribution changes, the surface electromyography detection device collects the sEMG voltage signal of muscle group activation, and the near-infrared brain signal detection device collects the fNIRS signal of changes in brain region blood oxygen concentration. The three sets of signals are wirelessly transmitted to the multi-signal synchronization box buffer in real time.

[0036] Step S5, Signal Preprocessing and Synchronization Coupling: The multi-signal synchronization box performs adaptive noise reduction filtering on the three types of raw signals in the buffer to remove environmental interference noise and abnormal data; based on the unified timestamp and multi-signal synchronization coupling quantization algorithm, the three types of signals are time-series matched and integrated to generate a multi-dimensional attitude control signal dataset with a synchronization time axis, and the integrated complete data is uploaded to the host computer.

[0037] The adaptive noise reduction filtering preprocessing formula is as follows: X out (t)=α×X in (t)+(1-α)×X out (t-1) In the formula, X out (t) represents the denoised signal value at time t, X in (t) represents the original acquired signal value at time t, X out (t-1) is the signal value after noise reduction at time t-1, and α is the filtering coefficient, which takes a value of 0.85~0.95. It can be adaptively adjusted according to the dynamic and static task scenarios to effectively filter out high-frequency environmental noise.

[0038] The multi-signal synchronous coupling quantization algorithm is defined by the following formula: S(t)=w1×C(t)+w2×M(t)+w3×B(t) In the formula, S(t) is the quantized value of the posture control integrated signal at time t, C(t) is the normalized value of the COP pressure center signal at time t, M(t) is the normalized value of the sEMG electromyography signal at time t, B(t) is the normalized value of the fNIRS cerebral oxygenation signal at time t, and w1, w2, and w3 are the corresponding signal weight coefficients, and w1 + w2 + w3 = 1. They are adaptively allocated according to the dynamic and static detection scenarios to achieve coupled quantitative analysis of multi-dimensional signals.

[0039] Step S6, Data storage and repeated detection: The host computer receives and stores the multi-signal dataset and synchronously displays the signal change curve; after a single task is completed, the data is automatically saved and the acquisition process for different posture tasks is repeated to complete the batch detection.

[0040] Furthermore, all parts of this application that are not described in detail are the same as or implemented using existing technology.

[0041] In summary: 1. This invention constructs a wireless synchronous acquisition system that integrates central brain function signals, peripheral electromyographic signals, and human pressure center signals. It can simultaneously acquire full-dimensional biological signals of brain region activation, muscle exertion, and center of gravity shift during posture control, fully covering the entire process of central regulation and peripheral execution of posture adjustment. It solves the industry pain point that existing technologies cannot detect central and peripheral physiological signals in a coordinated manner, and can significantly improve the comprehensiveness and systematicness of posture control ability detection.

[0042] 2. This invention adopts a fully wireless signal transmission architecture, which can not only ensure the accuracy of multi-signal synchronous acquisition, but also ensure that the subject can flexibly complete various static and dynamic posture control tasks, effectively avoid the interference of device hardware on the detection action, significantly improve the authenticity and accuracy of the detection data, and adapt to the posture detection needs of different scenarios and different action modes.

[0043] 3. This invention, through multi-signal acquisition and processing algorithms, can achieve automatic noise reduction, timing matching, and coupled quantization processing of multi-source signals, greatly improving data processing efficiency. It can provide accurate, comprehensive, and timing-consistent raw data support for posture control capability detection, and has strong practical application value and promotion prospects.

[0044] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0046] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-signal synchronous acquisition system for posture control capability detection, characterized in that: It includes a pressure platform detection device (1), a surface electromyography detection device (2), a near-infrared brain signal detection device (3), a multi-signal synchronization box (4), and a host computer (5); The pressure platform detection device (1) is placed on the ground under the feet of the subject to collect the changes in the center of pressure signal of the subject in real time; the surface electromyography detection device (2) is installed on the preset muscle group of the subject's leg and is in close contact with the skin of the subject's leg to collect the changes in the electromyography signal on the surface of the subject's leg muscle group in real time; the measuring end of the near-infrared brain signal detection device (3) is worn on the head of the subject to collect the changes in the subject's brain function signal in real time. The multi-signal synchronization box (4) is wirelessly connected to the pressure platform detection device (1), surface electromyography detection device (2), and near-infrared brain signal detection device (3) via wireless synchronization interface one (41), wireless synchronization interface two (42), and wireless synchronization interface three (43), respectively. The multi-signal synchronization box (4) is also electrically connected to the host computer (5) via a communication cable.

2. The multi-signal synchronous acquisition system for posture control capability detection according to claim 1, characterized in that, The multi-signal synchronization box (4) is equipped with a clock synchronization module, a signal triggering module and a data buffer module; The clock synchronization module is used to unify the acquisition clocks of the pressure platform detection device (1), surface electromyography detection device (2), and near-infrared brain signal detection device (3). The signal triggering module is used to synchronously send acquisition triggering instructions to the three sets of detection devices. The data caching module is used to cache multi-source acquisition signals in real time.

