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 . <EOS>