Abstract: A method comprising determining a sleep state of a test subject using input data from the test subject. The input data comprise: at least one measure of test subject movement; and at least one cardiorespiratory feature of the test subject and are derived from video images of the test subject. The at least one cardiorespiratory feature of the test subject is derived using a feature extractor trained using reference cardiorespiratory signals for each of a plurality of reference subjects derived from time-resolved measurements from a plurality of sensors worn by the reference subjects, the feature extractor comprising a neural network, and the reference cardiorespiratory signals. Also provided a method of training a machine-learning algorithm to determine a sleep state of a subject using training data in respect of a plurality of training subjects derived from video images of the training subjects using the feature extractor.

Abstract: A method of training a supervised machine-learning algorithm to determine a sleep state of a subject. The method uses training data and training labels in respect of a plurality of subjects derived from video images of the subjects. The training data comprises: at least one measure of subject movement; and at least one cardiorespiratory parameter of the subject. The method comprises training the supervised machine learning algorithm using the training data and the training labels.