3. The multi-signal synchronous acquisition system for posture control capability detection according to claim 1, characterized in that, The pressure platform detection device (1) integrates a capacitive pressure sensor array to collect pressure data under various static and dynamic posture tasks.

4. A multi-signal synchronous acquisition system for posture control capability detection according to claim 1, characterized in that, The surface electromyography (2) device adopts a flexible electrode sheet structure, which fits against the curved surface of the subject's leg and is used to collect the activation sequence and force intensity signals of the subject's leg muscles during posture adjustment.

5. A multi-signal synchronous acquisition system for posture control capability detection according to claim 1, characterized in that, The detection probe of the near-infrared brain signal detection device (3) is arranged in the motor cortex, sensory cortex and cerebellum motor association brain regions of the brain, and is used to capture brain region activation status data of the central nervous system during posture adjustment.

6. A method for simultaneous acquisition of multiple signals for posture control capability detection, employing the simultaneous acquisition system for multiple signals as described in any one of claims 1 to 5, characterized in that, Includes the following steps: S1, Device Wearing and Deployment: Place the pressure platform detection device horizontally on a flat surface, and have the subject stand barefoot in the center of the pressure platform; clean the skin of the subject's leg detection area, removing oil and keratin, and attach and fix the electrode pads of the surface electromyography detection device to the preset muscle groups on the subject's legs; wear the near-infrared brain signal detection device on the subject's head, adjust and fix the probe position to ensure that the probe corresponds to the preset brain region; S2, System clock synchronization configuration: Start the clock synchronization module of the multi-signal synchronization box, establish wireless communication links between the multi-signal synchronization box and the three sets of acquisition devices, and unify the acquisition clocks of the pressure platform detection device, surface electromyography detection device, and near-infrared brain signal detection device through the device clock synchronization calibration algorithm to complete the timing alignment of multiple devices; S3, Task mode selection and command issuance: The host computer selects a static posture detection task or a dynamic posture detection task according to the different posture control capability detection requirements; and transmits the task command and synchronous acquisition trigger signal to the multi-signal synchronization box; S4, Multi-signal synchronous acquisition: After the subject confirms that he is ready, the host computer outputs a task prompt to guide the subject to perform the corresponding posture control action; at the same time, the multi-signal synchronization box synchronously triggers the three sets of detection devices to start real-time signal acquisition. S5, Signal Preprocessing and Synchronization Coupling: The multi-signal synchronization box performs adaptive noise reduction filtering on the three types of raw signals in the buffer, and performs time-series matching and integration of the three types of signals based on a unified timestamp and a multi-signal synchronization coupling quantization algorithm to generate a multi-dimensional attitude control signal dataset with a synchronization time axis, and uploads the integrated complete data to the host computer. S6, Data Storage and Repeat Detection: The host computer receives and stores multi-signal datasets and synchronously displays signal change curves; after a single task is completed, the data is automatically saved and the acquisition process for different posture tasks is repeated.

7. A multi-signal synchronous acquisition method for posture control capability detection according to claim 6, characterized in that... In step S2, the device clock synchronization calibration algorithm is as follows: T s =T0+△T1+△T2+△T3 In the formula, T s To standardize the acquisition clock, T0 is the reference clock of the multi-signal synchronization box, △T1 is the clock deviation correction value of the pressure platform equipment, △T2 is the clock deviation correction value of the surface electromyography equipment, and △T3 is the clock deviation correction value of the near-infrared brain signal equipment. By using this formula, the clocks of all equipment are unified to the reference clock, ensuring the accuracy of synchronous acquisition.

8. A multi-signal synchronous acquisition method for posture control capability detection according to claim 6, characterized in that... In step S4, the pressure platform detection device continuously collects the COP signal of the human body's center of gravity coordinates and pressure distribution changes, the surface electromyography detection device collects the sEMG voltage signal of muscle group activation, and the near-infrared brain signal detection device collects the fNIRS signal of changes in brain region blood oxygen concentration. The three sets of signals are wirelessly transmitted in real time to the multi-signal synchronization box buffer.

9. A multi-signal synchronous acquisition method for posture control capability detection according to claim 8, characterized in that... In step S5, the adaptive noise reduction filtering preprocessing is performed using the following formula: X out (t)=α×X in (t)+(1-α)×X out (t-1) In the formula, X out (t) represents the denoised signal value at time t, X in (t) represents the original acquired signal value at time t, X out (t-1) represents the noise-reduced signal value at time t-1, and α is the filtering coefficient.

10. A multi-signal synchronous acquisition method for posture control capability detection according to claim 9, characterized in that... In step S5, the multi-signal synchronization coupling quantization algorithm, The formula is as follows: S(t)=w1×C(t)+w2×M(t)+w3×B(t) In the formula, S(t) is the quantized value of the posture control integrated signal at time t, C(t) is the normalized value of the COP pressure center signal at time t, M(t) is the normalized value of the sEMG electromyography signal at time t, B(t) is the normalized value of the fNIRS cerebral oxygenation signal at time t, and w1, w2, and w3 are the corresponding signal weight coefficients, and w1 + w2 + w3 = 1.

Images