Method for determining a membership function, intended to be applied to estimate a stressed state of a person

Abstract

A method for determining a membership function, the membership functions allowing a stress level of a user to be determined on the basis of a physiological parameter measured on the user, the membership function varying, depending on the physiological parameter, between: a first value, representative of a rest state;and a second value, representative of a stressed state; the membership function taking into account a distribution function, which is defined beforehand, the distribution function being a continuous and monotonic function, the distribution function being applied to a normalized parameter established on the basis of a measured physiological parameter, the method comprising determining a normalization function, and optionally a standardization function, the normalization function and the optional standardization function being determined on the basis of physiological parameters measured on a plurality of test individuals, in various calibration periods.

Description

TECHNICAL FIELD

The technical field of the invention is the determination of a stress level of a user on the basis of at least one measurement of a physiological parameter performed on the user. The stress level is determined by implementing a membership function, stemming from fuzzy logic.

TECHNICAL FIELD

It is possible to determine a stress level of an individual on the basis of measurements of one or more physiological parameters of said individual. The physiological parameter may be a cardiac activity, for example measured via an electrocardiogram (ECG) or a simple determination of a cardiac frequency, or a muscular activity, measured via an electromyogram (EMG), or even a measurement of the electrical conductance of the skin. The publication Ollander “A comparison of wearable and stationary sensors for stress detection”, 2016 IEEE international conference on Systems, Man and Cybernetics (SMC), October 2016, describes how certain physiological parameters may be used to establish a stress indicator for an individual. The emergence of portable connected sensors intended to be worn by an individual has made it possible to take measurements of physiological parameters both simply and inexpensively. It is for example a question of specific sensors able to be fastened to a bracelet or integrated into watches or connected to smart phones. For example, the device Empatica E4 comprises various sensors allowing physiological parameters such as electrodermal activity, cardiac activity or temperature to be easily accessed. On the basis of the measured parameters, classification algorithms may be implemented, so as to determine whether the individual is in a stressed state or in a rest state. Certain classification algorithms are based on fuzzy logic. This type of algorithm is for example described in the publication Kumar M “Fuzzy techniques for subjective workload-score modeling under uncertainties”, IEEE transactions on systems, man and cybernetics—part B, Vol. 38, No. 6, December 2008. This type of algorithm requires a learning phase to be carried out, in which an individual is placed in a stressful situation, or in various stressful situations.

The publication De Santos Sierra A. et al. “Real-time stress detection by means of physiological signals”, Recent Application in Biometrics (2011 Jul. 27) describes an analogous approach, according to which, during training, the unit user must be successively in a stressed state and in a rest state. The fact that training, during which the user is placed in a stressful situation, is required is constraining. In addition, the reliability of such methods may be compromised by physiological variability from one user to the next.

Patent application US20200015729, describes a method allowing a stress level of a user to be determined on the basis of physiological parameters measured on the user. One advantageous aspect of this method is that it does not require the user to be placed in a stressed state during the calibration. In other words, the calibration is performed only when the user is at rest. This allows a particularly simple implementation of the method, using measuring equipment worn by the user. In addition, the calibration may be renewed periodically. In this patent application, the stress level of the user is determined using the principles of fuzzy logic: a membership function is established beforehand. The stress level is determined depending on the image, generated by the membership function, of the value of the measured physiological parameter. This method is also described in the publication Charbonnier, Sylvie et al. “A Multi-feature Fuzzy Index to Assess Stress Level from Bio-signals. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society”. IEEE Engineering in Medicine and Biology Society. Conference. 2018. 1086-1089.

The inventors believe that the method described in patent application US20200015729, and in the aforementioned publication, could be improved, so as to increase the sensitivity and the specificity of the detection of a stress level. This is the objective of the method described below.

SUMMARY OF THE INVENTION

A first subject of the invention is a method for determining a membership function, the membership functions allowing a stress level of a user to be determined on the basis of a physiological parameter measured on the user, during a measurement period, the membership function varying, depending on the physiological parameter, between:

• • a first value, representative of a rest state;
  • and a second value, representative of a stressed state;the membership function taking into account a distribution function, which is defined beforehand, the distribution function being a continuous and monotonic function, the distribution function being applied to a normalized parameter that is established on the basis of a measured physiological parameter, the method comprising:a) measuring values of a physiological parameter, in various calibration periods, on at least one test individual, each test individual being placed, in each calibration period, either in a rest state, or in a stressed state, or in an intermediate state, the intermediate state being a state in which the test individual is considered to be neither in a stressed state, nor in a rest state;b) optionally, applying a standardization function to the values measured at rest, in step a), to obtain standardized parameters, the standardization function being concave;c) on the basis:
  • of the values measured in a) and/or optionally standardized in b);
  • of the distribution function;determining a normalization function, the normalization function being intended to be applied to the measured, and optionally standardized, values of the physiological parameter, in order to establish normalized values, the determination of the normalization function comprising:
  • an optimization of a rest interval, representative of the values of the parameters measured when each test individual is at rest;
  • an optimization of a threshold distance, between the rest interval, and a stressed interval, representative of the values of the parameter measured when each test individual is in a stressed state;d) determining the membership function associated with the physiological parameter, the membership function corresponding to a composition of the distribution function, of the normalization function and of the optional standardization function.

The role of the normalization function is to distribute the measured values of the physiological parameters, which are optionally standardized, optimally so as to optimize, after application of the distribution function, the estimation of the stress level of the individual.

In a), the values of the physiological parameter that are measured while each test individual is in a stressed, rest or intermediate state may be grouped into a stressed class, a rest class and an intermediate class, respectively, so that, in step c),

• • the rest interval is optimized so as to optimize a classification, by the distribution function, of normalized values corresponding to the rest class and to a union of the intermediate class and of the stressed class, respectively;
  • the threshold distance is optimized so as to optimize a classification, by the distribution function of normalized values corresponding to the stressed class and to a union of the intermediate class and of the rest class, respectively.

Step c) may comprise determining:

• • an indicator of classification of the normalized values between the rest class and a union of the intermediate class and of the stressed class, the classification indicator being based on a proportion of false positives and of false negatives;
  • an indicator of classification of the normalized values between the stressed class and a union of the intermediate class and of the rest class, the classification indicator being based on a proportion of false positives and of false negatives

Step c may comprise

• • determining an optimal rest interval, by setting the threshold distance;
  • then determining the optimal threshold distance, depending on the optimal rest interval.

Step c) may comprise optimizing an extent of the rest interval.

