Multi-sensory reweighted dynamic balance assessment method

Abstract

The invention discloses a multi-sensory reweighted dynamic balance assessment method. The method comprises the following steps: (1) data acquisition: acquiring plantar pressure center (COP) data of a subject under different sensory disturbance conditions; (2) posture stability evaluation: fitting a 95% confidence ellipse of COP data, and calculating a swing area A for evaluating the overall posture stability of the subject; (3) COP signal decomposition: preprocessing COP data, and decomposing the COP data into different frequency bands by adopting discrete wavelet transform; (4) multi-sense reweighted analysis: enabling the frequency bands to correspond to sense systems, calculating an energy ratio, and quantifying the contribution of each sense system to balance control; and (5) data integration and evaluation. According to the method, by combining a linear index (swing area) and a nonlinear index (energy distribution of discrete wavelet transform), the posture stability of a subject and the contribution of each sensory system to balance control are comprehensively and objectively evaluated under the condition of multi-sensory disturbance.

Description

Technical Field

[0001] The invention relates to the field of human balance function assessment and rehabilitation, and in particular to a multi-sensory weighted dynamic balance assessment method. Background Art

[0002] Balance control is a complex sensory-motor process that requires the nervous system to effectively integrate multiple sensory information, including visual, vestibular, and proprioceptive sensations, to maintain postural stability. However, with aging, the sensory integration ability and sensory reweighting mechanism of the elderly will gradually degenerate, leading to a decline in balance function and an increased risk of falls.

[0003] Existing balance assessment methods mainly use static or constant sensory perturbations, such as eyes open / closed, single-leg standing or tandem standing tasks. Although these methods can assess the balance ability of subjects under specific conditions, they cannot fully simulate the dynamically changing sensory environment in daily life, and it is difficult to quantify the specific contribution of each sensory system to balance control. In addition, traditional evaluation indicators are mostly linear indicators of a single scale, such as sway speed, sway amplitude, etc., which cannot fully reflect the complexity and multi-scale characteristics of balance control.

[0004] In recent years, the development of signal processing technology has provided new methods for the assessment of balance function. Discrete wavelet transform, as a nonlinear analysis method, has been introduced into the processing of COP signals. Through discrete wavelet transform, COP signals can be decomposed into different frequency bands, corresponding to the activity frequencies of different sensory systems, thereby quantifying the contribution of visual, vestibular and proprioceptive systems to balance control. However, current research focuses on the analysis under single sensory disturbance conditions, lacking in-depth research on the sensory reweighting mechanism under multi-sensory combined disturbance conditions.

[0005] Therefore, there is an urgent need for a method that can comprehensively evaluate the body's postural stability and sensory reweighting ability under dynamic, multisensory perturbation conditions by combining linear and nonlinear analysis methods. This is of great significance for accurately quantifying the contribution of each sensory system to balance control, deeply understanding the mechanism of balance function decline in the elderly, and formulating effective intervention measures. Summary of the invention

[0006] The purpose of the present invention is to solve the problems raised in the above background technology and to provide a multi-sensory weighted dynamic balance assessment method.

[0007] The technical solution of the present invention is: The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a multi-sensory weighted dynamic balance assessment method to solve the problem that the prior art cannot comprehensively evaluate the contribution of each sensory system to balance control, especially to evaluate the sensory weighting ability under dynamic, multi-sensory disturbance conditions.

[0008] To achieve the above-mentioned object of the invention, the present invention provides a multi-sensory weighted dynamic balance assessment method, which is characterized by comprising the following steps:

[0009] (1) Data collection: The center of plantar pressure (COP) data of the subjects were collected under different sensory perturbation conditions, including single perturbations of vision, vestibule and proprioception, and combined perturbations of multiple senses;

[0010] (2) Posture stability assessment: The collected COP data were processed to calculate the sway area $A$. The sway area was obtained by fitting the 95% confidence ellipse area of ​​the COP data and was used to assess the overall postural stability of the subject. The smaller the sway area, the more stable the posture. The calculation formula of the sway area A is:

[0011]

