A computer system for assessing sound to analyze a user's sleep includes a processor configured to perform operations including: (i) storing sample sound data associated with a plurality of sample sleep events, the sample sound data including a plurality of sample characteristics each respectively associated with at least one sample sleep event of the plurality of sample sleep events; (ii) receiving, from a client device, subject sound data collected during a sleep interval; (iii) analyzing, using a machine learning algorithm, the subject sound data collected during the sleep interval; (iv) identifying, based upon the analyzing, a subject characteristic associated with the subject sound data; (v) comparing the subject characteristic with the plurality of sample characteristics; and (vi) determining, based upon the comparing, whether the subject characteristic substantially matches at least one sample characteristic to identify one or more subject sleep events occurring during the sleep interval.

A method for automatically controlling an emotional environment in a vehicle is provided. The method includes generating reflected wave information by radiating an electromagnetic wave toward a rear seat of the vehicle and receiving a reflected wave. A degree of activity is determined based on the reflected wave information and at least one device installed within the vehicle is operated based on the degree of activity.

An electronic device is provided. The electronic device includes a biometric sensor for detecting biometric data, a situation sensing module, memory, and a processor operatively coupled to the biometric sensor, the situation sensing module, and the memory. The processor can check whether a user is in an inactive state using the situation sensing module, check reference biometric data corresponding to the inactive state if the user is in the inactive state, extract biometric data of the user using the biometric sensor, and measure the blood pressure of the user on the basis of the extracted biometric data and the reference biometric data.

A method, apparatus and device for obtaining a blood glucose measurement result. A neural network model is trained by using the following method, so as to obtain a trained first neural network model: acquiring a first invasive blood glucose measurement result of a tested object (101); forming a group of new training data by means of same and characteristic values of the most recent PPG signals of the tested object (102); training the neural network model with the training data, so as to obtain a trained first neural network model (106); and after a group of new PPG signals is acquired, extracting characteristic values of the new PPG signals, and inputting the characteristic values into the trained first neural network model, so as to obtain a target blood glucose measurement result (107).

A method includes receiving, from a first sensor, first physiological data associated with a first sleep session of a user. The method also includes receiving, from a sensor, second physiological data associated with a first sleep session of a user. The method also includes determining a first set of sleep-related parameters associated with the first sleep session of the user based at least in part on the first physiological data. The method also includes determining a second set of sleep-related parameters associated with the first sleep session of the user based at least in part on the second physiological data. The method also includes calibrating the second sensor based at least in part on a comparison between the first set of sleep-related parameters and the second set of sleep-related parameters.

An electronic device of a charging module system may include a housing having a first surface facing a first direction and a second surface facing a second direction opposite to the first direction; a display device at least partially is exposed through the first surface to display information to the outside; a biometric sensor disposed to be exposed in at least an area of the second surface and sensing biometric information of a user; a battery disposed between the display device and the biometric sensor; and a plurality of electrodes disposed adjacent to the at least an area of the second surface and formed to be exposed in at least another area of the second surface, in which the plurality of electrodes may surround at least a portion of the biometric sensor, and each of the plurality of electrodes has a notch protruding or recessed at least at one end.

Various examples are described for detecting heart rate and respiratory rate by using measurements of light applied to skin through an article. For example, a sensor application obtains a set of measurements of light. The application compensates for a contribution of the article based on one or more known optical properties of the article. The sensor application further determines, from the set of measurements of light, a periodic change in amplitude. The sensor application identifies the periodic change in amplitude as a heart rate having an identical periodicity. The sensor application identifies a respiratory rate as equal to the rate of change of the heart rate.

A bed includes components to control temperature of a sleep surface, for example based on time and historical usage patterns by a user. In some embodiments the temperature of the sleep surface is controlled based on information indicating a sleep state of the user. In some embodiments the temperature is dynamically adjusted so to achieve particular sleep states and/or sleep patterns for the user. In some embodiments the temperature and timing of temperature adjustments is iteratively adjusted over multiple sleep sessions so to achieve improvements in sleep states and/or sleep quality for the user.

A method and a system for providing feedback to a user for adjusting sleep pattern of the user. The method includes collecting a set of information related to the user, receiving a set of measurement data related to the user from a wearable electronic device, defining circadian rhythm and duration of sleep of the user, determining sleep scores for a predefined number of days and associating each sleep score with a corresponding go-to-bed time or time of falling asleep of the user. A sleep score is determined for each of the predefined number of days from the collected set of information, the set of measurement data, the circadian rhythm and the duration of sleep of the user. The method further includes analysing the sleep scores and associated go-to-bed time or time of falling asleep of the user to determine an optimum bedtime window for the user and providing feedback to the user based on the analysed sleep scores and the optimum bedtime window.

A circadian rhythm entrainment platform can use medication circadian profiles that include mappings of medications to circadian rhythm disruptions and can make conversions of such circadian rhythm disruptions to administrations of light therapy to adjust for the disruptions. The circadian rhythm entrainment platform can specify how to entrain a patient's circadian rhythm using light therapy to compensate for or anticipate side effects of medications and for optimizing medication schedules for a patient's circadian rhythm so as to minimize side effects and increase medication effectiveness. The circadian rhythm entrainment platform can also gather information on the effects of medications on circadian rhythms and interact with patients, medical providers, and other providers in relation to light therapy.