Abstract: A method of tuning a wearable device for collecting physiological data of a wearer, a wearable device for collecting physiological data of a wearer, a method of controlling a wearable device for collecting physiological data of a wearer, a wearable device for collecting physiological data of a wearer the method, and a computer-readable medium. The method of tuning a wearable device for collecting physiological data of a wearer comprises the steps of collecting a PPG signal using the device; determining whether or not the collected PPG signal is attributable to the wearer being human or animal; and discontinuing the tuning if the collected PPG signal is not attributable to the wearer being human or animal; wherein determining whether or not the collected PPG signal is attributable to the wearer being human or animal comprises determining information about at least one cardiac cycle based on at least one pulse in PPG signal.

Abstract: A method of sleep stage detection using vital sign features derived from PPG signals. The method includes performing a logistic regression operation based on a machine learning classifier model to calculate an indication value for an intermediate epoch. The indication value is calculated based on the vital sign features for the intermediate epoch as well as those of the preceding and succeeding epochs. The method then detects the sleep stage of the corresponding intermediate epoch based on the indication value for the corresponding intermediate epoch.

Abstract: A method of sleep stage detection using vital sign features derived from PPG signals. The method includes performing a logistic regression operation based on a machine learning classifier model to calculate an indication value for an intermediate epoch. The indication value is calculated based on the vital sign features for the intermediate epoch as well as those of the preceding and succeeding epochs. The method then detects the sleep stage of the corresponding intermediate epoch based on the indication value for the corresponding intermediate epoch.

Abstract: In a described embodiment, a method of sleep stage detection is disclosed. The method comprises receiving a first vital sign feature 701a including a plurality of first feature values v1-v11 corresponding to respective ones of a plurality of epochs; and performing a first logistic regression operation 701a? to calculate a first indication value 702a for each intermediate one of the epochs based on the corresponding first feature value v6 and those of preceding v1-v5 and succeeding v7-v11 ones of the epochs, the first indication value 702a being descriptive of a sleep stage of the corresponding intermediate epoch. A method of creating a model for logistic regression, a method of extracting a heart rate variability feature, a method of creating a model for extracting a heart rate variability feature, a method of deriving a medical dosage, a method of assessing a responsiveness to a medical dosage, a computer-readable medium, a computing device and a wearable device are also disclosed.