A method for configuring a device for detecting a situation from a set of situations where a physical system comprises the following steps: reception of a learning sequence corresponding to a given situation of the physical system; determination of parameters of a hidden-state Markov statistical model recorded in the detection device and relating to the given situation, on the basis of a prior initialisation of these parameters. The prior initialisation comprises the following steps: determination of a plurality of probability distributions from the learning sequence; distribution of the probability distributions between the various hidden states of the statistical model in question by global optimisation of a function of adaptation of these probability distributions to the various hidden states and to impossible transition constraints; and initialisation of the parameters of the statistical model in question using given representative probability distributions for each hidden state of the statistical model in question.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for configuring a detection device for detecting a situation from a set of situations wherein a physical system observed by at least one sensor is liable to be, comprising the following steps: reception of a sequence of observation data for the physical system, referred to as a learning sequence, supplied by the sensor and corresponding to a given situation of the physical system, determination, from the learning sequence, of parameters of a hidden-state Markov statistical model recorded in storage means of the detection device and relating to the given situation, by prior initialisation of these parameters, and then updating of these initialised parameters, wherein the prior initialisation comprises the following steps: the statistical model in question comprising a given number of hidden states, determination of a plurality of probability distributions from the learning sequence, by dividing the sequence into sub-sequences and allocating to each sub-sequence a probability distribution that models it statistically, the number of given probability distributions being greater than the number of hidden states of the statistical model in question, distribution of the probability distributions determined between the various hidden states of the statistical model in question, determination, for each hidden state of the statistical model in question and from the probability distributions allocated to this hidden state, of a single probability distribution representing this hidden state, and initialisation of the parameters of the statistical model in question from the determined representative probability distributions, characterised in that, the statistical model in question further comprising impossible transition constraints between certain hidden states, the distribution of the probability distributions determined between the various hidden states of the statistical model in question is done by global optimisation of a function of adaptation of these probability distributions to the various hidden states and to the impossible transition constraints, and the method further comprises a step of configuring the detection device so that the statistical model in question includes the parameters determined by said prior initialisation and then said updating. 2. The method for configuring as claimed in claim 1, wherein the distribution comprises the execution of an iterative K-Means algorithm on a number of classes equal to the number of hidden states of the statistical model in question, this iterative algorithm comprising, at each iteration: for each probability distribution determined from the learning sequence, the association of this probability distribution with one of the classes, this association using the Kullback Leibler divergence and the impossible transition constraints, and the calculation for each class, of a probability distribution representing its centre. 3. The method for configuring as claimed in claim 2, wherein the distribution comprises an initialisation of the iterative K-Means algorithm consisting of: sorting the probability distributions in the sequential order of the sub-sequences with which they are associated in the learning sequence, distributing the probability distributions sorted in the classes in this sequential order, from the first to the last class, for each class thus initialised, determining a probability distribution representing its centre. 4. The method for configuring as claimed in claim 2, wherein the function of adaptation of the probability distributions to the various hidden states and to the impossible transition constraints of the statistical model in question is, for a given distribution of the probability distributions determined between the various hidden states of the statistical model in question, this distribution being in accordance with the impossible transition constraints, a sum of Kullback Leibler distances between each probability distribution determined and each probability distribution representing the centre of the hidden state associated in this distribution. 5. The method for configuring as claimed in claim 2, wherein: the function of adaptation of the probability distributions to the various hidden states and to the impossible transition constraints of the statistical model in question is, for each distribution “a” of the probability distributions determined from the learning sequence between the various hidden states of the statistical model in question, a product between a function taking into account the Kullback Leibler divergence between each probability distribution determined from the learning sequence and each probability distribution representing the centre of the hidden state that is associated with it in this distribution “a”, and probabilities that each probability distribution determined from the learning sequence is associated with the hidden state defined by the distribution “a”, knowing the hidden state associated by the distribution “a” with the probability distribution preceding it in a given order of the probability distributions issuing from the learning sequence, the global optimisation of this adaptation function is achieved by execution of the Viterbi algorithm for the selection of a distribution that maximises it. 6. The method for configuring as claimed in claim 2, wherein, each probability distribution being a normal distribution, the probability distribution representing the centre of a class Ki is a normal distribution determined by the calculation of its expectation μi and its variance Ei according to the expectations μi,j and the variances Ei,j of all the probability distributions of this class Ki, as follows: μ i = 1 Card  ( Ki )  ∑ j ∈ Ki  μ i , j   and Σ i = 1 Card  ( Ki )  ∑ j ∈ Ki  ( Σ i , j + μ i , j H  μ i , j ) - μ i H  μ i , where Card is the “Cardinal” function and H is the Hermitian operator. 7. The method for configuring as claimed in claim 1, wherein the hidden-state Markov statistical model relating to the given situation is a left/right or cyclic model wherein a sequence or a cycle of hidden states is imposed. 8. A computer program downloadable from a communication network and/or recorded on a medium that can be read by computer and/or can be executed by a processor, characterised in that it comprises instructions for executing steps of a method for configuring as claimed in claim 1 when said program is executed on a computer. 9. An adaptive device for detecting a situation from a set of situations wherein a physical system observed by at least one sensor is liable to be, from observation data of the physical system supplied by the sensor, comprising: at least one sensor for supplying a sequence of observation data of the physical system, means for storing, for each situation in the set of situations, a hidden-state Markov statistical model, a computer, connected to the sensor and to the storage means, programmed to select one of the situations by comparing probabilities of these situations knowing the sequence of observation data, the probabilities being estimated on the basis of the statistical models stored, wherein the computer is further programmed to execute the steps of a method for configuring as claimed in claim 1, on reception of a sequence identified as a learning sequence corresponding to a given situation of the physical system. 10. The adaptive device as claimed in claim 9, wherein the sensor comprises at least one of the elements of the set consisting of a movement sensor with at least one measurement axis, a pressure sensor, a cardiometer and a glycaemia sensor. 11. The method for configuring as claimed in claim 3, wherein the function of adaptation of the probability distributions to the various hidden states and to the impossible transition constraints of the statistical model in question is, for a given distribution of the probability distributions determined between the various hidden states of the statistical model in question, this distribution being in accordance with the impossible transition constraints, a sum of Kullback Leibler distances between each probability distribution determined and each probability distribution representing the centre of the hidden state associated in this distribution. 12. The method for configuring as claimed in claim 3, wherein: the function of adaptation of the probability distributions to the various hidden states and to the impossible transition constraints of the statistical model in question is, for each distribution “a” of the probability distributions determined from the learning sequence between the various hidden states of the statistical model in question, a product between a function taking into account the Kullback Leibler divergence between each probability distribution determined from the learning sequence and each probability distribution representing the centre of the hidden state that is associated with it in this distribution “a”, and probabilities that each probability distribution determined from the learning sequence is associated with the hidden state defined by the distribution “a”, knowing the hidden state associated by the distribution “a” with the probability distribution preceding it in a given order of the probability distributions issuing from the learning sequence, the global optimisation of this adaptation function is achieved by execution of the Viterbi algorithm for the selection of a distribution that maximises it.