Abstract: The subject system may be implemented by at least one processor configured to, by a system process, receive a request for one or more content item bundles, obtain the one or more content item bundles for presentation of the one or more content item bundles, receive a selection of a content item bundle from the presented one or more content item bundles, and providing, to the application process, one or more content items of the selected content item bundle. The one or more content item bundles are inaccessible to an application process before they are provided to the application process by the system process.

Abstract: The subject technology provides a framework for generating personalized and relevant content suggestions for a user of an electronic device. The system collects data from various sources, including location, motion sensors, routine places, contacts, calls, and proximity to people and devices. The collected data is analyzed using inference technology to identify patterns and anomalies. The system can detect routine activities and identify anomalies based on the duration and frequency of activities, social interactions, and changes in user behavior. The system includes modules for grouping related activities and summarizing individual events and coarse-grained activities. The system also includes a ranking algorithm that generates recommendations based on various factors such as recency, distinctiveness, media richness, and user engagement. The system further includes a method of adjusting recommendation ranking based on prior analytics and user preferences.

Abstract: In an example method, a computing device obtains sensor data generated by one or more accelerometers and one or more gyroscopes over a time period, including an acceleration signal indicative of an acceleration measured by the one or more accelerometers over a time period, and an orientation signal indicative of an orientation measured by the one or more gyroscopes over the time period. The one or more accelerometers and the one or more gyroscopes are physically coupled to a user walking along a surface. The computing device identifies one or more portions of the sensor data based on one or more criteria, and determines characteristics regarding a gait of the user based on the one or more portions of the sensor data, including a walking speed of the user and an asymmetry of the gait of the user.

Abstract: In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

Abstract: An electronic device detects an activity event associated with a user of the electronic device and displays a prompt that facilitates creation of a journaling entry based on the activity event, displays suggested content for inclusion in a journal entry of a journaling application, displays different user interfaces including options for a user to selectively include content items of suggested content in a journal entry of a journaling application, and/or displays one or more lookbacks.

Abstract: A method includes: presenting a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window; and in response to detecting a user input directed to the first affordance within the posture summary interface, presenting an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window.

Abstract: In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

Abstract: A method includes: presenting a posture summary interface including: a representation of the user, a visualization of a current accumulated strain value for the user, and a first affordance for initiating an animated posture summary associated with the accumulated strain value for the user over a respective time window; and in response to detecting a user input directed to the first affordance within the posture summary interface, presenting an animation of the representation of the user over the respective time window that corresponds to one or more instances in which head pose information changes associated with the user caused an increase or a decrease to the accumulated strain value greater than a significance threshold, wherein an appearance of the visualization of the current accumulated strain value for the user changes to represent the accumulated strain value for the user over the respective time window.

Abstract: In some embodiments, an electronic device detects an activity event associated with a user of the electronic device and displays a prompt that facilitates creation of a journaling entry based on the activity event, displays suggested content for inclusion in a journal entry of a journaling application, and/or displays one or more lookbacks.

Abstract: One or more electronic device may use motion and/or activity sensors to estimate a user's 6 minute walking distance. In particular, because users typically walk at less than their maximum output and in imperfect conditions, control circuitry within the device(s) may rely on walks of shorter distances to estimate the 6 minute walking distance. For example, the control circuitry may gather activity information for the user, such as heart rate, calories burned, and step count, and analyze a distance component and a speed component for periods in which the user has walked. Individual 6 minute walk distance estimates may be generated based on each of the activity information, distance component, and speed component. The distance and speed estimates may be corrected for walking behaviors that deviate from an ideal testing environment, and may then be fused with the activity estimate to generate a final 6 minute walk distance estimate.

Abstract: In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

Abstract: In one implementation, a method is performed for tiered posture awareness. The method includes: while presenting a three-dimensional (3D) environment, via the display device, obtaining head pose information for a user associated with the computing system; determining an accumulated strain value for the user based on the head pose information; and in accordance with a determination that the accumulated strain value for the user exceeds a first posture awareness threshold: determining a location for virtual content based on a height value associated with the user and a depth value associated with the 3D environment; and presenting, via the display device, the virtual content at the determined location while continuing to present the 3D environment via the display device.

