A method for assessing circadian rhythm disorders based on wearable devices

Abstract

The present invention provides a method for evaluating circadian rhythm disorders based on wearable devices, which relates to the technical field of circadian rhythm evaluation. The method includes: continuously collecting light intensity data and body movement data of an individual in a natural living state through a wearable device; preprocessing the collected light intensity data and body movement data; performing discrete Fourier transform on the preprocessed light intensity data and body movement data, extracting the fundamental frequency component and representing it as a phasor, and calculating the difference phasor between the body movement phasor and the light phasor; evaluating the degree of circadian rhythm disorder of the individual according to the calculated difference phasor between the body movement phasor and the light phasor. The present invention realizes convenient and non-invasive circadian rhythm monitoring through wearable devices, and provides an effective tool for early screening and intervention of circadian rhythm disorders.

Description

Technical Field

[0001] The present invention relates to the technical field of circadian rhythm assessment, and specifically, to a method for assessing circadian rhythm disorders based on a wearable device. Background Art

[0002] Circadian rhythm refers to the oscillation of physiological, biochemical, and behavioral processes with an approximate 24-hour cycle in living organisms. These rhythms are regulated by an internal biological clock and synchronized with external environmental factors (especially the light cycle). Disorders of circadian rhythm are closely related to the occurrence and development of various diseases, including sleep disorders, metabolic diseases (such as diabetes, obesity), cardiovascular diseases, mental diseases (such as depression, bipolar disorder), periodontal diseases, and even cancer. Therefore, accurately assessing the circadian rhythm state of an individual is of great significance for disease prevention, diagnosis, and treatment.

[0003] Traditional circadian rhythm assessment methods mainly rely on measuring physiological indicators such as core body temperature or melatonin in a strictly controlled environment (such as a sleep laboratory). These methods are complex to operate, costly, require professional equipment and personnel, and are difficult to continuously monitor in daily life for a long time, limiting their widespread application in clinical and scientific research. In recent years, with the development of wearable device technology, wrist actigraphs have been widely used in circadian rhythm research due to their convenience, non-invasiveness, and long-term monitoring ability. The circadian rhythm disorder detection method based on an actigraph monitors the body movement of a human through a built-in acceleration sensor and infers the activity-rest rhythm of an individual based on the body movement data. However, simple body movement data cannot reflect the impact of light, an important circadian rhythm synchronization factor, on the human body.

[0004] Existing circadian rhythm disorder assessment methods have many limitations in specific implementation and are difficult to meet the requirements of convenient, accurate, and comprehensive assessment. The method based on a polysomnograph is expensive and complex to operate, requires technicians to operate in a professional sleep laboratory, cannot achieve large-scale population screening, and can only reflect the rhythm during sleep and is difficult to assess the circadian rhythm changes of an individual in the waking state. Another common method is based on a wrist actigraph, which infers the circadian rhythm by analyzing body movement data. However, this method only relies on single body movement information, is easily interfered by factors such as individual activity habits, and completely ignores light, a key circadian rhythm synchronization factor, resulting in limited accuracy of the assessment results. Summary of the Invention

[0005] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a method for evaluating circadian rhythm disorders based on wearable devices, which realizes convenient and non-invasive circadian rhythm monitoring through wearable devices, and combines spectrally weighted light data and a standardized data processing process, improving the accuracy and reliability of the evaluation results, and providing an effective tool for the early screening and intervention of circadian rhythm disorders.

[0006] To solve the above problems, the technical solution of the present invention is as follows:

[0007] A method for evaluating circadian rhythm disorders based on wearable devices, comprising the following steps:

[0008] Continuously collect light intensity data and body movement data of an individual in a natural living state through a wearable device;

[0009] Preprocess the collected light intensity data and body movement data;

[0010] Perform discrete Fourier transform on the preprocessed light intensity data and body movement data, extract the fundamental frequency components and represent them as phasors, and calculate the difference phasor between the body movement phasor and the light phasor;

[0011] Evaluate the degree of circadian rhythm disorder of an individual according to the calculated difference phasor between the body movement phasor and the light phasor.