Step c) may comprise optimizing a scale factor, the threshold distance being determined by multiplying the scale factor by the extent of the rest interval.

The normalization function may be an affine function; c) may comprise determining parameters of the affine function on the basis of the optimized rest interval and of the optimized threshold distance.

The standardization function may be of logarithmic or square-root type.

The method may comprise:

• • following a), computing a coefficient of asymmetry of a distribution of the values of the physiological parameter that are measured when each test individual is at rest, the coefficient of asymmetry quantifying the asymmetry of the distribution;
  • comparing the coefficient of asymmetry to a predefined threshold;
  • depending on the comparison, implementing b).

According to an embodiment:

• • steps a) to d) form a sequence of steps that is associated with the physiological parameter measured in a), and leading to the determination of a membership function associated with the physiological parameter;
  • the method further comprises various sequences of steps a) to d), respectively associated with various physiological parameters, so as to determine various membership functions, each membership function being respectively associated with one physiological parameter.

A second subject of the invention is determining a stress level of a user, depending on a physiological parameter measured on the user, during a measurement period, the value of which is liable to vary depending on the stress level of the user, the method comprising:

i) measuring a value of the physiological parameter during a measurement period;

ii) applying a membership function to the value of the physiological parameter measured in step i);

iii) estimating the stress level of the user depending on the value of the membership function computed in step ii);

wherein the membership function is a function determined using the method according to the first embodiment.

A third subject of the invention is a method for determining a stress level of a user, depending on a plurality of physiological parameters of the user, the value of each physiological parameter being liable to vary depending on the stress level of the user, the method comprising:

i) measuring values of various physiological parameters during a measurement period;

ii) for each value of the physiological parameter measured in i), applying a membership function associated with the physiological parameter;

iii) estimating the stress level by combining the values of the membership functions computed in step i);

wherein at least one membership function, associated with one physiological parameter, is a function determined using the method according to the first embodiment.

The method according to the second or third subject of the invention may comprise an initialization phase, containing various initialization periods, in which periods the user is considered to be in a rest state, the initialization phase comprising:

• • measuring, in each initialization period, one physiological parameter;
  • determining an indicator of the dispersion of the values measured in each initialization period;the method comprising, prior to steps i) to iii), adjusting the membership function established for the physiological parameter so as to take into account the dispersion indicator.

The invention will be better understood on reading the description of the exemplary embodiments, which are described, in the rest of the description, with reference to the figures listed below.

FIGURES

FIG. 1 shows a device allowing the invention to be implemented.

FIG. 2 illustrates the main steps of one embodiment of the invention.

FIG. 3A schematically shows the main steps of a calibration phase, allowing the membership function to be determined.

FIGS. 3B and 3C illustrate the case where the variation in the parameter with respect to the stress level is positive or negative, respectively.

FIG. 3D illustrates a hierarchy between parameters defining a normalization function.

FIG. 4A shows a distribution, considered to be symmetric, of values of one physiological parameter measured in a calibration phase, while each test individual is at rest.

FIG. 4B shows a distribution, considered to be asymmetric, of values of another physiological parameter measured in a calibration phase, while each test individual is at rest.

FIG. 4C shows a distribution obtained after a standardization function has been applied to the values, the distribution of which is shown in FIG. 4B. Comparison of FIGS. 4B and 4C illustrates the effect of the standardization function.

FIGS. 5A and 5B show optimization of the extent of the rest interval and optimization of a scale factor, respectively.

FIG. 6A shows application of the membership function taking into account a scale factor of zero.

FIG. 6B shows application of the membership function taking into account a scale factor that has been subject to an optimization.

FIG. 7 shows the steps of a method for determining a stress level of a user, using at least one membership function such as determined in the steps schematically shown in FIG. 3A.

DESCRIPTION OF PARTICULAR EMBODIMENTS

FIG. 1 shows a device 1 allowing the invention to be implemented. A sensor 2 is placed against the body of an individual, for example on his wrist. The sensor 2 is configured to measure the value x(t) of a physiological parameter of the individual during a measurement period t. The term period designates a time interval, for example a few seconds or a few minutes. The value of the physiological parameter in question may vary depending on a stress level of the individual. The physiological parameter x may be a parameter such as described in the publications cited with respect to the prior art, in which it is designated by the term “feature”. The physiological parameter may for example be:

• • A parameter related to a cardiac activity of the individual, for example cardiac frequency. Such a parameter may be measured by electrodes similar to the electrodes of an electrocardiogram (ECG), or by optical means, of the photoplethysmography (PPG) type.
  • It may also be a question of a mean of the cardiac frequency or of a dispersion parameter of the cardiac frequency such as its standard deviation, calculated during a preset time period. The physiological parameter may result from a frequency analysis of the cardiac activity such as an analysis of the inter-beat interval (IBI). In an ECG, this interval may lie between two R peaks of two consecutive cardiac pulses. The frequency analysis may be carried out in frequency ranges comprised between 0 Hz and 0.5 Hz, and more specifically, for example between 0 and 0.003 Hz (ultra-low frequencies), or between 0.003 and 0.04 Hz (very low frequencies), between 0.04 Hz and 0.15 Hz (low frequencies), or between 0.15 Hz and 0.5 Hz (high frequencies).
  • A parameter related to a muscular activity of the individual, this type of parameter being able to be measured by electromyography (EMG).
  • A parameter related to the electrodermal activity of the individual, representative of electrical-conduction or electrical-impedance properties of the skin (for example electrodermal impedance or conductance) and/or capacitive properties of the skin. It may be a question of a mean value of the conductance of the skin or of the mean absolute value of a derivative of the conductance of the skin. It may also be a question of a frequency, or of a mean duration or of a mean amplitude of electrodermal responses calculated during a preset time period. More generally, the parameter in question is a feature of an electrodermal response.
  • A skin temperature of the individual.
  • A measurement of a movement, for example an acceleration along at least one axis, such a measurement allowing a tremble or a physical activity liable to impact the physiology of the individual to be measured. The parameter in question may be a norm of the acceleration.
  • A measurement of a cortical activity, for example taken using electroencephalography (EEG) electrodes. It may for example be a question of a spectral power in a preset frequency band, calculated in a preset time period.

The physiological parameter may be a parameter representing the cardiac frequency (or heart rate). If HR_j is the heart rate measured at a time j, the physiological parameter x(t) at t may be:

$x\left(t\right)=\sqrt{\frac{1}{N_j}{\sum }_j{\left(H{R}_j-H{R}_{j-1}\right)}^2}$ where N_j is a number of heart rate measurements taken into account, and t−N_j≤j≤t. The number N_j is set so as to include the measurements of the hear rate during a sliding duration of a few seconds or a few tens of seconds, of a few minutes, for example 60 seconds.

