A multi-sensory weighted dynamic balance assessment method

Abstract

The present invention discloses a multi-sensory reweighted dynamic balance assessment method, comprising: (1) data acquisition: collecting center of plantar pressure (COP) data of a subject under different sensory disturbance conditions; (2) postural stability assessment: fitting a 95% confidence ellipse of the COP data and calculating the sway area A to assess the subject's overall postural stability; (3) COP signal decomposition: pre-processing the COP data and decomposing it into different frequency bands using discrete wavelet transform; (4) multi-sensory reweighted analysis: mapping the frequency bands to sensory systems, calculating the energy proportion, and quantifying the contribution of each sensory system to balance control; and (5) data integration and evaluation. The present invention comprehensively and objectively assesses the subject's postural stability and the contribution of each sensory system to balance control under multi-sensory disturbance conditions by combining a linear index (sway area) and a nonlinear index (energy distribution of discrete wavelet transform).

Description

Technical Field

[0001] The present 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] With the increasing aging of the global population, balance dysfunction and the risk of falls in the elderly population are gradually increasing, becoming a serious public health issue. Balance control is a complex sensory-motor process that requires the nervous system to effectively integrate multiple sensory information, including visual, vestibular, and proprioceptive perception, to maintain postural stability. However, with aging, the sensory integration ability and sensory reweighting mechanism of the elderly gradually deteriorate, leading to decreased balance function and increased risk of falls.

[0003] Existing balance assessment methods primarily employ static or constant sensory perturbations, such as eyes-open / eye-closed, single-leg standing, or tandem standing tasks. While these methods can assess a subject's balance ability under specific conditions, they fail to fully simulate the dynamic sensory environment of daily life and struggle to quantify the specific contributions of each sensory system to balance control. Furthermore, traditional assessment metrics are often linear, operating on a single scale, such as sway velocity and sway amplitude, which fail to fully reflect the complexity and multi-scale nature of balance control.

[0004] In recent years, advances in signal processing technology have provided new methods for assessing balance function. Discrete wavelet transform, 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 contributions of the visual, vestibular, and proprioceptive systems to balance control. However, current research has mostly focused on the analysis of single sensory perturbations, lacking in-depth research on the sensory reweighting mechanism under conditions of multisensory combined perturbations.

[0005] Therefore, a comprehensive assessment method combining linear and nonlinear analysis methods to assess postural stability and sensory reweighting under dynamic, multisensory perturbations is urgently needed. This is crucial for accurately quantifying the contribution of each sensory system to balance control, gaining a deeper understanding of the mechanisms of balance decline in the elderly, and developing effective interventions. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems raised in the above background technology and 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 reweighted 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 reweighting ability under dynamic, multi-sensory disturbance conditions.

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

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

[0010] (2) Posture stability assessment: The collected COP data were processed and the sway area A was calculated. 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, and the covariance matrix C is composed of the COP displacement data x i and y i Calculation yields:

[0013]

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

[0015] (3) COP signal decomposition: 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;

[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 preparation method of the above-mentioned 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 subject's visual input;

[0019] Vestibular perturbation: Using a controllable rotating platform to apply acceleration or deceleration of body rotation, disrupting 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 postural stability assessment, while step (3) preprocesses the COP data and decomposes it into different frequency bands. During the signal decomposition process, each frequency band is mapped to a corresponding sensory system.

[0022] In the preparation method of the above-mentioned multi-sensory weighted dynamic balance assessment method, the step (3) decomposes the COP data into: ultra-low frequency band, very low frequency band, low frequency band, and 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 using the following formula:

[0028] A j (n) = ∑kh(m)·Aj-1(2n-m)

[0029] D j (n) = ∑kg(m)·Aj-1(2n-m)

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

[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 to calculate the energy ratio 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]

[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 reweight 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 used. j The change of , evaluates the subject's ability to re-weight sensory information; Example: Under visual perturbation conditions, if the energy proportion of the visual frequency band P 视觉 Decreased, while the energy proportion of proprioception or vestibular sensation P 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, thereby making up for the shortcomings of the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Designed for sensory perturbations;

[0044] Figure 2 Comparison 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 with reference to 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 perturbations, including single perturbations of vision, vestibule, and proprioception, as well as combined perturbations of multiple senses:

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

[0051] Vestibular perturbation: Using a controllable rotating platform to apply acceleration or deceleration of body rotation, disrupting 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 was 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 subject's overall postural stability. The smaller the sway area, the more stable the posture. The calculation formula for 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, and the covariance matrix C is composed of the COP displacement data x i and y i Calculation yields:

[0058]

[0059] in, and x i and y 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 discrete wavelet transform (such as the Symlet-8 wavelet function) is used to decompose the preprocessed COP signal into different frequency bands. During the signal decomposition process, each frequency band is mapped to 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 using the following formula:

[0067] A j (n) = ∑kh(m)·Aj-1(2n-m)

[0068] D j (n) = ∑kg(m)·Aj-1(2n-m)

[0069] Among them, A j (n) is the approximate coefficient of the jth layer, D j (n) is the detail coefficient of the jth layer, h(m) and g(m) 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]

[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 P 视觉 Decreased, while the energy proportion of proprioception or vestibular sensation P 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 with reference to 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 scope of protection 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 various community sports centers. All participants were right-handed and right-foot dominant and had normal or corrected-to-normal vision. Inclusion criteria included age ≥60 years and the ability 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 included a history of sensory, neurological, or musculoskeletal injuries that affect balance; cardiovascular disease, motion sickness, dizziness, vertigo, or other vestibular disorders; psychological problems associated with fall risk, such as fear of falling, anxiety, or depression; a Montreal Cognitive Assessment (MoCA) score of less than 25; or a Berg Balance Scale (BBS) score of less than 45. All participants signed an informed consent form before the start of the experiment, and the experiment adhered to 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) headset (HTC Vive Pro 2) to present a rotating visual scene. After 20 seconds, the visual scene began to rotate clockwise at a speed of 30° / second for 36 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). After 20 seconds, the platform began to rotate clockwise at a speed of 30° / second for 36 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), which recorded the subjects' center of plantar pressure (COP) data at a sampling frequency of 100 Hz. This device has been shown to have good reliability and validity in measuring COP.

[0093] 3. Safety measures

[0094] During the experiment, the subjects wore safety harnesses to prevent falls, and two researchers closely monitored the subjects during the test to ensure their safety.

[0095] 3. Test Protocol Design

[0096] The subjects performed a standing balance task under baseline conditions and six sensory perturbation conditions, e.g. Figure 1 As shown, each task lasted 100 seconds. The order of the test conditions was randomized, with 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 apparatus. 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; and 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 were 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, where N is the number of sampling points.

[0100] 2. Data Preprocessing

[0101] Data preprocessing includes discarding the acceleration and deceleration data at the start and stop of rotation and retaining the data during the stable phase; filtering the COP data using a 20 Hz low-pass, second-order, zero-phase lag Butterworth filter to remove high-frequency noise; and subtracting the mean value from the filtered COP data to center the signal around 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 of the covariance matrix C (λ1≥λ2). The calculation formula for the swing area A is:

[0106]

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

[0108] 6. COP Signal Decomposition and Multisensory Reweighted 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 bands corresponding to scale j are:

[0111]

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

[0113] A j (n) = ∑kh(m)·Aj-1(2n-m)

[0114] D j (n) = ∑kg(m)·Aj-1(2n-m)

[0115] Among them, A j (n) is the approximate coefficient of the jth layer, D j (n) is the detail coefficient of the jth layer, h(m) and g(m) 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 contributions

[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 proportion of the cerebellum system is:

[0129] P cerebellum = ∑j = 5 6 P j

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

[0131] P body = ∑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. Results Analysis and Application

[0134] 1. Comparison of Postural Stability

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

[0136] Specifically, if Figure 2 As shown in the results, under visual perturbation conditions, the sway area in the Tai Chi group was significantly smaller than that in the control group (p < 0.05). Statistical analysis showed that the mean sway area in the Tai Chi group was 120.5 ± 15.3 mm², while that in the control group was 158.7 ± 20.1 mm². Under vestibular perturbation conditions, the mean sway area in the Tai Chi group was 130.2 ± 16.7 mm², while that in the control group was 170.4 ± 22.5 mm², with a statistically significant difference between the two groups (p < 0.05).