Abstract: Embodiments are disclosed for stride length estimation and calibration at the wrist. In some embodiments, a method comprises: obtaining sensor data from a wearable device worn on a wrist of a user; deriving features from the sensor data; estimating a form-based stride length using an estimation model that takes the features and user height as input; and calibrating the form-based stride length. In other embodiments, user cadence and speed are used to estimate speed-based stride length which, upon certain conditions, is blended with the form-based stride length to get a final estimated stride length of the user.

Abstract: Embodiments are disclosed for estimating heart rate recovery (HRR) after maximum or high-exertion activity based on sensor observations. In some embodiments, a method comprises: obtaining, with at least one processor, sensor data from a wearable device worn on a wrist of a user; obtaining, with the at least one processor, a heart rate (HR) of the user; identifying, with the at least one processor, an observation window of the sensor data and HR; estimating, with the at least one processor during the observation window, input features for estimating maximum or near maximum exertion HRR of the user based on the sensor data and HR; and estimating, with the at least one processor during the observation window, the maximum or near maximum exertion HRR of the user based on a machine learning model and the input features.

Abstract: Enclosed are embodiments for estimating vertical oscillation (VO) at the wrist. In some embodiments, a method comprises: obtaining, with a wearable device worn on a wrist of a user, sensor data indicative of the user's acceleration and rotation rate; estimating centripetal acceleration based on the user's acceleration and rotation rate; calculating a modified user's acceleration by subtracting the estimated centripetal acceleration from the user's acceleration; estimating center of mass (CoM) acceleration by decoupling an arm swing component of the user's acceleration from the modified user's acceleration; and computing vertical oscillation of the user's CoM using a machine learning model with at least the CoM acceleration as input to the machine learning model, or by integrating vertical acceleration derived from the CoM acceleration and a gravity vector.

Abstract: Detecting a physiological parameter of a user at a first level during a first activity and at a second level during a second activity and displaying, based on the first level and the second level, a predictive change in the physiological parameter had the second activity been a third activity that is different from the second activity.

Abstract: Enclosed are embodiments for estimating gait time events and GCT using a wrist-worn device. In some embodiments, a method comprises: obtaining, with at least one processor of a wrist-worn device, sensor data indicative of acceleration and rotation rate; and predicting, with the at least one processor, at least one gait event time based on a machine learning (ML) model with the acceleration and rotation rate as input to the ML model.

Abstract: Embodiments are disclosed for a biomechanical trigger for improved responsiveness in grade estimation. In some embodiments, a method comprises: A method comprises: obtaining, from a wearable device worn by a user, cadence data, speed data and elevation data; determining a grade of a surface on which the user is traveling based on a ratio of a change in elevation based on the elevation data and a change in speed data; determining that the grade satisfies a first condition indicative of a horizontal speed compensation by the user at a grade onset; determining that the grade satisfies a second condition indicative of a rapid elevation increase or decrease at a grade onset; and confirming that the grade is a valid estimate based on either the first condition or the second condition being satisfied.

Abstract: In some implementations, a method includes: while presenting a 3D environment, obtaining user profile and head pose information for a user; determining locations for visual cues within the 3D environment for a first portion of a guided stretching session based on the user profile and head pose information; presenting the visual cues at the determined locations within the 3D environment and a directional indicator; and in response to detecting a change to the head pose information: updating a location for the directional indicator based on the change to the head pose information; and in accordance with a determination that the change to the head pose information satisfies a criterion associated with a first visual cue among the visual cues, providing at least one of audio, haptic, or visual feedback indicating that the first visual cue has been completed for the first portion of the guided stretching session.

Abstract: Embodiments are disclosed for identifying poor cardio metabolic health using sensors of wearable devices. In an embodiment, a method comprises: obtaining estimates of maximal oxygen consumption of a user during exercise; determining at least one confidence weight based on context data; adjusting the maximal oxygen consumption estimates using the at least one confidence weight; aggregating the adjusted maximal oxygen consumption estimates to generate a summary maximal oxygen consumption estimate and corresponding confidence interval for the user; and classifying cardiorespiratory fitness of the user based on at least one of the summary maximum consumption estimate, the corresponding confidence interval, a population error model or a low cardiorespiratory fitness threshold.