If IBI_j is the inter-beat interval measured at a time j, the physiological parameter x(t) at t may be:

$x\left(t\right)=\sqrt{\frac{1}{N_j}{\sum }_j{\left({\mathrm{IBI}}_j-{\mathrm{IBI}}_{j-1}\right)}^2}$ where N_j is a number of inter-beat intervals taken into account, and t−N_j≤j≤t. The number N_j is set so as to include the measurements of the inter-beat interval during a sliding duration of a few seconds or a few tens of seconds, of a few minutes, for example 60 seconds.

The physiological parameters described in the two preceding paragraphs are particularly suitable for an implementation of the invention. They may be used alone or be combined, as described below. A physiological parameter is determined, for a measurement period, on the basis of signals measured during the measurement period. It may result from a statistical or frequency analysis of these signals.

The objective of the invention is to determine a stress level Sl(t) of a user in various measurement periods t. The user is a living individual, for example a human being or an animal.

The sensor 2 is connected to a microprocessor 4, the latter being connected to a memory 5 in which are stored instructions for implementing the method described below. The microprocessor 4 receives the measurements from the sensor 2, via a wired link or a wireless link. The microprocessor 4 may be borne by the user, it being placed nearby the sensor or incorporated into an associated device borne by the user, a nomadic object such as a smart phone for example. The microprocessor 4 may also be remote from the user.

The operation of the device 1 is described in patent application US20200015729, and more particularly in steps 130 and 140 and FIG. 3 of said patent application. Generally, the device implements a method that is repeated in various measurement periods t. The objective is to estimate a stress level Sl(t) corresponding to each measurement period. In each period, a physiological parameter x(t), such as defined above, is determined. The method comprises applying a membership function ƒ, such that the stress level Sl(t), during the measurement period, corresponds to the image, generated by the membership function, of the physiological parameter. In other words,Sl(t)=ƒ(x(t))

The membership function ƒ is intended to estimate a stress level Sl on the basis of the value of a physiological parameter x(t) measured in a measurement period t. The stress level Sl may for example vary between 0 and 1, 0 corresponding to a rest state and 1 corresponding to a stressed state of the user. According to the principles of fuzzy logic, the membership function ƒ may define intermediate levels, comprised between 0 and 1, and corresponding to a state in which the user is neither in a stressed state, nor at rest. The membership function ƒ is preferably continuous in an input space E defined by the values capable of being taken by the measured physiological parameter. The input space E may for example be the set of all real positive numbers. Thus, ƒ:=⁺→[0,1] and ƒ(x(t))=Sl(t). The membership function ƒ may for example be piecewise linear, or take the form of a hyperbolic tangent or sigmoid.

FIG. 2 shows an example in which the membership function ƒ is of sigmoid type. A sigmoid function is of type:

$\begin{array}{cc}f\left(x\right)=\frac{1}{1+e^x}& \left(3\right)\end{array}$

When the values of the parameter x(t) are comprised in a rest interval X_r, the stress level Sl(t) is close to 0 or equal to 0. The user is considered to be at rest. When the values of the parameter x(t) are distant, by a distance smaller than a threshold distance d, from the rest interval R, the user is considered to be in an intermediate state, between the rest state and a stressed state: 0<Sl(t)<1. When the values of the parameter x(t) are distant, by a distance larger than a threshold distance d, from the rest interval, the user is considered to be in a stressed state. Sl(t) is then equal to 1 or close to 1.

The example given with reference to FIG. 2 is valid when the membership function ƒ is increasing, i.e. the stress level increases as the measured value of the physiological parameter increases. In certain particular cases, for example when the parameter in question is the resistance of the skin, the membership function ƒ is decreasing: the stress level increases as the value of the measured parameter decreases.

According to one possibility, the method described above may be applied by measuring, in each measurement period t, various physiological parameters x_i, the index i identifying the parameter in question, with 1≤i≤I, I designating the number of physiological parameters in question. One membership function ƒ_i is then defined for each physiological parameter x_i. The membership functions ƒ_i, ƒ_i+1 respectively associated with two different parameters x_i, x_i+1 may be established independently of each other. It is however preferable, for each membership function, that the rest state and the stressed state respectively correspond to the same levels, for example 0 for the rest state and 1 for the stressed state. A definition of I membership functions ƒ₁ . . . ƒ_I, respectively associated with the I measured physiological parameters x₁ . . . x_I, is thus obtained.

After each membership function ƒ_i has been defined, the method comprises, in each measurement period t, implementing it, for each measured parameter x_i(t), so as to determine a stress level Sl_i(t) associated with each parameter x_i(t), using the expression Sl_i(t)=ƒ_i(x_i(t)). A definition of I stress levels Sl₁(t) . . . Sl_I(t), respectively associated with the I physiological parameters x₁ . . . x_I in question, is thus obtained. The various stress levels Sl₁(t) . . . Sl_I(t), respectively associated with each parameter x_i(t), are combined, so as to determine an overall, or multiparameter, stress level Sl(t), according to the principles of fuzzy logic. The combination may be a computation of a mean value or of a median value. It may also be a question of a weighted mean, in which each stress level Sl_i(t) is assigned a weighting factor λ_i depending on the importance that it is desired to attribute to the physiological parameter x_i relative to the other parameters in question. The various stress levels Sl₁(t) . . . Sl_I(t) may be combined by applying predefined inference rules. On the basis of the multiparameter stress level Sl(t), it is possible to determine whether the user is in a stressed state.

The inventors have considered it necessary for the membership function to be refined, so as to process the measured values of each physiological parameter. This is particularly relevant when various physiological parameters x_i are addressed in each measurement period t, so as to obtain a multiparameter stress level. The objective of the processing is to improve the performance of the determination of the stress level, and notably sensitivity (i.e. a minimum proportion of false negatives) and specificity (i.e. a minimum proportion of false positives).

