Abstract: Methods, systems, and devices for physiological pattern recognition are described. A device may receive physiological data associated with a user from a wearable device. The device may determine that at least one physiological parameter associated with the received physiological data satisfies a physiological threshold associated with a pattern between the physiological threshold and a taggable event or a set of taggable events defined within an application associated with the wearable device. The device may then identify, based on the pattern, the taggable event or the set of taggable events indicating an activity the user engaged in that contributed to the at least one physiological parameter satisfying the physiological threshold, and cause a graphical user interface (GUI) of the device running the application to prompt the user to provide feedback associated with the identified taggable event or the identified set of taggable events.

Abstract: Methods, systems, and devices for activity tracking are described. A method for automatic activity detection may include receiving physiological data associated with a user from a wearable device and identifying an activity segment during which the user is engaged in a physical activity based on the physiological data. The method may further include identifying location data associated with the user for at least a portion of the activity segment and identifying one or more parameters associated with the physical activity based on the physiological data and the location data. The method may further include causing a user device to display the one or more parameters associated with the physical activity.

Abstract: Methods, systems, and devices for evaluating a sleep quality of a user using wearable-based data are described. The sleep quality of a user may be evaluated by combining physiological data collected via a wearable device, along with environmental sound data collected via one or more external devices. For example, a microphone on a device may monitor environmental sounds while a user sleeps, and the environmental sounds may be used to improve sleep stage classification. Additionally, or alternatively, sound instances occurring throughout a sleep interval may be identified. In some examples, sound data may be combined with other data, such as physiological data, to determine relationships between the sleep sound data and sleep quality. An indication of the sleep stages and transitions between the sleep stages, the sound instances, the sleep quality, or a combination thereof, may be presented to the user via an application.

Abstract: Methods, systems, and devices for activity tracking are described. A method for automatic activity detection may include receiving physiological data associated with a user from a wearable device and identifying an activity segment during which the user is engaged in a physical activity based on the physiological data. The method may further include identifying location data associated with the user for at least a portion of the activity segment and identifying one or more parameters associated with the physical activity based on the physiological data and the location data. The method may further include causing a user device to display the one or more parameters associated with the physical activity.

Abstract: Methods, systems, and devices for content recommendation are described. A device may receive physiological data associated with a user from a wearable device. The device may receive classifier data indicating an activity in which the user engaged. The classifier data is provided by the user via a graphical user interface (GUI) of the device running an application. The device may correlate the received physiological data and the received classifier data associated with the activity in which the user engaged, and select content for recommending to the user. The content may correspond to a respective effectiveness for regulating the received physiological data. The device may cause the GUI of the device to display the selected content for recommending to the user.

Abstract: Methods, systems, and devices for content delivery are described. A device may receive biometric data associated with a user from a wearable device, and receive application data associated with the user from the device. The application data may be indicative of a previous activity in which the user engaged or an upcoming activity in which the user is to engage. The device may determine content for recommending to the user based on correlating the received biometric data and the received application data. The content may correspond to a respective effectiveness for regulating the received biometric data. In some examples, the content may facilitate improvements to the user’s health and wellness. The device may cause a graphical user interface (GUI) of the device running an application to display the content for recommending to the user based on correlating the received biometric data and the received application data.

Abstract: Methods, systems, and devices for physiological pattern recognition are described. A device may receive physiological data associated with a user from a wearable device. The device may determine that at least one physiological parameter associated with the received physiological data satisfies a physiological threshold associated with a pattern between the physiological threshold and a taggable event or a set of taggable events defined within an application associated with the wearable device. The device may then identify, based on the pattern, the taggable event or the set of taggable events indicating an activity the user engaged in that contributed to the at least one physiological parameter satisfying the physiological threshold, and cause a graphical user interface (GUI) of the device running the application to prompt the user to provide feedback associated with the identified taggable event or the identified set of taggable events.

Abstract: Methods, systems, and devices for activity tracking are described. A method for automatic activity detection may include receiving physiological data associated with a user from a wearable device and identifying an activity segment during which the user is engaged in a physical activity based on the physiological data. The method may further include identifying location data associated with the user for at least a portion of the activity segment and identifying one or more parameters associated with the physical activity based on the physiological data and the location data. The method may further include causing a user device to display the one or more parameters associated with the physical activity.

Abstract: Methods, systems, and devices for activity classification are described. A system may receive physiological data associated with a user via a wearable device, where the physiological data includes at least motion data. The system may identify an activity segment during which the user is engaged in a physical activity based on the motion data, where the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment. The system may generate activity classification data associated with the activity segment based on the activity segment data, the activity classification data including a set of classified activity types and corresponding confidence values. The system may then cause a graphical user interface (GUI) of a user device to display the activity segment data and at least one classified activity type of the set of classified activity types.

Abstract: A method for providing speech recognition to a user on a mobile device are provided, the method comprising: 1) receiving, by a processor, audio data; 2) processing the audio data, by a speech recognition engine, to determine one or more corresponding text, wherein the processing comprises querying a local language model and a local acoustic model; and 3) displaying the one or more corresponding text on a screen of the mobile device.

Abstract: A method for providing speech recognition to a user on a mobile device are provided, the method comprising: 1) receiving, by a processor, audio data; 2) processing the audio data, by a speech recognition engine, to determine one or more corresponding text, wherein the processing comprises querying a local language model and a local acoustic model; and 3) displaying the one or more corresponding text on a screen of the mobile device.