[0012] Preferably, the step of continuously collecting light intensity data and body movement data of an individual in a natural living state through a wearable device specifically includes: using a wearable device integrated with a light sensor and a three-axis acceleration sensor to continuously collect light intensity data and three-axis acceleration data of an individual in a natural living state, the data collection time is not less than 24 hours, and the sampling frequencies of the light sensor and the acceleration sensor are both not lower than 1 Hz to capture finer activity and light changes.

[0013] Preferably, the preprocessing of the light intensity data includes:

[0014] Spectral weighting: Perform spectral weighting on the original light intensity data to more accurately reflect the biological effects of light, use the spectral sensitivity function for spectral weighting, and calculate the circadian rhythm stimulation value;

[0015] Temporal smoothing: Use the moving average filtering method to smooth the circadian rhythm stimulation value to remove high-frequency noise;

[0016] Binarization: Binarize the smoothed circadian rhythm stimulation value according to a preset threshold of the circadian rhythm stimulation value to obtain a light-dark pattern sequence.

[0017] Preferably, the preprocessing of the body movement data includes:

[0018] Calculate the body movement amplitude: Calculate the body movement amplitude based on the three-axis acceleration data;

[0019] Time smoothing: Use the moving average filtering method to smooth the body movement amplitude to remove high-frequency noise;

[0020] Normalization: Normalize the smoothed body movement amplitude data to eliminate the influence of differences in activity levels between individuals.

[0021] Preferably, the step of performing a discrete Fourier transform on the preprocessed light intensity data and body movement data, extracting the fundamental frequency component and representing it as a phasor, and calculating the difference phasor between the body movement phasor and the light phasor specifically includes: performing a discrete Fourier transform with a period of 24 hours on the preprocessed light intensity data and body movement data respectively, extracting the fundamental frequency component with a 24-hour period and representing it with a phasor, and calculating the difference phasor obtained by subtracting the light phasor from the body movement phasor.

[0022] Preferably, the calculation formula of the discrete Fourier transform DFT is:

[0023] X(k) = Σ[n=0 to N-1] x(n) * e^(-j * 2π * k * n / N)

[0024] Where: x(n) is the input signal, which can be light intensity data or body movement data; N is the signal length, corresponding to the number of sampling points in 24 hours; k is the frequency index; X(k) is the DFT coefficient; j is the imaginary unit.

[0025] Preferably, the phasor is represented as: Represent the fundamental frequency components of light and body movement as phasors L_phasor and A_phasor respectively:

[0026] L_phasor = |L_phasor| * e^(j * θ_L) = X_L(1)

[0027] A_phasor = |A_phasor| * e^(j * θ_A) = X_A(1)

[0028] Where: |L_phasor| and |A_phasor| are the amplitudes of the fundamental frequency components of light and body movement respectively, corresponding to the magnitudes of the DFT coefficients X_L(1) and X_A(1); θ_L and θ_A are the phases of the fundamental frequency components of light and body movement respectively, corresponding to the arguments of the DFT coefficients X_L(1) and X_A(1).

[0029] Preferably, the calculated difference phasor is: the difference phasor Δ_phasor obtained by subtracting the light phasor L_phasor from the body movement phasor A_phasor is: Δ_phasor = A_phasor - L_phasor.

[0030] Preferably, the step of evaluating the degree of circadian rhythm disorder of an individual according to the calculated difference phasor between the body movement phasor and the light phasor specifically includes: based on the calculated difference phasor, evaluating the degree of circadian rhythm disorder of the individual by calculating its modulus and argument. The modulus of the difference phasor reflects the comprehensive influence of the amplitude difference and phase difference between the light rhythm and the activity rhythm. The calculation formula of the light-activity rhythm alignment index is:

[0031] PM = |Δ_phasor| = sqrt(Re(Δ_phasor)^2 + Im(Δ_phasor)^2)

[0032] where: Re(Δ_phasor) represents the real part of Δ_phasor; Im(Δ_phasor) represents the imaginary part of Δ_phasor; the smaller the PM value, the higher the alignment degree of the light rhythm and the activity rhythm, and the more stable the circadian rhythm; the larger the PM value, the lower the alignment degree of the light rhythm and the activity rhythm, and the higher the degree of circadian rhythm disorder.

