Methods, systems, and devices for multiple wearable devices are described. A method may include receiving first physiological data associated with a user from a first wearable device worn at a first position on the user and second physiological data associated with the user from a second wearable device worn at a second position on the user. This method may include determining one or more physiological characteristics associated with the user based on a comparison of the first physiological data and the second physiological data and displaying an indication of the one or more physiological characteristics on a graphical user interface (GUI) of a user device.

An alarm application is described. A user interface of the alarm application may be presented that includes a generic alarm option and a sleep alarm option. A first view of the user interface may be presented that includes generic alarm related options when the generic alarm option is selected. A second view of the user interface may be presented that includes sleep alarm related options when the sleep alarm option is selected.

A method and apparatus for generating mood-based haptic feedback are disclosed. A haptic system includes a sensing device, a digital processing unit, and a haptic generator. The sensing device, in one embodiment, is configured to detect user's modalities in accordance with mood information collected by one or more sensors and capable of issuing a sensing signal in response to the user's modalities. The digital processing unit is capable of identifying a user's condition in accordance with the sensing signal and providing a haptic signal in response to the user's condition. The user's condition, in one aspect, indicates user's mood and/or user's psychological conditions. The haptic generator generates haptic feedback in accordance with the haptic signal.

Devices, systems and methods are disclosed that allow a patient to self-treat a medical condition, such as migraine headache, by electrical non-invasive stimulation of a nerve, such as the trigeminal nerve. A nerve stimulator is configured for coupling to a mobile device configured to receive a wireless signal, such as a mobile phone. The stimulator is further configured to generate an electrical impulse and to transmit the electrical impulse through one or more electrodes and the outer skin surface of the forehead of the patient to modulate the nerve within the patient.

Technology for a wearable heart rate monitoring device is disclosed. The wearable heart rate monitoring device can include a heart rate sensor operable to collect sensor data, a modulator operable to generate a modulated signal that includes the sensor data, a housing configured to engage a body feature or surface in a manner that allows for heart rate detection, and a communication module configured to transmit the sensor data in the modulated signal to a mobile computing device via a wired connection that is power limited. The mobile computing device is typically configured to demodulate the modulated signal in order to extract the sensor data.

A system and method for automated body mass index is disclosed. The disclosed method operates within a system architecture including one or more computing devices, one or more servers, and one or more databases. A processor operating within the one or more servers executes one or more algorithms for detecting relevant features associated with a potential client's multimedia information. The method may include calculating feature values, such as abdomen circumference, face width, face height, cheekbone width, jaw width, and neck width, and the like as well as calculating the body mass index of the potential client using one or more regression algorithms. A baseline and updated BMI may be determined, and used for determining a baseline and updated value.

Autoregressive modelling is used to identify periodic physiological signals such as heart rate or breathing rate in an image of a subject. The colour channels of a video signal are windowed and normalised by dividing each signal by its mean. The ratios of the normalised channels to each other are found and principal component analyses conducted on the ratio signals. The most periodic of the principal components is selected and autoregressive models of one or more different orders are fitted to the selected component. Poles of the fitted autoregressive models of different orders are taken and pure sinusoids corresponding to the frequency of each pole are generated and their cross-correlation with the original component is found. Whichever pole corresponds to the sinusoid with the maximum cross-correlation is selected as the best estimate of the frequency of periodic physiological information in the original video signal. The method may be used in a patient monitor or in a webcam-enabled device such as a tablet computer or smart phone.

An apparatus for estimating bio-information may include an optical sensor comprising a light emitter disposed on a substrate, and a plurality of light receiving groups which are arranged on a plurality of concentric circles on the substrate, at different distances from the light emitter, respectively, and a processor configured to drive one of the plurality of light receiving groups that is selected based on a type of the bio-information to be estimated, and estimate the bio-information of an object based on optical signals detected by the driven light receiving group.

A pulse measuring device includes: an image acquisition unit that acquires plural pieces of time-series photographed image data obtained by photographing a living body; an image processor that generates plural pieces of transformed image data corresponding to the plural pieces of photographed image data by performing a multiple resolution analysis on each of the plural pieces of photographed image data a plurality of times, each of the plural pieces of photographed image data being decomposed into a high-resolution component and a low-resolution component on the multiple resolution analysis; and a pulse measuring unit that calculates a feature quantity indicating luminance of a predetermined area in each of the plural pieces of transformed image data, calculates a variation period of the feature quantity by analyzing time-series data of the feature quantity, and calculates a pulse of the living body based on the variation period of the feature quantity.

A physiological monitor switches between a time domain analysis and a frequency domain analysis for determining a heart rate based on objective indicia of signal quality in an acquired heart rate signal.