[0012] Where k is the confidence coefficient. For a 95% confidence level, k = 2.4477. λ1 and λ2 are the eigenvalues ​​of the covariance matrix C of the COP data in the anterior-posterior (AP) direction and the lateral-lateral (ML) direction. The covariance matrix C is composed of the COP displacement data x i and i The calculation results are:

[0013]

[0014] in, and x i and i The variance, σ xy is the covariance;

[0015] (3) COP signal decomposition: COP data is preprocessed by filtering and denoising, and the preprocessed COP signal is decomposed by discrete wavelet transform (such as Symlet-8 wavelet function) to decompose the COP signal into different frequency bands. In the process of signal decomposition, each frequency band is matched with the corresponding sensory system;

[0016] (4) Multi-sensory weighted analysis: For each frequency band, calculate its energy proportion to quantify the contribution of each sensory system to balance control;

[0017] (5) Data integration and evaluation: The overall balance performance of the subjects was evaluated based on the sway area A. The smaller the A value, the more stable the posture. The subject's ability to reweight sensory information was evaluated by comparing the changes in the energy proportion of each sensory system under different sensory disturbance conditions.

[0018] In the above-mentioned method for preparing a multisensory reweighted dynamic balance assessment method, visual perturbation: presenting a rotating or moving visual scene through a virtual reality (VR) device to interfere with the visual input of the subject;

[0019] Vestibular perturbation: Using a controllable rotating platform to apply acceleration or deceleration of body rotation to interfere with the subject's vestibular input;

[0020] Proprioceptive perturbation: using a soft support surface (e.g., foam pad) to change the stability of the standing surface and interfere with the subject's proprioceptive input;

[0021] The subject stands on the force plate and is tested according to a preset test protocol to collect COP data at a sampling frequency of 100 Hz; step (2) processes the collected COP data and performs a posture stability assessment, while step (3) pre-processes the COP data and decomposes it into different frequency bands. During the signal decomposition process, each frequency band is matched to a corresponding sensory system.

[0022] In the above-mentioned preparation method of a multi-sensory re-weighted dynamic balance assessment method, the step (3) decomposes the COP data into: an ultra-low frequency band, an extremely low frequency band, a low frequency band, and a mid-frequency band;

[0023] Ultra-low frequency band (f<0.10Hz): corresponds to the activity frequency of the visual system;

[0024] Very low frequency band (0.10≤f<0.39Hz): corresponds to the activity frequency of the vestibular system;

[0025] Low frequency band (0.39≤f<1.56Hz): corresponds to the activity frequency regulated by the cerebellum;

[0026] Middle frequency band (1.56≤f<6.25Hz): corresponds to the activity frequency of the proprioceptive system;

[0027] Discrete wavelet transform decomposes the COP signal by the following formula:

[0028] A j (n) = ∑kh(k)·Aj-1(2n-k)

[0029] D j (n) = ∑kg(k)·Aj-1(2n-k)

[0030] Among them, A j (n) is the approximation coefficient of the jth layer, D j (n) is the detail coefficient of the jth layer, h(k) and g(k)

[0031] are the low-pass and high-pass filter coefficients respectively, and A0(n) is the original signal; the data obtained in step (3) is used for the energy proportion calculation in step (4).

[0032] In the preparation method of the above-mentioned multi-sensory weighted dynamic balance assessment method, the energy ratio of step (4) is as follows: energy E j The calculation formula is:

[0033]

[0034] The total energy is:

[0035] Etotal=∑j=1 L E j

[0036] The energy proportion of each frequency band P j for:

[0037]

[0038] Then, the energy proportions of the corresponding frequency bands are added together to obtain the energy proportions of each sensory system. For example, the energy proportion of the visual system is P 视觉 for:

[0039] P vision = ∑ visual frequency band P j ; Step (5) evaluates the subject's ability to re-weight sensory information through the energy proportion of each sensory system.