[0033] Preferably, the step of evaluating the degree of circadian rhythm disorder of an individual according to the calculated difference phasor between the body movement phasor and the light phasor specifically further includes: calculating the argument of the difference phasor as the light-activity rhythm phase difference index, and the calculation formula is:

[0034] PA = atan2(Im(Δ_phasor), Re(Δ_phasor))

[0035] where: atan2(y, x) is the four-quadrant arctangent function, which returns the arctangent value of y / x, and the result range is -π to π; the PA value reflects the phase difference between the light rhythm and the body movement rhythm, and the phase characteristics of the individual's circadian rhythm are evaluated according to the PA value to judge the degree of circadian rhythm disorder of the individual.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention uses wearable devices to collect light intensity data and body movement data, realizing portable and non-invasive continuous monitoring of circadian rhythm in the natural living state.

[0038] 2. The present invention adopts spectrally weighted light intensity data and a standardized data processing process, improving the accuracy and reliability of the evaluation results.

[0039] 3. The evaluation method of the present invention based on the alignment degree of light-activity rhythm can objectively and quantitatively evaluate the degree of circadian rhythm disorder of an individual. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] By reading the following detailed description of the non-limiting embodiments with reference to the accompanying drawings, other features, objects and advantages of the present invention will become more apparent:

[0041] Figure 1 It is a flow block diagram of the method for evaluating circadian rhythm disorder based on a wearable device according to the present invention;

[0042] Figure 2 It is a schematic diagram of the cosine fitting period of minute-level light-dark data;

[0043] Figure 3 It is a schematic diagram of the cosine fitting period of minute-level activity-rest data;

[0044] Figure 4 It is a polar coordinate diagram for measuring the difference phasor between the body movement phasor and the light phasor using the phase analysis method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0046] Specifically, the present invention provides a method, as Figure 1 shown, the method includes the following steps:

[0047] S1: Continuously collect the light intensity data and body movement data of an individual in a natural living state through a wearable device;

[0048] Specifically, a wearable device integrated with a light sensor and a three-axis acceleration sensor, such as ActiGraph GT3X+, a smart bracelet, etc., is used to continuously collect the light intensity data and three-axis acceleration data of an individual in a natural living state. The data collection time should be no less than 24 hours, and it is recommended to continuously collect for 7 days or longer to obtain more stable evaluation results. The sampling frequencies of both the light sensor and the acceleration sensor should not be lower than 1 Hz, and it is recommended to be set to 5 Hz or higher to capture finer activity and light changes.

[0049] S2: Preprocess the collected light intensity data and body movement data;

[0050] Specifically, the preprocessing of the light intensity data includes:

[0051] 1. Spectral weighting: Since lights of different wavelengths have different effects on the human circadian rhythm, it is necessary to perform spectral weighting on the original light intensity data to more accurately reflect the biological effects of light. In the present invention, the spectral sensitivity function S(λ) recommended by the International Commission on Illumination (CIE) is used for spectral weighting to calculate the circadian stimulus value (CS).

[0052] The calculation formula of CS is: CS = C_A * [1 - (1 / (1 + (L / L_0)^p))], where: C_A is the light intensity after spectral weighting, and the calculation formula is: C_A = ∫ P(λ) * S(λ) dλ. In the formula, P(λ) is the light intensity at wavelength λ; S(λ) is the spectral sensitivity function defined by CIE, which describes the relative influence of lights of different wavelengths on the circadian rhythm; L is the original light intensity; L_0 and p are empirical parameters used to adjust the shape of the CS curve; the value range of L_0 is usually between 10 and 1000 lux, and the value range of p is between 0.5 and 1.5.

[0053] 2. Temporal smoothing: The moving average filtering method is used to smooth the CS value to remove high-frequency noise. The mathematical expression of the moving average filtering is: CS_s(t) = (1 / n) * Σ[i=t-n+1 to t] CS(i), where: t is the time index, corresponding to a specific time point or sampling point in the discrete time series, representing the moment when the smoothed value CS_s(t) is currently calculated; CS_s(t) is the smoothed CS value; CS(i) is the original CS value; n is the moving average window size, and it is recommended to be set to the number of sampling points corresponding to 5 minutes.