The processing of the measured values consists in applying a membership function to the value of a physiological parameter measured in each measurement period. The invention differs from the prior art in that the membership function is a composition of various functions, defined in a calibration phase that is described below. More precisely, for each physiological parameter x_i in question, a membership function ƒ_i is defined in the calibration phase. Each membership function ƒ_i associated with one physiological parameter x_i corresponds to a composition:

• • optionally of a standardization function T_i. The standardization function is generally determined in the calibration phase. The standardization function is defined such that when a user is at rest, the values x_i(t) of the physiological parameter x_i are distributed in a distribution that approaches a symmetric distribution, a Gaussian distribution for example. The application of the standardization function to a measured value x_i(t) of the physiological parameter x_i leads to the obtainment of a standardized value T_i(x_i(t)). When the method is based on a measurement of various physiological parameters x₁, x₂ during a measurement period t, the standardization functions T₁, T₂ respectively associated with the parameters x₁, x₂, are generally different.
  • a normalization function A_i, established during the calibration phase. The normalization function A_i allows the measured values x_i(t) of the physiological parameter, or of the standardized values T_i(x_i(t)), to be distributed optimally with respect to specificity and sensitivity. The application of the normalization function A_i to a measured value x_i(t) of the physiological parameter x_i, or to a standardized value T_i(x_i(t)), allows a normalized value A_i(x_i(t)) or A_i (T_i(x_i(t))) to be obtained. When the method is based on a measurement of various physiological parameters x₁, x₂ during a measurement period t, the normalization functions A₁, A₂ respectively associated with the parameters x₁, x₂, are generally different.
  • a predefined distribution function S_i. The distribution function S_i, applied to a normalized value A_i(x_i(t)) or A_i (T_i(x_i(t))), allows a stress level Sl_i(t) associated with the physiological parameter x_i to be estimated. The distribution function is preferably monotonic, and preferably continuous. The distribution function S_i may for example be piecewise linear, or take the form of a hyperbolic tangent or sigmoid. When the method is based on a measurement of various physiological parameters x₁, x₂ during a measurement period t, the distribution functions S₁, S₂ respectively associated with the parameters x₁, x₂, may be different or identical.

Thus, the method implements, for each physiological parameter x_i in question, a membership function ƒ_i formed by a composition of the optional standardization function T_i, with the normalization function A_i and the distribution function S_i. Thus,ƒ_i=S_i∘A_i∘T_i  (4)orƒ_i=S_i∘A_i  (5)∘ designating the function-composition operator.

The normalization function A_i allows, for a given user, the measured physiological parameters to be projected into a space comprising the antecedents of the distribution function. The normalization function A_i is optimized so as to optimize, after the distribution function S_i has been applied, the estimation of the stress level in particular with respect to specificity and sensitivity.

The main steps of the calibration phase, allowing the standardization and normalization functions to be defined taking into account a predefined distribution function, will now be described with reference to FIG. 3A. The calibration phase is performed on a test population, formed from at least one test individual, and preferably from a plurality of test individuals. In this phase, each test individual of the test population follows a protocol, so as to be able to be considered to be either in a rest state, or in a stressed state, or in an intermediate state, i.e. neither in a rest state nor a stressed state. The intermediate state may for example correspond to a state in which the test individual is active, without being stressed: he is for example performing a physical and/or intellectual activity that is not considered to be stressful. Each test individual is considered to be representative of the user intended to use the device. Protocols known to those skilled in the art allow an individual to be considered to be in a stressed state. It is for example a protocol usually designated by the acronym “MST” (Mental Stress Test), in which an individual must count backwards in increments starting from a preset number. The individual is filmed, and the trial is carried out in the presence of two observers. For example, the individual must count backwards from 1022 in increments of 13. It may also be a question of a protocol usually designated by the acronym “TSST” (Trier Social Stress Test), in which the individual is filmed during a mock recruitment interview in front of two observers. In each protocol, one or more physiological parameters are measured, during various periods. During the calibration phase, the periods t_c in which physiological parameters are measured are called calibration periods.

Steps 100 to 140 described below relate to one physiological parameter x_i. When the method implements various physiological parameters, steps 100 to 140 are performed in parallel, for each addressed physiological parameter.

Step 100: measuring values x_i(t_c) of the physiological parameter x_i during a plurality of calibration periods t_c, and for one or more test individuals. The measurements are taken while each test individual is in a state considered to be known. The population and number of measurements are dimensioned so as to obtain a sample that is statistically representative of each parameter, and in each state (stress state, rest state, intermediate state). Typically, at least 30 measured values are obtained for each parameter and in each state.

Step 110: Study of the symmetry of the values measured in step 100, while each test individual is at rest.

For the physiological parameter x_i, a distribution D_i of the values x_i(t_c), measured while each test individual is at rest, is determined. A coefficient of symmetry k_i is computed for the physiological parameters. The coefficient of symmetry k_i quantifies the symmetry of the distribution D_i. For example, the coefficient of symmetry k_i is such that:

$\begin{array}{cc}{k}_i=\frac{{E\left[x_i\left(t_c\right)-{\mu }_i\right]}^3}{{\sigma }_i^3}& \left(6\right)\end{array}$ where μ_i and σ_i are the mean and standard deviation of the distribution D_i, respectively. E is the expectation operator. When k_i is positive, the distribution D_i is centered on the lowest values of the parameter x_i. When k_i is negative, the distribution D_i is centered on the highest values of the physiological parameter x_i. The lower the absolute value of k_i, the more the distribution D_i is considered to be symmetric.

Following step 110, a coefficient of symmetry k_i corresponding to the distribution D_i of the values x_i(t_c) of the physiological parameter x_i that were measured in the calibration phase, when each test individual is at rest, is obtained.

Step 120: Determining a Standardization Function

This step is optional. It is preferably implemented when, following step 110, the absolute value |k_i| of the coefficient of symmetry k_i of a distribution D_i exceeds a certain threshold k_th: the distribution D_i is then considered to be asymmetric. A standardization function T_i is applied to each value x_i (t_c), such that the distribution D′_i of the data T_i(x_i(t_c)), obtained following the application of the standardization function, has a coefficient of symmetry k′_i the absolute value of which is lower than the absolute value of k_i: |k′_i|<|k_i|.

Preferably, |k′_i|<k_th, such that the distribution D′_i is considered to be symmetric.

The standardization function T_i is preferably a concave function. It may for example be a logarithmic function, for example a function that is logarithmic to the base 10, or a square-root function. Whatever the (logarithmic, square-root or other) type of standardization function in question, the standardization function is parameterized to minimize the absolute value of the coefficient of symmetry of the distribution D′_i. Thus,

$\begin{array}{cc}{T}_i=\underset{T_i}{\arg \min }\left(❘k_i^{\prime }❘\right)& \left(7\right)\end{array}$

FIG. 4A shows an experimentally obtained statistical distribution of a mean value of the heart rate of 20 test individuals in rest state. The coefficient of symmetry of such a distribution is close to 0.