[0040] In the preparation method of the above-mentioned multi-sensory weighted dynamic balance assessment method, the energy proportion P of each sensory system is j , assessing the subject's ability to reweight sensory information; Example: Under visual perturbation conditions, if the energy proportion of the visual frequency band is P 视觉 The energy proportion of proprioception or vestibular sensation is reduced. 本体 or P 前庭 An increase indicates that the subject is able to reweight sensory input to maintain balance.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention combines linear indicators (sway area) and nonlinear indicators (energy distribution of discrete wavelet transform) to comprehensively and objectively evaluate the subject's postural stability and the contribution of each sensory system to balance control under multi-sensory disturbance conditions, thus making up for the shortcomings of the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Designed for sensory disturbances;

[0044] Figure 2Comparison of sway area between the Tai Chi group and the control group under visual perturbation conditions (*p<0.05);

[0045] Figure 3 Comparison of the energy proportion of each sensory system between the Tai Chi group and the control group under visual perturbation conditions (*p<0.05). DETAILED DESCRIPTION

[0046] The present invention will be further described in conjunction with the accompanying drawings.

[0047] See also Figures 1 to 3 The present invention provides a multi-sensory weighted dynamic balance assessment method, which specifically includes the following steps:

[0048] 1. Data collection:

[0049] The center of plantar pressure (COP) data of the subjects were collected under different sensory perturbation conditions. The sensory perturbation included single perturbation of vision, vestibular and proprioception, and combined perturbation of multiple senses:

[0050] Visual perturbation: Presenting rotating or moving visual scenes through virtual reality (VR) devices to interfere with the subject's visual input;

[0051] Vestibular perturbation: Using a controllable rotating platform to apply acceleration or deceleration of body rotation to interfere with the subject's vestibular input;

[0052] Proprioceptive perturbation: using a soft support surface (e.g., foam pad) to change the stability of the standing surface and interfere with the subject's proprioceptive input;

[0053] The subjects stood on the force plate and were tested according to the preset test protocol to collect COP data with a sampling frequency of 100 Hz.

[0054] 2. Postural stability assessment:

[0055] The collected COP data is processed to calculate the sway area $A$. The sway area is obtained by fitting the 95% confidence ellipse area of ​​the COP data and is used to evaluate the overall posture stability of the subject. The smaller the sway area, the more stable the posture. The calculation formula of the sway area A is:

[0056]

[0057] Where k is the confidence coefficient, for a 95% confidence level, k = 2.4477; λ1 and λ2 are the eigenvalues ​​of the covariance matrix C of the COP data in the anterior-posterior (AP) direction and the lateral-lateral (ML) direction. The covariance matrix C is composed of the COP displacement data x i and i The calculation results are:

[0058]

[0059] in, and x i and i The variance, σ xy is the covariance.

[0060] 3.COP signal decomposition:

[0061] The COP data is preprocessed by filtering and denoising, and the preprocessed COP signal is decomposed by discrete wavelet transform (such as Symlet-8 wavelet function) to decompose the COP signal into different frequency bands. In the process of signal decomposition, each frequency band is matched with the corresponding sensory system:

[0062] Ultra-low frequency band (f<0.10Hz): corresponds to the activity frequency of the visual system;

[0063] Very low frequency band (0.10≤f<0.39Hz): corresponds to the activity frequency of the vestibular system;

[0064] Low frequency band (0.39≤f<1.56Hz): corresponds to the activity frequency regulated by the cerebellum;

[0065] Middle frequency band (1.56≤f<6.25Hz): corresponds to the activity frequency of the proprioceptive system;

[0066] Discrete wavelet transform decomposes the COP signal by the following formula:

[0067] A j (n) = ∑kh(k)·Aj-1(2n-k)

[0068] D j (n) = ∑kg(k)·Aj-1(2n-k)

[0069] Among them, A j (n) is the approximation coefficient of the jth layer, D j (n) is the detail coefficient of the jth layer, h(k) and g(k) are the low-pass and high-pass filter coefficients respectively, and A0(n) is the original signal.