[0054] 3. Binarization: The smoothed CS value CS_s(t) is binarized according to the preset threshold CS_th to obtain the light-dark mode sequence LDP(t). The binarization formula is:

[0055] LDP(t) = 1, if CS_s(t) ≥ CS_th

[0056] LDP(t) = 0, if CS_s(t)<CS_th

[0057] Among them, CS_th is the preset threshold, which can be set to 0.3, for example.

[0058] The preprocessing of the body movement data includes:

[0059] 1. Calculate the body movement amplitude: Calculate the body movement amplitude A(t) based on the three-axis acceleration data a_x(t), a_y(t), and a_z(t) at time t. Common methods include vector magnitude and digital integration. In this embodiment, the vector magnitude method (Vector Magnitude, VM) is adopted, and its calculation formula is:

[0060] A(t) = sqrt(a_x(t)^2 + a_y(t)^2 + a_z(t)^2)

[0061] Where t is the time index, corresponding to the sampling point in the discrete time series, representing the current moment when calculating the body movement amplitude A(t). A(t) is the body movement amplitude value at time t, indicating the intensity of the movement; a_x(t) is the acceleration value along the x-axis at time t; a_y(t) is the acceleration value along the y-axis at time t; a_z(t) is the acceleration value along the z-axis at time t.

[0062] 2. Temporal smoothing: Use the moving average filtering method to smooth the body movement amplitude A(t) to remove high-frequency noise. The mathematical expression of the moving average filtering is: A_s(t) = (1 / m) * Σ[i=t-m+1 to t] A(i), where: A_s(t) is the smoothed body movement amplitude; A(i) is the original body movement amplitude; m is the moving average window size.

[0063] 3. Normalization: Normalize the smoothed body movement amplitude data A_s(t) to eliminate the influence of individual activity level differences. In this embodiment, the maximum normalization method is adopted, and the body movement amplitude within each 24-hour period is divided by the maximum value of this period. The normalization formula is: A_n(t) = A_s(t) / max(A_s(t))_T, where: A_n(t) is the normalized body movement amplitude; max(A_s(t))_T is the maximum value of A_s(t) within the time period T (such as 24 hours).

[0064] S3: Perform discrete Fourier transform on the preprocessed light intensity data and body movement data, extract the fundamental frequency component and represent it as a phasor, and calculate the difference phasor between the body movement phasor and the light phasor;

[0065] Specifically, as Figure 2 and Figure 3As shown, for the pre - processed light intensity data (LDP(t)) and body movement data (A_n(t)), discrete Fourier transform (DFT) processing with a period of 24 hours is performed respectively to extract the fundamental frequency components corresponding to the 24 - hour period. This fundamental frequency component is used to represent the main change trend of the signal within the 24 - hour period. Further, it can be represented in the form of a phasor to quantify its amplitude and phase characteristics. The calculation formula of DFT is:

[0066] X(k)=\sum_{n = 0}^{N - 1}x(n)\cdot e^{(-j\cdot2\pi\cdot k\cdot n / N)}

[0067] Where: X(k) is the complex coefficient of the (k) - th frequency component after discrete Fourier transform, which contains the amplitude and phase information of this frequency component; x(n) represents the input discrete - time series signal, which can be the light intensity data (LDP(t)) or body movement data (A_n(t)), and this signal has been sampled at a unified time interval (such as 5 minutes); is the input signal, which can be LDP(t) or A_n(t); e^{(-j\cdot2\pi\cdot k\cdot n / N)} is the complex - exponential kernel function used to project the time - domain signal into the frequency domain; N is the signal length, corresponding to the total number of sampling points in 24 hours of the 24 - hour period; n is the discrete - time index of the time series, used to mark the position of the current sampling point within the 24 - hour period, and its value range is n = 0, 1, 2, ..., N - 1; k is the frequency index, representing the discrete frequency points in the frequency domain, used to mark the position of the extracted frequency component in the frequency domain, and its value range is k = 0, 1, 2, ..., N - 1; j is the imaginary unit.

