A management apparatus includes an obtainer that obtains detected information from a sensor that detects sleep of a user; a sleep determiner that determines whether the user is in a sleep state, based on the detected information; a manager that is connected to an appliance via a network, and obtains state information indicating an operation state of the appliance; an appliance determiner that determines whether the state information is different from predetermined normal information indicating a normal operation state of the appliance, when the sleep determiner determines that the user is in the sleep state; and a notification controller that outputs a notification signal, when the appliance determiner determines that the state information is different from the normal information.

The method of the present invention quantifies the severity of a subject's neurodegenerative disorder. The subject answers a questionnaire which results in a patient-reported outcome dataset. Benchmark tests are carried out by the subject performing one or more tasks resulting in a task result dataset. Continuous sensors collect data resulting in a sensor dataset. Short assessment tests of the subject are conducted resulting in a short assessment dataset. The patient-reported outcome dataset, task result dataset, sensor dataset, and short assessment dataset are aggregated into an output dataset that includes non-motor outcome measures and motor outcome measures. A single score is generated that quantifies the severity of a neurodegenerative disorder of the subject based on the output dataset.

An electronic device may include a display, a photoplethysmogram (PPG) sensor, a wireless communication circuit, a processor operatively connected to the display, the PPG sensor, and the wireless communication circuit, and a memory operatively connected to the processor. The electronic device implements the method, including monitoring blood glucose values of a user using the PPG sensor, displaying a notification prompting the user to measure blood glucose values using an external electronic device based at least partially on the monitored blood glucose values, and receive additional blood glucose values measured by the external electronic device using the wireless communication circuit.

Exemplary embodiments described in this disclosure are generally directed to systems and methods for detecting alertness of a driver of a vehicle. In one exemplary method, a driver alertness detection system determines whether a driver of a vehicle is susceptible to lagophthalmos. If the driver is susceptible to lagophthalmos, the driver alertness detection system may evaluate an alertness state of the driver by disregarding an eyelid status of the driver and monitoring biometrics of the driver such as, a heart rate and/or a breathing pattern. Alternatively, the driver alertness detection system may evaluate an alertness state of the driver by placing a higher priority on the biometrics of the driver than on the eyelid status. However, if the driver is not susceptible to lagophthalmos, the driver alertness detection system evaluates the alertness state by placing a higher priority on the eyelid status than on the biometrics of the driver.

Hearing management, using a portable device or integrated portable devices, can include generating during a hearing diagnostics phase an audiogram based on responses of a user to signals conveyed to the user. In response to detecting ambient noises during the hearing diagnostics phase, noise cancellation can be performed to cancel the ambient noises in conjunction with conveying the signals to the user. During a hearing enhancement phase, sounds can be captured with the portable device. The captured sounds can be enhanced in real-time during the hearing enhancement phase by amplifying select frequencies of the captured sounds using signal gain. The frequencies can be selected, and the signal gain determined based on the audiogram. The captured sounds, now enhanced, can be conveyed to the user as frequency-enhanced sounds.

In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

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 monitoring system configured to measure the user's health-related parameters while in a certain state is disclosed. Based on whether the user is in the certain state and/or one or more criteria being met, the monitoring system can perform a physiological measurement such as a blood pressure measurement. The monitoring system can be capable of dynamically adjusting the measurement parameters, criteria, and acquired information based one or more scalers. The criteria can be based on user states or conditions such that user disruptions can be reduced and the measurement accuracy and/or efficiency can be enhanced. The monitoring system can also measure the user's parameters during the measurement and may abort the measurement if the measurement may not have accurate information and/or to reduce any disruption to the user. Alternatively, the measurement can be annotated so that the measurement can be used during data interpretation with certain qualifiers attached.

There is provided an information processing apparatus including an eyeball behavior analysis unit (300) that analyzes an eyeball behavior of a driver who drives a moving object, in which the eyeball behavior analysis unit dynamically switches an analysis mode according to a driving mode of the moving object.

A sleep-wakefulness determination device is provided which can determine sleep and wakefulness of a user. The sleep-wakefulness determination device is provided with a scalar calculation unit, a feature amount calculation unit, and a sleep-wakefulness determination unit. The scalar calculation unit is configured to calculate a scalar value on the basis of each component of an acceleration vector in a part of the body of the user. The feature amount calculation unit is configured to calculate, on the basis of the scalar value, a feature amount for each epoch defined as a prescribed time. The sleep-wakefulness determination unit is configured to determine sleep or wakefulness of the user on the basis of the feature amount of a desired epoch among the epochs and the feature amounts of surrounding epochs included in preceding and subsequent epochs of the desired epoch in a time series.