[0070] 4. Multi-sensory weighted analysis:

[0071] For each frequency band, calculate its energy proportion to quantify the contribution of each sensory system to balance control. j The calculation formula is:

[0072]

[0073] The total energy is:

[0074] Etotal=∑j=1 L E j

[0075] The energy proportion of each frequency band P j for:

[0076]

[0077] Then, the energy proportions of the corresponding frequency bands are added together to obtain the energy proportions of each sensory system. For example, the energy proportion of the visual system is P 视觉 for:

[0078] P vision = ∑ visual frequency band P j

[0079] 5. Data integration and evaluation:

[0080] Postural stability: The overall balance performance of the subject was assessed based on the sway area A. The smaller the A value, the more stable the posture.

[0081] Sensory reweighting ability: By comparing the energy proportion P of each sensory system under different sensory disturbance conditions j Example: Under visual perturbation conditions, if the energy proportion of the visual frequency band is P 视觉 The energy proportion of proprioception or vestibular sensation is reduced. 本体 or P 前庭 An increase indicates that the subject is able to reweight sensory input to maintain balance.

[0082] The present invention will now be further described in detail in conjunction with specific experiments. The application of the present invention is not limited to the following experimental applications, and any form of modification made to the present invention will fall within the protection scope of the present invention.

[0083] 1. Experimental Subjects

[0084] 1. Inclusion criteria

[0085] A total of 48 elderly people aged 60 to 83 years were recruited from multiple community sports centers. All participants were right-handed and right-footed, with normal or corrected-to-normal vision. Inclusion criteria included: age ≥ 60 years, able to walk independently without assistive devices. The Tai Chi group (TC group) was required to have long-term Tai Chi practice experience, at least 3 days a week, ≥ 30 minutes each time, for more than 6 years; the control group had no Tai Chi experience, but had general aerobic exercise experience, such as walking, at least 3 days a week, ≥ 30 minutes each time, for more than 6 years.

[0086] 2. Exclusion criteria

[0087] Exclusion criteria were: history of sensory, neurological or musculoskeletal injuries that affect balance; cardiovascular disease, motion sickness, dizziness, vertigo or other vestibular disorders; psychological problems related to fall risk, such as fear of falling, anxiety or depression; Montreal Cognitive Assessment (MoCA) score less than 25 points; Berg Balance Scale (BBS) score less than 45 points. All participants signed informed consent before the start of the experiment and received certain financial compensation. The experiment followed the ethical standards of the Declaration of Helsinki and was approved by the Institutional Review Board.

[0088] 2. Experimental Apparatus and Setup

[0089] 1. Sensory perturbation system

[0090] The visual perturbation device used a virtual reality (VR) helmet (HTC Vive Pro 2) to present a rotating visual scene. The visual scene began to rotate clockwise at a speed of 30° / s for 36 seconds after 20 seconds, then stopped rotating, and the subjects continued to stand for 44 seconds. The vestibular perturbation device used a controllable rotating platform (All Controller, Nanjing, China), which began to rotate clockwise at a speed of 30° / s for 36 seconds after 20 seconds, then stopped rotating, and the subjects continued to stand for 44 seconds. The proprioceptive perturbation device used a soft foam pad (Airex AG, Switzerland) placed on a force plate, and the subjects stood on the foam pad for the entire 100 seconds.

[0091] 2. Data acquisition equipment

[0092] The data acquisition device used was a Wii Balance Board (Nintendo, Kyoto, Japan) with a sampling frequency of 100 Hz to record the center of plantar pressure (COP) data of the subjects. This device has been shown to have good reliability and validity in COP measurement.

[0093] 3. Safety measures

[0094] During the experiment, the subjects wore safety belts to prevent falls, and two researchers kept a close eye on the subjects during the test to ensure their safety.

[0095] 3. Test protocol design

[0096] Subjects performed a standing balance task under baseline conditions and six sensory perturbation conditions, e.g. Figure 1As shown, each task lasted 100 seconds. The order of the test conditions was randomized, and there was a 5-minute break between each condition. Under baseline conditions (no perturbation), the subjects wore a VR helmet and stood for 100 seconds without any sensory perturbation. Single sensory perturbation conditions included visual perturbation (V), vestibular perturbation (Ve), and proprioceptive perturbation (S), as described in the above device. Multisensory combined perturbation conditions included visual-vestibular perturbation (VVe), which applied visual and vestibular perturbations at the same time; visual-proprioceptive perturbation (VS), which applied visual and proprioceptive perturbations at the same time; vestibular-proprioceptive perturbation (VeS), which applied vestibular and proprioceptive perturbations at the same time.

