Abstract: Provided are sleep quality assessment methods, apparatuses, electronic devices, and storage medium, and relates to the field of artificial intelligence and deep learning technologies, and in particular to sleep quality assessment methods, apparatuses, electronic devices, and storage medium. The method includes: determining sleep data of a subject; obtaining sleep feature data based on a reference core sleep period of the subject and the sleep data; and evaluating sleep quality of the subject based on the sleep feature data. The method assesses the sleep quality of the subject based on the sleep feature data extracted based on the reference core sleep period of the subject and the sleep data of the subject, takes into account individual factors of the subject in extracting the sleep feature data, thus providing a more accurate assessment of the sleep quality of the subject.

Abstract: A method, an apparatus, a computer device and storage media for generating a sleeping aid audio are provided. The method for generating a sleeping aid audio includes: a plurality of sleeping aid audio spectrums are obtained; the plurality of sleeping aid audio spectrums are processed with a genetic algorithm, to obtain a sleeping aid audio spectrum chain; and the sleeping aid audio is generated according to the sleeping aid audio spectrum chain.

Abstract: A blood oxygen saturation measurement method includes: acquiring a pressure measurement signal between a blood oxygen saturation measurement apparatus and an object; in response to determining that the pressure measurement value is within a preset range, and acquiring a blood oxygen saturation measurement signal of the object; and according to the blood oxygen saturation measurement signal of the object, obtaining a blood oxygen saturation measurement value of the object.

Abstract: The present disclosure provides a noise detection method and a noise detection apparatus. The method includes: segmenting a collected photoplethysmography signal into a plurality of sub-signal segments; extracting a characteristic of each sub-signal segment; for each sub-signal segment, determining a self-similarity of the sub-signal segment in the photoplethysmography signal based on the characteristic of the sub-signal segment; and determining that the sub-signal segment is noise when the self-similarity is lower than a threshold.

Abstract: The present disclosure provides a wearable device, a signal processing method and a signal processing apparatus, the signal processing method is applied to the wearable device, the wearable device is provided with a biosensor and a motion detector group, and the signal processing method includes the following steps. The human physiological signal collected by the biosensor is acquired, the human physiological signal is divided into multiple signal frames, each of the multiple signal frames corresponds to the time range; for each signal frame, it is determined whether the user wearing the wearable device is in the body motion state according to the body motion signal collected by the motion detector group within the time range corresponding to the signal frame, and when the user wearing the wearable device is not in the body motion state, the signal frame is stored into the preset buffer.