FIG. 4B shows an experimentally obtained statistical distribution of a spectral power of the inter-beat intervals of 20 test individuals in rest state. The coefficient of symmetry of such a distribution is close to 2. The distribution is highly asymmetric. A logarithmic function to the base 10 has been applied to the values forming the statistical distribution shown in FIG. 4B. FIG. 4C shows the distribution of values thus standardized. The standardization function, in the present case the logarithmic function to the base 10, allows a symmetric distribution of standardized data to be obtained.

Following step 120, measured values, or measured then standardized values, the distribution of which is symmetric, are obtained. This facilitates implementation of step 130. Specifically, following the application of the standardization function, the data corresponding to the stress state are grouped, symmetrically, about a central value, the occurrence of which is maximal, as shown in FIGS. 3B and 3C.

Step 130: Determining a Normalization Function

The objective of the normalization function is to process the values such that the image, generated by the distribution function S_i, of the values processed by the normalization function A_i, leads to an optimal separation between the various states, and in particular between stressed states and unstressed states. This step assumes that the distribution function S_i is known. As indicated above, the distribution function may be a sigmoid function. According to this example:

• • when, for the parameter x_i in question, a standardization function T_i was determined in step 120, the normalization function A_i is applied to the standardized values, such that:

$\begin{array}{cc}{S}_i\left(A_i\left(T_i\left(x_i\left(t_c\right)\right)\right)\right)=\frac{1}{1+e\left(A_i\left(T_i\left(x_i\left(t_c\right)\right)\right)\right)}& \left(10\right)\end{array}$

• • when, for the parameter x_i in question, a standardization function was not determined in step 120, the normalization function A_i is applied to the measured values, such that:

$\begin{array}{cc}{S}_i\left(A_i\left(x_i\left(t_c\right)\right)\right)=\frac{1}{1+e^{A_i\left(x_i\left(t_c\right)\right)}}& \left(11\right)\end{array}$ In the rest of this example, the input variable processed by the normalization function A_i, which results from step 120, is designated by X_i(t_c):

• • in the presence of standardization: X_i(t_c)=T_i(x_i(t_c))
  • without standardization: X_i(t_c)=x_i(t_c).

FIG. 3B shows a distribution function S_i, which is applied to values A_i(X_i(t_c)), which are represented on the x-axis. The left y-axis corresponds to S_i (A_i(X_i(t_c))), this, according to expression (4), corresponding to the computed stress index Sl_i(t_c). In this step, the processed data are generated in the calibration phase. Thus, in each calibration period t_c, the stress index Sl_i(t_c) is known. The example of FIG. 3B corresponds to a positive variation in the parameter with respect to the stress level: this means that a stressed state corresponds to an increase in the value of the parameter.

The form of the normalization function A_i is predefined. The latter is parameterized by parameters θ_i. Step 130 aims to determine the parameters θ_i of the normalization function A_i. In this example, the normalization function A_i is a function A_i(X_i)=a_iX_i+b_i (12) of affine type. The parameters of the function are θ_i=(a_i,b_i).

A first property of the normalization function A_i is that the image, generated by the latter, of the input data X_i(t_c), resulting from the calibration phase and corresponding to a rest state, and grouped in a rest interval R_i, form antecedents of a rest state Sl_i,rest, after the distribution function S_i has been applied. Thus:X_i(t_c)∈R_i⇔S_i(A_i(X_i(t_c)))≅Sl_i,rest  (12′)

The term ≅ signifies equal to a term to within an uncertainty ε. The uncertainty term c is defined beforehand.

The term Sl_i,rest corresponds to the value of the stress indicator corresponding to a rest state. In the example shown in FIG. 3B, the distribution function A_i is such that the stress indicator takes a minimum value Sl_i,rest when the user is at rest, and a maximum value Sl_i,stress when the user is in a stressed state. In the example shown in FIG. 3B, Sl_i,rest=0 and Sl_i,stress=1. Thus, expression (12) becomes:X_i(t_c)εR_i⇔S_i(A_i(X_i(t_c)))<ε  (13)Alternatively, the variation with respect to stress is negative: this meaning that a stressed state corresponds to a decrease in the measured parameter. Such an alternative is shown in FIG. 3C.

The input data X_i,rest(t_c), which correspond to a rest state, are comprised between a lower bound m_i and an upper bound M_i, defining the interval R_i. During the normalization, the normalized values A_i(X_i(t_c)) are such that:A_i(m_i)≤A_i(X_i,rest(t_c))<A_i(M_i)  (13′)

Because the distribution of the input data X_i(t_c) is considered to be symmetric, a lower limit m_i and an upper limit M_i may be defined such that:

$m_i={\mu }_i-\frac{z_i}{2}{\sigma }_i\mathrm{and}{M}_i={\mu }_i+\frac{z_i}{2}{\sigma }_i$ where μ_i and σ_i are the mean and standard deviation of the distribution of the input data X_i,rest(t_c) corresponding to the measurements taken, at rest, in the calibration phase, respectively. The parameter z_i is a standardized amplitude. The amplitude Δ_i of the rest interval R_i is equal to the product σ_iz_i. Thus, the rest interval R_i is dependent on the parameter z_i, the value of σ_i being obtained experimentally, from the input data X_i,rest(t_c). The value of z_i allows the rest interval R_i and its amplitude Δ_i, which is such that Δ_i=M_i−m_i, to be determined.

In FIGS. 3B and 3C, the distribution E_i formed by the normalized values A_i(X_i,rest(t_c)) has been illustrated (right y-axis). This distribution is bounded by A_i(m_i) and by A_i(M_i), which respectively correspond to the respective images of m_i and M_i generated by the normalization function A_i. These bounds define a reference interval A_i(R_i), the latter corresponding to the image, generated by the function A_i, of the rest interval R_i.

A second property of the normalization function A_i is that the image, generated by the latter, of the input data X_i(t_c) resulting from the calibration phase and distant from the rest interval R_i by a distance d_i larger than or equal to α_iΔ_i, form antecedents of a stressed state Sl_i,stress, after the distribution function S_i has been applied. The value β_iΔ_i is a threshold distance d_i. α_i is a positive parameter when the variation in the values of the parameter with respect to stress is positive (see FIG. 3B). α_i is a negative parameter when the variation in the values of the parameter with respect to stress is negative (see FIG. 3C).