[0097] 4. Data Collection and Preprocessing

[0098] 1. Data Collection

[0099] Under each test condition, the COP data of the subject are collected, including the displacement sequence x(n) in the anterior-posterior (AP) direction and the displacement sequence y(n) in the lateral-lateral (ML) direction, where n=1, 2, ..., N, and N is the number of sampling points.

[0100] 2. Data Preprocessing

[0101] Data preprocessing includes: discarding the acceleration and deceleration phase data when the rotation starts and stops, and retaining the data of the stable phase; filtering the COP data with a 20 Hz low-pass, second-order, zero-phase lag Butterworth filter to remove high-frequency noise; subtracting the average value from the filtered COP data to make the signal centered on zero.

[0102] 5. Postural Stability Assessment

[0103] Use the ellipse to fit the COP data and get a 95% confidence ellipse. Calculate the covariance matrix C of the COP data:

[0104]

[0105] Among them, σAP 2 and σML 2 are the variances of x(n) and y(n), σ AP,ML is the covariance. Then calculate the eigenvalues ​​λ1 and λ2 (λ1≥λ2) of the covariance matrix C. The calculation formula for the swing area A is:

[0106]

[0107] Wherein, k is the confidence coefficient, and for a confidence level of 95%, k = 2.4477. The smaller the sway area A is, the better the posture stability is.

[0108] VI. COP signal decomposition and multi-sensory re-weighted analysis

[0109] 1. Discrete Wavelet Transform (DWT)

[0110] Symlet-8 wavelet is used as the wavelet basis function, according to the sampling frequency f s =100Hz, select the decomposition level L = 12, and decompose the COP signal into different frequency bands. The frequency band corresponding to scale j is:

[0111]

[0112] The corresponding relationship of the sensory system is: the ultra-low frequency band (f<0.10Hz, scale 9-12) corresponds to the visual system; the very low frequency band (0.10≤f<0.39Hz, scale 7-8) corresponds to the vestibular system; the low frequency band (0.39≤f<1.56Hz, scale 5-6) corresponds to the cerebellum regulation; the medium frequency band (1.56≤f<6.25Hz, scale 1-4) corresponds to the proprioceptive system. The wavelet decomposition formula is:

[0113] A j (n) = ∑kh(k)·Aj-1(2n-k)

[0114] D j (n) = ∑kg(k)·Aj-1(2n-k)

[0115] Among them, A j (n) is the approximation coefficient of the jth layer, D j (n) is the detail coefficient of the jth layer, h(k) and g(k) are the filter coefficients of the low-pass and high-pass filters respectively, and A0(n) = x(n) or y(n).

[0116] 2. Energy calculation

[0117] For each scale j, calculate the detail coefficient D j Energy of (n):

[0118]

[0119] The total energy is:

[0120] Etotal=∑j=1 L E j

[0121] The energy proportion of each scale is:

[0122]

[0123] 3. Quantification of sensory system contribution

[0124] Add up the energy proportions of the corresponding frequency bands to get the energy proportions of each sensory system. The energy proportion of the visual system is:

[0125] P vision = ∑j = 9 12 P j

[0126] The energy share of the vestibular system is:

[0127] P vestibule = ∑j = 7 8 P j

[0128] The energy share of the cerebellum system is:

[0129] P cerebellum = ∑j = 5 6 P j

[0130] The energy proportion of the proprioceptive system is:

[0131] P entity = ∑j = 1 4 P j

[0132] The sensory reweighting ability of the subjects was evaluated by comparing the changes in the energy proportion of each sensory system under different sensory disturbance conditions.

