A computer-implemented method for estimating a condition of a person, said method comprising: receiving a set of measurement values, said set of measurement values comprising a plurality of physiological measurement values applying to the condition of the person and a plurality of context measurement values, wherein said set of measurement values are obtained by a set of sensors (202a-d) comprising a plurality of physiological sensors (202a-b) and at least one environment sensor (202c-d), wherein each physiological measurement value is obtained by a physiological sensor (202a-b) making a measurement on the person and wherein each context measurement value is obtained by an environment sensor (202c-d) making a measurement on an environment in which the person is located; inputting the set of measurement values to an ensemble of learners for estimating the condition of the person, wherein each learner in the ensemble of learners is trained to make an estimate of the condition of the person based exclusively on features which are extracted from measurements by a single physiological sensor (202a-b) or environment sensor (202c-d) and wherein each learner, depending on availability amongst the received set of measurement values, receives measurement values relevant to the features as input; computing, by a learner in the ensemble of learners for which measurement values corresponding to the features used by the learner is available, an individual estimate value of the condition of the person, wherein said computing is performed by a plurality of learners to determine a plurality of individual estimate values; receiving weights to be applied to the individual estimate values determined by the learners in the ensemble of learners, wherein the weights are at least partly adapted to individual characteristics of the person; and combining individual estimate values based on the received weights to make a final estimate of the condition of the person. receiving a set of measurement values, said set of measurement values comprising a plurality of physiological measurement values applying to the condition of the person and a plurality of context measurement values, wherein said set of measurement values are obtained by a set of sensors (202a-d) comprising a plurality of physiological sensors (202a-b) and at least one environment sensor (202c-d), wherein each physiological measurement value is obtained by a physiological sensor (202a-b) making a measurement on the person and wherein each context measurement value is obtained by an environment sensor (202c-d) making a measurement on an environment in which the person is located; inputting the set of measurement values to an ensemble of learners for estimating the condition of the person, wherein each learner in the ensemble of learners is trained to make an estimate of the condition of the person based exclusively on features which are extracted from measurements by a single physiological sensor (202a-b) or environment sensor (202c-d) and wherein each learner, depending on availability amongst the received set of measurement values, receives measurement values relevant to the features as input; computing, by a learner in the ensemble of learners for which measurement values corresponding to the features used by the learner is available, an individual estimate value of the condition of the person, wherein said computing is performed by a plurality of learners to determine a plurality of individual estimate values; receiving weights to be applied to the individual estimate values determined by the learners in the ensemble of learners, wherein the weights are at least partly adapted to individual characteristics of the person; and combining individual estimate values based on the received weights to make a final estimate of the condition of the person.