Thus:X_i(t_c)>M_i+α_iΔ_i⇔S_i(A_i(X_i(t_c)))≅Sl_i,stress  (14).

Considering a positive variation with respect to stress, and a value Sl_i,stress equal to 1, expression (14) becomes:X_i(t_c)>M_i+α_iΔ_i⇔S_i(A_i(X_i(t_c)))>1−ε  (15)

The value of ε is low with respect to 1. It may for example be equal to 0.1. In FIGS. 3B and 3C, the distribution F_i formed by the normalized values A_i(X_i,stress(t_c)) has been illustrated. In FIG. 3B (α_i>0), the distribution F_i is bounded by a bound the value of which is A_i(M_i+α_iΔ_i), this corresponding to the image, generated by the function A_i, of M_i+α_iΔ_i. In FIG. 3C (α_i<0), the distribution F_i is bounded by a bound the value of which is A_i(m_i+α_iΔ_i), this corresponding to the image, generated by the function A_i, of m_i+α_iΔ_i.

The distribution function S_i is for example of sigmoid type, such as defined with reference to the expressions (10) and (11). It may be deduced that:

$\begin{array}{cc}{A}_i\left(M_i\right)=S_i^{-1}\left(\epsilon \right)=\ln \left(\frac{\epsilon }{1-\epsilon }\right)& \left(16\right)\end{array}$ andA_i(M_i)+α_i[A_i(M_i)−A_i(m_i)]=S_i⁻¹(1−ε)=S_i⁻¹(ε)=−A_i(M_i)  (17)

Assuming A_i to be an affine transformation (see expression (12)), the following is obtained:

$\begin{array}{cc}{a}_i=\frac{2\ln \left(\frac{1-\epsilon }{\epsilon }\right)}{{\alpha }_i\left(M_i-m_i\right)}& \left(18\right)\end{array}$ and

$\begin{array}{cc}{b}_i=\ln \left(\frac{\epsilon }{1-\epsilon }\right)\left[1+2\frac{M_i}{{\alpha }_i\left(M_i-m_i\right)}\right]& \left(19\right)\end{array}$

When the variation in the physiological parameter is negative between the rest state and the stressed state, as shown in FIG. 3C, then α_i<0, and it is possible to write:

$\begin{array}{cc}{A}_i\left(m_i\right)=S_i^{-1}\left(\epsilon \right)=\ln \left(\frac{\epsilon }{1-\epsilon }\right)& \left(20\right)\end{array}$ andA_i(m_i)+α_i[A_i(m_i)−A_i(m_i)]=S_i⁻¹(1−ε)=S_i⁻¹(ε)=−A_i(m_i)  (21)

It then becomes:

$\begin{array}{cc}{a}_i=\frac{2\ln \left(\frac{1-\epsilon }{\epsilon }\right)}{{\alpha }_i\left(M_i-m_i\right)}& \left(22\right)\end{array}$ and

$\begin{array}{cc}{b}_i=\ln \left(\frac{\epsilon }{1-\epsilon }\right)\left[1+2\frac{m_i}{{\alpha }_i\left(M_i-m_i\right)}\right]& \left(23\right)\end{array}$

The normalization function A_i is dependent on the parameter α_i and on the parameter z_i.

The parameter z_i is representative of the rest interval R_i, the image of which, generated by the distribution function S_i composed with the normalization function A_i, corresponds to the rest level Sl_i,rest. More precisely, z_i is representative of the width of the rest interval. The parameter α_i dimensions the threshold distance d_i, between the rest interval and a stressed interval, the image of which, generated by the distribution function S_i composed with the normalization function A_i, is equal to Sl_i,stress to within the uncertainty term ε.

The optimization of the parameters z_i and α_i is carried out successively, starting with the optimization of the parameter z_i. It is performed using the fact that, in the calibration phase, in each calibration period t_c, each state Sl_i(t_c) of the test individual is known. The optimization consists in setting one parameter (for example z_i), and making the other parameter (for example α_i) vary while analyzing the classification performance given by the distribution function ƒ_i ((X_i(t_c))=S_i∘A_i(X₁(t_c). The normalization function A_i is parameterized by the parameters a_i and b_i such as defined by expressions (22) and (23), the latter themselves being dependent on the parameters α_i and z_i, the latter having a hyper-parameter function. FIG. 3D illustrates the hierarchical structure of the dependency between z_i and α_i on the one hand and a_i and b_i on the other hand. The optimization of the parameters z_i and α_i allows optimized parameters z_i,opt and α_i,opt to be obtained. The latter are used to define the parameters a_i and b_i of the normalization function A_i.

By classification performance, what is for example meant is sensitivity, which corresponds to 1—the rate of false negatives, and specificity, which corresponds to 1 minus the rate of false positives. Other statistical indicators, for example a “Cohen's d” test, which is known to those skilled in the art, may also be employed. Cohen's d corresponds to an inter-class distance, in the present case a distance between the distributions of the values respectively considered to be representative of a stressed state and of an unstressed state. The unstressed state is a rest state or an intermediate state.

For example, if two statistical distributions are considered that have mean, standard-deviation and population values denoted μ₁, σ_i, n₁ and μ₂, σ₂ and n₂, respectively, the Cohen's d may be such that:

$d=\frac{{\mu }_1-{\mu }_2}{\sqrt{\frac{\left({\mu }_1-1\right)s_1^2+\left({\mu }_2-1\right)s_2^2}{n_1+n_2-2}}}$

The inventors have established a classification-performance indicator c, such that:c=ρd where:

• • ρ is a likelihood ratio, such that

$\rho =\frac{1-\beta }{\gamma }$ where β and γ designate sensitivity and specificity, respectively;

• • d corresponds to the aforementioned Cohen's d.

In order to determine the parameter z_i that is optimal in terms of classification performance, the parameter α_i is set to an absolute value equal to 0.01. Next, z_i is varied in increments of 0.25 between two predefined bounds, between 1 and 5 for example. The optimal value of z_i, denoted z_i,opt, is such that:

$\begin{array}{cc}{z}_{i,\mathrm{opt}}=\underset{z_i}{\arg \max }\left(c_{{\alpha }_i\approx 0}\right)& \left(25\right)\end{array}$

The notation c_α1≈0 corresponds to the fact that the performance indicator is computed considering α_i to be close to 0, and for example equal to 0.01 (when α_i>0) or −0.01 (when α_i<0). During the determination of z_i,opt, classification performance is determined on the basis of two classes: the rest state and the “non-rest” state, the latter containing the intermediate states and the stressed state. It is a question of evaluating the ability of the distribution function to correctly classify the rest state.