[0137] Furthermore, under conditions of multisensory perturbation, the Tai Chi group also demonstrated improved postural stability. For example, under conditions of visual-vestibular perturbation, the mean sway area in the Tai Chi group was 140.8±18.2 mm², while that in the control group was 182.6±24.3 mm², a significant difference (p<0.05). These results suggest that Tai Chi practitioners are more effective in maintaining postural stability in complex sensory environments.

[0138] 2. Sensory Reweighted Ability Assessment

[0139] Visual perturbation conditions: e.g. Figure 3 As shown, the proprioceptive system energy percentage (mid-frequency band) in the Tai Chi group was significantly higher than in the control group (p < 0.01), reaching 45.2% ± 4.5% and 38.7% ± 5.1%, respectively. The vestibular system energy percentage (extremely low-frequency band) was also higher than in the control group (p < 0.05). Simultaneously, the visual system energy percentage (ultra-low-frequency band) in the Tai Chi group was significantly lower than in the control group (p < 0.01), reaching 15.3% ± 2.8% and 22.6% ± 3.2%, respectively. This suggests that Tai Chi practitioners can enhance their reliance on proprioception and vestibular sensations when visual information is disrupted.

[0140] Under vestibular disturbance conditions, the Tai Chi group showed an increase in the proportion of visual system energy (25.1% ± 3.0%), significantly higher than the control group (19.4% ± 2.7%, p < 0.05). The proportion of proprioceptive system energy also increased. Meanwhile, the proportion of vestibular system energy decreased, suggesting that Tai Chi practitioners are able to enhance their utilization of visual and proprioceptive information when vestibular information is disturbed.

[0141] Under the combined visual-proprioceptive perturbation condition, the vestibular system energy share increased significantly in the Tai Chi group (35.7% ± 4.2%), compared to the control group's 28.3% ± 3.8% (p < 0.01). The decreased energy share of the visual and proprioceptive systems suggests a greater reliance on vestibular information when multiple sensory inputs are disrupted.

[0142] Under the combined vestibular-proprioceptive perturbation condition, the Tai Chi group's visual system energy share increased to 30.5% ± 3.5%, significantly higher than the control group's 22.8% ± 3.1% (p < 0.01). This suggests that Tai Chi practitioners enhanced their use of visual information under this condition.

[0143] These results demonstrate that the Tai Chi group was able to flexibly adjust the weights of each sensory system based on changes in the sensory environment, achieving effective sensory reweighting. The method of the present invention can accurately quantify the contribution of each sensory system to balance control and assess the subject's sensory reweighting ability.

[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 herein 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 may 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 COP data of the plantar pressure center 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 and the sway area A was calculated. 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 front-to-back direction and the left-to-right direction. The covariance matrix C is composed of the COP displacement data x i and y i Calculation yields: in, and x i and y i The variance, σ xy is the covariance; (3) COP signal decomposition: The COP data is preprocessed by filtering and denoising, and the preprocessed COP signal is decomposed by discrete wavelet transform 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. The 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, disrupting the subject's vestibular input; Proprioceptive perturbation: using a soft support surface 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 postural stability assessment, while step (3) preprocesses the COP data and decomposes it into different frequency bands. During the signal decomposition process, each frequency band is mapped to a corresponding sensory system.

3. The 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 mid-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 using the following formula: A j (n)=∑kh(m)·Aj-1(2n-m) D j (n)=∑kg(m)·Aj-1(2n-m) Among them, A j (n) is the approximate coefficient of the jth layer, D j (n) is the detail coefficient of the jth layer, h(m) and g(m) are the low-pass and high-pass filter coefficients respectively; the data obtained in step (3) is used for the energy proportion calculation in step (4).

4. The 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: Where L is the number of decomposition levels set in wavelet decomposition; 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, among which 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 reweight sensory information through the energy proportion of each sensory system.

5. The multi-sensory weighted dynamic balance assessment method according to claim 4, characterized in that: The energy proportion P of each sensory system j The change of , evaluates the subject's ability to re-weight sensory information; among them, under the condition of visual perturbation, if the energy proportion of the visual frequency band P 视觉 Decreased, while the energy proportion of proprioception or vestibular sensation P 本体 or P 前庭 An increase indicates that the subject is able to reweight sensory input to maintain balance.

Images