1. A method, implemented by processing circuitry of a detection device, for configuring the detection device for detecting a situation from a set of situations wherein a physical trait of an object is observed by at least one sensor, comprising the following steps: reception, by the at least one sensor which is physically attached to or proximal to the object, of a sequence of observation data for the object, referred to as a learning sequence, and corresponding to a given situation of the object, determination, by the processing circuitry of the detection device, 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 parameters of the hidden-state Markov statistical model relating to the given situation include a matrix (a i,j ) of transition probabilities of each hidden state i towards each other hidden state j of this hidden-state Markov statistical model, wherein the prior initialisation comprises the following steps: the statistical model in question comprising a given number Cn of hidden states, determination, by the processing circuitry, of a plurality of Ln probability distributions from the learning sequence, by dividing the sequence into Ln sub-sequences and allocating to each sub-sequence a probability distribution that models it statistically, the number Ln of determined probability distributions being greater than the number Cn of hidden states of the statistical model in question, distribution, by the processing circuitry, of the Ln determined probability distributions determined between the Cn various hidden states of the statistical model in question, determination, by the processing circuitry, 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 initialization, by the processing circuitry, of the parameters of the statistical model in question from the determined representative probability distributions, wherein, the statistical model in question further comprises impossible transition constraints, which correspond to coefficients of the matrix (a i,j ) of transition probabilities set to zero, between certain hidden states, wherein the distribution of the Ln determined probability distributions between the Cn various hidden states of the statistical model in question is done by global optimisation of a function of adaptation of these Ln probability distributions to the Cn various hidden states and to the impossible transition constraints, said function of adaptation including a term relating to the probabilities of transition from one state to another for each of the Ln sub-sequences with respect to a next one of the Ln sub-sequences, and wherein 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.