After z_i,opt has been determined, the optimal parameter α_i is determined setting the parameter z_i such that z_i=z_i,opt. Next, α_i is varied in increments of 0.1 between two predefined bounds, between 0 and 3 for example when α_i>0 or between −3 and 0 when α_i<0. The optimal value of α_i, which is denoted α_i,opt, is such that:

$\begin{array}{cc}{\alpha }_{i,\mathrm{opt}}=\underset{{\alpha }_i}{\arg \max }\left(c_{z_i=z_{i,\mathrm{opt}}}\right)& \left(26\right)\end{array}$

The notation c_zi=zi,opt corresponds to the fact that the performance indicator is computed considering z_i=z_i,opt. During the determination of α_i,opt, classification performance is determined on the basis of two classes: the stressed state and the “unstressed” state, the latter containing the intermediate states and the rest state. It is a question of evaluating the ability of the distribution function to correctly classify the stressed state.

FIG. 5A illustrates a variation, as a function of z_i (x-axis) of the likelihood ratio ρ, of Cohen's d and of the classification-performance indicator c. In this example, the studied parameter was the variance of heart rate. FIG. 5B illustrates a variation, as a function of α_i (x-axis) of the likelihood ratio ρ, of Cohen's d and of the classification-performance indicator c. In this example, the studied parameter was also the variance of heart rate. FIGS. 5A and 5B illustrate the ability to determine z_i,opt and α_i,opt, respectively.

According to one variant, the parameters α_i,opt and z_i,opt may be determined simultaneously. According to another variant, α_i,opt may be determined before z_i,opt.

The determination of z_i,opt and α_i,opt described above corresponds to one example, based on a determination of specificity, sensitivity and Cohen's d. More generally, the parameters z_i,opt and α_i,opt may be determined on the basis of classification performance determined in a training phase, in which the state of each test individual is known.

FIG. 6A shows a distribution function S_i composed with a normalization function A_i established with z_i=z_i,opt and α_i=0. FIG. 6B shows a distribution function S_i composed with a normalization function A_i established with z_i=z_i,opt and α_i=α_i,opt. In FIGS. 6A and 6B, the parameters established while the test individual is in a rest state, stressed state and an intermediate state are identified by circles O, crosses + and stars *, respectively. In FIG. 6A, classification errors may be seen (for example false positives: circles O the y-coordinate of which corresponds to a stress level Sl_i corresponding to the stressed state). This curve also returns false negatives: stars * the y-coordinate of which corresponds to a stress level Sl_i corresponding to the rest state. FIG. 6B contains fewer classification errors.

Following step 130, the normalization function A_i corresponding to the physiological parameter x_i, is obtained.

Step 140: Determining the Membership Function ƒ_i

In step 140, the membership function is determined according to expressions (4) or (5), using:

• • the optional standardization function T_i resulting from step 120;
  • the distribution function S_i taken into account in step 130;
  • the normalization function A_i defined following step 130.

Steps 100 to 140 may be respectively implemented for various physiological parameters x_i. As many distribution functions are obtained as there are physiological parameters in question. As indicated above, the distribution functions may be combined, in particular in the form of a weighted summation, so as to establish a stress level.

FIG. 7 shows the main steps of implementation of a method for determining a stressed state, by implementing a membership function as determined following step 140.

In step 200, one or more physiological parameters x_i(t) are measured in a measurement period t. Each physiological parameter x_i measured in each measurement period corresponds to one physiological parameter addressed during the calibration.

In step 210, the membership function ƒ_i, which is defined beforehand for each physiological parameter, is applied to the value x_i(t) of the physiological parameter in the measurement period, so as to determine a stress level Sl_i(t)=ƒ_i(x_i(t)) relating to the physiological parameter x_i.

In step 220, the stress level Sl(t) of the user, during the measurement period t, is determined depending on each stress level Sl_i(t) established, for each addressed physiological parameter, during step 210.

In step 230, the method is reiterated for another measurement period or it is decided to exit the algorithm.

According to one variant, forming the subject matter of steps 160 to 190, the method comprises an initialization phase, in which the user is exclusively in a rest state, or considered in a rest state. The initialization consists in taking measurements of at least one physiological parameter x_i(t_init), so as to estimate a range of variation. The measurements are taken in various initialization periods t_init. A dispersion indicator is determined, characterizing the range of variation of the parameter x_i during the various initialization periods t_init. The dispersion indicator may for example be a standard deviation σ_i,init.

The dispersion indicator σ_i,init thus determined is used to determine the bounds M_i and m_i of the rest interval on the basis of z_i. The following expressions are then used:

$\begin{array}{cc}{m}_i={\mu }_i-\frac{z_{i,\mathrm{opt}}}{2}{\sigma }_{i,\mathrm{init}}& \left(27\right)\end{array}$ and

$\begin{array}{cc}{M}_i={\mu }_i+\frac{z_{i,\mathrm{opt}}}{2}{\sigma }_{i,\mathrm{init}}& \left(28\right)\end{array}$

Thus, according to this variant, the initialization is performed only with values of the parameter that are measured during the calibration, while the user is considered to be in a rest state. The initialization does not require measurements of a physiological parameter to be taken while the user is in a stressed state. One advantage of the method is that the initialization is faster and simpler to carry out. Another advantage is that the initialization may be repeated periodically, in order to take into account potential physiological user variability. Since the initialization is simple to carry out, it may be repeated frequently.

Steps 160 to 190 are then as follows:

Step 160: taking an initialization measurement x_i(t_init)

Step 170: reiterating step 160 or passing to step 180

Step 180: computing the dispersion indicator σ_i,init

Step 190: using the dispersion indicator σ_i,init to adjust the membership function ƒ_i used in step 210, by combining expressions (27) and (28) with expressions (18) and (19) or (22) and (23).

According to another variant, the initialization is not performed and the membership function, relating to one parameter, or even to each parameter in question, is established without adjustment of the membership function resulting from the calibration.

The invention will possibly be employed to track the stress level of users. It may for example be a question of tracking stress level in a professional environment, or of tracking stress level of users subject to anxiety in particular situations, in a means of transportation for example. It may also be applied to track the stress level of a sportsperson. Thus, the stress level can be a level of anxiety or effort, whether physical or mental, or a level of excitement. The stress level can correspond to a mental load of the user: joy, anger, anxiety, fatigue, feeling of lack. The invention can also be applied to the determination of a level of wakefulness or sleepiness.