[0068] Phasor representation: The fundamental frequency components of light and body movement are represented as phasors L_phasor and A_phasor respectively:

[0069] L_phasor = |L_phasor|\cdot e^{(j\cdot\theta_L)} = X_L(1)

[0070] A_phasor = |A_phasor|\cdot e^{(j\cdot\theta_A)} = X_A(1)

[0071] Where: |L_phasor| and |A_phasor| are the amplitudes of the fundamental frequency components of light and body movement respectively, corresponding to the magnitudes of the DFT coefficients X_L(1) and X_A(1); θ_L and θ_A are the phases of the fundamental frequency components of light and body movement respectively, corresponding to the arguments of the DFT coefficients X_L(1) and X_A(1).

[0072] Calculate the difference phasor: Calculate the difference phasor Δ_phasor of the body movement phasor A_phasor minus the light phasor L_phasor as: Δ_phasor = A_phasor - L_phasor.

[0073] S4. Evaluate the degree of circadian rhythm disorder of an individual according to the calculated difference phasor between the body movement phasor and the light phasor.

[0074] Specifically, as Figure 4 shown, based on the difference phasor Δ_phasor calculated in step S3, evaluate the degree of circadian rhythm disorder of an individual by calculating its magnitude and argument. The magnitude of the difference phasor Δ_phasor reflects the combined effects of the amplitude difference and phase difference between the light rhythm and the activity rhythm, and can be used as an index to evaluate the degree of circadian rhythm disorder.

[0075] The calculation formula for the light-activity rhythm alignment index (Phasor Magnitude, PM) is:

[0076] PM = |Δ_phasor| = sqrt(Re(Δ_phasor)^2 + Im(Δ_phasor)^2)

[0077] Where: Re(Δ_phasor) represents the real part of Δ_phasor; Im(Δ_phasor) represents the imaginary part of Δ_phasor. The smaller the PM value, the higher the alignment degree of the light rhythm and the activity rhythm, and the more stable the circadian rhythm; the larger the PM value, the lower the alignment degree of the light rhythm and the activity rhythm, and the higher the degree of circadian rhythm disorder.

[0078] Principle explanation: When the light rhythm and the activity rhythm are completely aligned, the phase difference between the two is 0 and the amplitudes are similar. At this time, the magnitude of Δ_phasor is close to 0. When the phase difference between the light rhythm and the activity rhythm is large or the amplitude difference is large, the magnitude of Δ_phasor increases.

[0079] Calculating the light-activity rhythm phase difference index (Phasor Angle, PA): In addition to the PM index, the argument of the difference phasor Δ_phasor can also be calculated as the light-activity rhythm phase difference index (Phasor Angle, PA). The calculation formula for PA is:

[0080] PA = atan2(Im(Δ_phasor), Re(Δ_phasor))

[0081] where: atan2(y, x) is the four-quadrant arctangent function, which returns the arctangent value of y / x, and the result range is from -π to π. The PA value reflects the phase difference between the light rhythm and the body movement rhythm. The phase characteristics of an individual's circadian rhythm can be evaluated based on the PA value, for example, to determine whether an individual is an "early bird type" or a "night owl type".

[0082] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. Without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A circadian rhythm disorder assessment method based on wearable devices, characterized in that: The method comprises the following steps: Wearable devices are used to continuously collect light intensity data and body movement data of individuals in their natural living conditions; Preprocessing the collected light intensity data and body motion data; Perform discrete Fourier transform on the preprocessed illumination intensity data and body motion data, extract the fundamental frequency component and express it as a phasor, and calculate the difference phasor between the body motion phasor and the illumination phasor; The degree of circadian rhythm disorder of an individual is evaluated based on the difference phasor between the calculated body movement phasor and the light phase, specifically including: based on the calculated difference phasor, the degree of circadian rhythm disorder of an individual is evaluated by calculating its modulus and argument, and the modulus of the difference phasor reflects the combined influence of the amplitude difference and phase difference between the light rhythm and the activity rhythm.

2. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The step of continuously collecting light intensity data and body motion data of an individual in a natural living state through a wearable device specifically includes: using a wearable device integrated with a light sensor and a three-axis acceleration sensor to continuously collect light intensity data and three-axis acceleration data of an individual in a natural living state, wherein the data collection time is not less than 24 hours, and the sampling frequencies of the light sensor and the acceleration sensor are not less than 1 Hz, so as to capture more detailed activities and light changes.

3. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The light intensity data preprocessing comprises: Spectral weighting: Spectral weighting is performed on the original light intensity data to more accurately reflect the biological effects of light. Spectral weighting is performed using a spectral sensitivity function to calculate the circadian rhythm stimulation value. Temporal smoothing: The circadian rhythm stimulus values ​​were smoothed using a moving average filtering method to remove high-frequency noise; Binarization: The smoothed circadian rhythm stimulus value is binarized according to the preset threshold of the circadian rhythm stimulus value to obtain a light-dark mode sequence.

4. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The body motion data preprocessing includes: Calculate body motion amplitude: Calculate body motion amplitude based on triaxial acceleration data; Temporal smoothing: The moving average filtering method is used to smooth the body motion amplitude to remove high-frequency noise; Normalization: The smoothed body motion amplitude data is normalized to eliminate the influence of differences in activity levels among individuals.

5. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The steps of performing discrete Fourier transform on the preprocessed light intensity data and body motion data, extracting the fundamental frequency component and expressing it as a phasor, and calculating the difference phasor between the body motion phasor and the light photography phasor specifically include: performing discrete Fourier transform on the preprocessed light intensity data and body motion data with a period of 24 hours, respectively, extracting the fundamental frequency component of the 24-hour period, expressing it as a phasor, and calculating the difference phasor of the body motion phasor minus the light photography phasor.

6. The circadian rhythm disorder assessment method based on a wearable device according to claim 5, characterized in that: The calculation formula of the discrete Fourier transform DFT is: X(k) = Σ[n=0 to N-1] x(n) * e^(-j * 2π * k * n / N) Where: x(n) is the input signal, which is light intensity data or body motion data; N is the signal length, corresponding to the number of sampling points in 24 hours; k is the frequency index; X(k) is the DFT coefficient; j is the imaginary unit.

7. The circadian rhythm disorder assessment method based on a wearable device according to claim 6, characterized in that: The phasor representation is: The fundamental frequency components of illumination and body motion are represented as phasors L_phasor and A_phasor respectively: L_phasor = |L_phasor| * e^(j * θ_L) = X_L(1) A_phasor = |A_phasor| * e^(j * θ_A) = X_A(1) Where: |L_phasor| and |A_phasor| are the amplitudes of the fundamental frequency components of illumination and body motion, respectively, corresponding to the modulus of the DFT coefficients X_L(1) and X_A(1); θ_L and θ_A are the phases of the fundamental frequency components of illumination and body motion, respectively, corresponding to the arguments of the DFT coefficients X_L(1) and X_A(1).

8. The circadian rhythm disorder assessment method based on a wearable device according to claim 7, characterized in that: The difference phasor of the calculated body motion phasor minus the light illumination phasor is: the difference phasor Δ_phasor of the calculated body motion phasor A_phasor minus the light illumination phasor L_phasor is: Δ_phasor=A_phasor-L_phasor.

9. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The calculation formula of the light-activity rhythm alignment index is: PM = |Δ_phasor| = sqrt(Re(Δ_phasor)^2 + Im(Δ_phasor)^2) Where: Re(Δ_phasor) represents the real part of Δ_phasor; Im(Δ_phasor) represents the imaginary part of Δ_phasor; the smaller the PM value, the higher the alignment between the light rhythm and the activity rhythm, and the more stable the circadian rhythm; the larger the PM value, the lower the alignment between the light rhythm and the activity rhythm, and the higher the degree of circadian rhythm disorder.

10. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The step of evaluating the degree of circadian rhythm disorder of an individual based on the calculated difference phasor between the body movement phasor and the light illumination phasor specifically includes: calculating the argument of the difference phasor as a light-activity rhythm phase difference index, and the calculation formula is: PA = atan2(Im(Δ_phasor), Re(Δ_phasor)) Among them: atan2(y, x) is a four-quadrant inverse tangent function, which returns the inverse tangent value of y / x, and the result range is -π to π; the PA value reflects the phase difference between the light rhythm and the body movement rhythm. The phase characteristics of the individual's circadian rhythm are evaluated based on the PA value to determine the degree of circadian rhythm disorder of the individual.

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