[0133] VII. Result Analysis and Application

[0134] 1. Comparison of Postural Stability

[0135] During the implementation of the method of the present invention, the posture stability of the subjects under different sensory disturbance conditions was evaluated. By calculating indicators such as the sway area A, it was found that the posture stability of the Tai Chi group under each sensory disturbance condition was better than that of the control group.

[0136] Specifically, if Figure 2 As shown in the figure, under visual disturbance conditions, the sway area of ​​the Tai Chi group was significantly smaller than that of the control group (p<0.05). The statistical analysis results showed that the average sway area of ​​the Tai Chi group was 120.5±15.3mm2, and that of the control group was 158.7±20.1mm2. Under vestibular disturbance conditions, the average sway area of ​​the Tai Chi group was 130.2±16.7mm2, and that of the control group was 170.4±22.5mm2. The difference between the two groups was statistically significant (p<0.05).

[0137] In addition, under the condition of multisensory combined perturbation, the Tai Chi group also showed better postural stability. For example, under the condition of visual-vestibular perturbation, the average sway area of ​​the Tai Chi group was 140.8±18.2mm2, and that of the control group was 182.6±24.3mm2, with a significant difference (p<0.05). These results indicate that Tai Chi practitioners can more effectively maintain postural stability in a complex sensory environment.

[0138] 2. Sensory reweighting ability assessment

[0139] Visual perturbation conditions: Figure 3 As shown in the figure, the energy proportion of the proprioceptive system (mid-frequency band) in the Tai Chi group was significantly higher than that in the control group (p<0.01), which were 45.2%±4.5% and 38.7%±5.1%, respectively. The energy proportion of the vestibular system (extremely low frequency band) was also higher than that in the control group (p<0.05). At the same time, the energy proportion of the visual system (ultra-low frequency band) in the Tai Chi group was significantly lower than that in the control group (p<0.01), which were 15.3%±2.8% and 22.6%±3.2%, respectively. This shows that Tai Chi practitioners can strengthen their reliance on proprioception and vestibular sensation when visual information is disturbed.

[0140] Vestibular disturbance condition: The proportion of energy in the visual system of the Tai Chi group increased (25.1% ± 3.0%), which was significantly higher than that of the control group (19.4% ± 2.7%, p < 0.05), and the proportion of energy in the proprioceptive system also increased. At the same time, the proportion of energy in the vestibular system decreased, indicating that Tai Chi practitioners can enhance the use of visual and proprioceptive information when vestibular information is disturbed.

[0141] Combined visual-proprioceptive disturbance condition: The energy percentage of the vestibular system in the Tai Chi group increased significantly (35.7%±4.2%), which was higher than the 28.3%±3.8% in the control group (p<0.01). The energy percentage of the visual and proprioceptive systems decreased, indicating that the reliance on vestibular information was enhanced when multiple sensory information was disturbed.

[0142] Vestibular-proprioceptive combined disturbance condition: The proportion of visual system energy in the Tai Chi group increased to 30.5%±3.5%, significantly higher than the 22.8%±3.1% in the control group (p<0.01). This shows that Tai Chi practitioners enhanced their use of visual information under this condition.

[0143] The above results prove that the Tai Chi group can flexibly adjust the weight of each sensory system according to the changes in the sensory environment and achieve effective sensory reweighting. The method of the present invention can accurately quantify the contribution of each sensory system in balance control and evaluate the sensory reweighting ability of the subjects.

[0144] The above is a detailed introduction to a multi-sensory weighted dynamic balance assessment method provided by an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the technical solutions disclosed by the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation of the present invention.