Claims

1. A method for determining a membership function, the membership function allowing a stress level of a user to be determined on the basis of a physiological parameter measured on the user, during a measurement period, using a sensor, the membership function varying, depending on the physiological parameter, between: a first value, representative of a rest state; anda second value, representative of a stressed state;the membership function taking into account a distribution function, which is defined beforehand, the distribution function being a continuous and monotonic function, the distribution function being applied to a normalized parameter that is established on the basis of a measured physiological parameter, the method comprising:a) measuring values of a physiological parameter with the sensor in various calibration periods on at least one test individual, each test individual being placed, in each calibration period, either in a rest state or in a stressed state, or in an intermediate state, the intermediate state being a state in which the test individual is considered to be neither in a stressed state, nor in a rest state;b) on the basis: of the values of the physiological parameter resulting from a); andof the distribution function;determining, using processing circuitry, a normalization function, the normalization function being configured to be applied to the values of the physiological parameter resulting from a), in order to establish normalized values, the determination of the normalization function comprising:an optimization of a rest interval, representative of the values of the parameters measured when each test individual is at rest; andan optimization of a threshold distance, between the rest interval, and a stressed interval, representative of the values of the parameter measured when each test individual is in a stressed state; andc) determining, using the processing circuitry, the membership function associated with the physiological parameter, the membership function comprising a composition of the distribution function and the normalization function.

2. The method of claim 1, wherein, in a), the values of the physiological parameter that are measured while each test individual is in a stressed, rest or intermediate state are grouped into a stressed class, a rest class and an intermediate class, respectively, and wherein, in step b), the rest interval is optimized so as to optimize a classification, by the distribution function, of normalized values corresponding to the rest class and to a union of the intermediate class and of the stressed class, respectively; andthe threshold distance is optimized so as to optimize a classification, by the distribution function of normalized values corresponding to the stressed class and to a union of the intermediate class and of the rest class, respectively.

3. The method of claim 2, wherein b) comprises determining: an indicator of classification of the normalized values between the rest class and a union of the intermediate class and of the stressed class, the classification indicator being based on a proportion of false positives and of false negatives; andan indicator of classification of the normalized values between the stressed class and a union of the intermediate class and of the rest class, the classification indicator being based on a proportion of false positives and of false negatives.

4. The method of claim 1, wherein b) comprises: determining an optimal rest interval, by setting the threshold distance; thendetermining the optimal threshold distance, depending on the optimal rest interval.

5. The method of claim 4, wherein b) comprises optimizing an extent of the rest interval.

6. The method of claim 5, wherein b) comprises optimizing a scale factor, the threshold distance being determined by multiplying the scale factor by the extent of the rest interval.

7. The method of claim 1, wherein the normalization function is an affine function, and wherein b) comprises determining parameters of the affine function on the basis of the optimized rest interval and of the optimized threshold distance.

8. The method of claim 1, wherein a) comprises applying a standardization function to the values of the physiological parameter measured at rest, to obtain standardized values of said physiological parameter, the standardization function being concave, andb) comprises determining, using the processing circuitry, the normalization function, the normalization function being determined on a basis: of the standardized values of the physiological parameter resulting from a), andof the distribution function;wherein the normalization function is configured to be applied to the standardized values of the physiological parameter resulting from a); andin c), the membership function comprises a composition of the distribution function, the normalization function, and the standardization function.

9. The method of claim 1, wherein: steps a) to c) form a sequence of steps that is associated with the physiological parameter measured in a), and leading to the determination of a membership function associated with the physiological parameter;the method further comprises various sequences of steps a) to c), respectively associated with various physiological parameters, so as to determine various membership functions, each membership function being respectively associated with one physiological parameter.

10. The method of claim 8, wherein the standardization function is of logarithmic or square-root type.

11. The method of claim 8, wherein a) comprises, using the processing circuitry: computing a coefficient of asymmetry of a distribution of the values of the physiological parameter that are measured when each test individual is at rest, the coefficient of asymmetry quantifying the asymmetry of the distribution;comparing the coefficient of asymmetry to a predefined threshold; anddepending on the comparison, applying the standardization function to the values measured at rest to obtain standardized values of the physiological parameter.

12. A method for determining a stress level of a user, depending on a physiological parameter measured on the user, during a measurement period, the value of which is liable to vary depending on the stress level of the user, the method comprising: i) measuring a value of the physiological parameter during a measurement period;ii) applying a membership function to the value of the physiological parameter measured in step i); andiii) estimating the stress level of the user depending on the value of the membership function computed in step ii);wherein the membership function is a function determined using the method of claim 1.

13. The method of claim 12, comprising an initialization phase, containing various initialization periods, in which periods the user is considered to be in a rest state, the initialization phase comprising: measuring, in each initialization period, one physiological parameter; anddetermining an indicator of dispersion of the values measured in each initialization period;the method comprising, prior to steps i) to iii), adjusting the membership function established for the physiological parameter so as to take into account the dispersion indicator.

14. A device, comprising the sensor, configured to be placed against a body of an individual, the sensor being configured to measure a physiological parameter of the individual, the device further comprising the processing circuitry, configured to receive measurements from the sensor and estimate a stress level of the individual according to the method of claim 12.

15. A method for determining a stress level of a user, depending on a plurality of physiological parameters of the user, the value of each physiological parameter being liable to vary depending on the stress level of the user, the method comprising: i) measuring values of various physiological parameters during a measurement period;ii) for each value of the physiological parameter measured in i), applying a membership function associated with the physiological parameter; andiii) estimating the stress level by combining the values of the membership functions computed in step i);wherein at least one membership function, associated with one physiological parameter, is a function determined using the method of claim 1.

16. The method of claim 15, comprising an initialization phase, containing various initialization periods, in which periods the user is considered to be in a rest state, the initialization phase comprising: measuring, in each initialization period, one physiological parameter; anddetermining an indicator of dispersion of the values measured in each initialization period;the method comprising, prior to steps i) to iii), adjusting the membership function established for the physiological parameter so as to take into account the dispersion indicator.

17. A device for determining a stress level of a user, the device comprising a sensor, configured to be placed against a body of an individual, the sensor being configured to measure a value of a physiological parameter of the individual during a measurement period, the device further comprising processing circuitry, configured to apply a membership function to the value of the physiological parameter measured by the sensor during the measurement period; andestimate the stress level of the user depending on the value of the membership function,wherein the membership function is determined using the method of claim 8.