Abstract: According to an embodiment of an aspect, there is provided a computer-implemented method for determining a sleep state of a user. The method comprising receiving (S11) a physiological signal from a physiological signal detector used by the user. The method further comprising determining (S12), based on the received physiological signal, the sleep state of the user. The method further comprising calculating (S13) a reliability value associated with the determination. The reliability value being calculated based on a comparison of the received physiological signal with historic physiological signals of the same sleep state as the determined sleep state. There is further provided a device (20) and computer-readable medium (30). In accordance with the present disclosure, the sleep state of a user may be determined with greater accuracy when compared with past methods.

Abstract: According to an embodiment of an aspect, there is provided a computer-implemented method for determining a sleep state of a user. The method comprising receiving (S11) a physiological signal from a physiological signal detector used by the user. The method further comprising determining (S12), based on the received physiological signal, the sleep state of the user. The method further comprising calculating (S13) a reliability value associated with the determination. The reliability value being calculated based on a comparison of the received physiological signal with historic physiological signals of the same sleep state as the determined sleep state. There is further provided a device (20) and computer-readable medium (30). In accordance with the present disclosure, the sleep state of a user may be determined with greater accuracy when compared with past methods.

Abstract: The present disclosure relates to a method for emotion-triggered capturing of audio and/or image data by an audio and/or image capturing device. The method includes receiving and analyzing a time-sequential set of data including first physiological data representing a first physiological parameter corresponding to a first person, a second physiological data representing a second physiological parameter corresponding to a second person, and voice audio data including a voice of at least one of the first and the second person, to determine whether a simultaneous change of emotional state of a first person and a second person occurs and transmitting a trigger signal to the capturing device. The present disclosure also relates to a corresponding apparatus and a system comprising the apparatus.

Abstract: The present disclosure relates to a method for emotion-triggered capturing of audio and/or image data by an audio and/or image capturing device. The method includes receiving and analyzing a time-sequential set of data including first physiological data representing a first physiological parameter corresponding to a first person, a second physiological data representing a second physiological parameter corresponding to a second person, and voice audio data including a voice of at least one of the first and the second person, to determine whether a simultaneous change of emotional state of a first person and a second person occurs and transmitting a trigger signal to the capturing device. The present disclosure also relates to a corresponding apparatus and a system comprising the apparatus.

Abstract: A method for estimating a condition of a person is disclosed. In one aspect, the method includes inputting a set of measurement values to an ensemble of machine learners. Each machine learner in the ensemble of machine 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 or environment sensor. The method further includes computing, by a machine learner in the ensemble of machine learners for which measurement values corresponding to the features used by the machine learner is available, an individual estimate value of the condition of the person, The method further includes receiving weights to be applied to the individual estimate values. The weights are at least partly adapted to individual characteristics of the person. The method further includes combining individual estimate values based on the received weights to make a final estimate of the condition of the person.