Claims

1. A multi-sensory weighted dynamic balance assessment method, characterized by: The following steps are involved: (1) Data collection: The center of plantar pressure (COP) data of the subjects were collected under different sensory perturbation conditions, including single perturbations of vision, vestibule and proprioception, and combined perturbations of multiple senses; (2) Posture stability assessment: The collected COP data were processed to calculate the sway area $A$. The sway area was obtained by fitting the 95% confidence ellipse area of ​​the COP data and was used to assess the overall postural stability of the subject. The smaller the sway area, the more stable the posture. The calculation formula of the sway area A is: Where k is the confidence coefficient. For a 95% confidence level, k = 2.4477. λ1 and λ2 are the eigenvalues ​​of the covariance matrix C of the COP data in the anterior-posterior (AP) direction and the lateral-lateral (ML) direction. The covariance matrix C is composed of the COP displacement data x i and i The calculation results are: in, and x i and i The variance, σ xy is the covariance; (3) COP signal decomposition: COP data is preprocessed by filtering and denoising, and the preprocessed COP signal is decomposed by discrete wavelet transform (such as Symlet-8 wavelet function) to decompose the COP signal into different frequency bands. In the process of signal decomposition, each frequency band is matched with the corresponding sensory system; (4) Multi-sensory weighted analysis: For each frequency band, calculate its energy proportion to quantify the contribution of each sensory system to balance control; (5) Data integration and evaluation: The overall balance performance of the subjects was evaluated based on the sway area A. The smaller the A value, the more stable the posture. The subject's ability to reweight sensory information was evaluated by comparing the changes in the energy proportion of each sensory system under different sensory disturbance conditions.

2. A multi-sensory weighted dynamic balance assessment method according to claim 1, characterized in that: Visual perturbation: Presenting rotating or moving visual scenes through virtual reality (VR) devices to interfere with the subject's visual input; Vestibular perturbation: Using a controllable rotating platform to apply acceleration or deceleration of body rotation to interfere with the subject's vestibular input; Proprioceptive perturbation: using a soft support surface (e.g., foam pad) to change the stability of the standing surface and interfere with the subject's proprioceptive input; The subject stands on the force plate and is tested according to a preset test protocol to collect COP data at a sampling frequency of 100 Hz; step (2) processes the collected COP data and performs a posture stability assessment, while step (3) pre-processes the COP data and decomposes it into different frequency bands. During the signal decomposition process, each frequency band is matched to a corresponding sensory system.

3. A multi-sensory weighted dynamic balance assessment method according to claim 2, characterized in that: The step (3) decomposes the COP data into: ultra-low frequency band, very low frequency band, low frequency band, and medium frequency band; Ultra-low frequency band (f<0.10Hz): corresponds to the activity frequency of the visual system; Very low frequency band (0.10≤f<0.39Hz): corresponds to the activity frequency of the vestibular system; Low frequency band (0.39≤f<1.56Hz): corresponds to the activity frequency regulated by the cerebellum; Middle frequency band (1.56≤f<6.25Hz): corresponds to the activity frequency of the proprioceptive system; Discrete wavelet transform decomposes the COP signal by the following formula: THE j (n)=∑kh(k)·Aj-1(2n-k) <h2 style=";text-align:left;direction:ltr">D<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> (n) = ∑kg(k)·Aj-1(2n-k) Among them, A j (n) is the approximation coefficient of the jth layer, D j (n) is the detail coefficient of the jth layer, h(k) and g(k) are the low-pass and high-pass filter coefficients respectively, and A0(n) is the original signal; the data obtained in step (3) is used for the energy proportion calculation in step (4).

4. A multi-sensory weighted dynamic balance assessment method according to claim 3, characterized in that: The energy proportions of step (4) are as follows: j The calculation formula is: The total energy is: Total=∑j=1 L THAT j The energy proportion of each frequency band P j for: Then, the energy proportions of the corresponding frequency bands are added together to obtain the energy proportions of each sensory system. For example, the energy proportion of the visual system is P 视觉 for: P vision = ∑ visual frequency band P j ; Step (5) evaluates the subject's ability to re-weight sensory information through the energy proportion of each sensory system.

5. A multi-sensory weighted dynamic balance assessment method according to claim 4, characterized in that: The energy proportion of each sensory system P j changes in , assessing the subject's ability to re-weight sensory information; Example: Under visual perturbation conditions, if the energy proportion of the visual frequency band is P 视觉 The energy proportion of proprioception or vestibular sensation is reduced. 本体 or P 前庭 An increase indicates that the subject is able to reweight sensory input to maintain balance.

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