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: Disclosed embodiments include wearable devices and techniques for detecting cardio machine activities, estimating user direction of travel, and monitoring performance during cardio machine activities. By accurately and promptly detecting cardio machine activities and automatically distinguishing between activities performed on different types of cardio machines, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording activities on a wide variety of cardio machines. In various embodiments, cardio machine activity detection techniques may use magnetic field data from a magnetic field sensor to improve the accuracy of orientation data and device heading measurements used to detect the end of a cardio machine activity.

Abstract: Embodiments are disclosed for assessing walking steadiness of a mobile device user. In some embodiments, a method comprises: obtaining, with at least one processor of a mobile device, one or more mobility metrics indicative of a user's mobility, the mobility metrics obtained at least in part from a time series of sensor data output by at least one sensor of the mobile device; evaluating, with the at least one processor, the one or more mobility metrics over one or more specified time periods to derive one or more longitudinal features indicative of variability of the user's gait; and generating, with the at least one processor, at least one walking steadiness indicator for the user based on one or more walking steadiness component models and the one or more longitudinal features. Also disclosed are embodiments for training the component models.

Abstract: Embodiments are disclosed for assessing fall risk of a mobile device user. In some embodiments, a method comprises: obtaining one or more mobility metrics indicative of a user’s mobility, the mobility metrics obtained at least in part from sensor data output by at least one sensor of the mobile device; evaluating the one or more mobility metrics over one or more specified time periods to derive one or more longitudinal features; estimating a plurality of walking steadiness indicators based on a plurality of component models and the one or more longitudinal features; inferring the user’s risk of falling based at least in part on the plurality of walking steadiness indicators; and initiating an action or application on the mobile device based at least in part on the user’s risk of falling.

Abstract: The present disclosure generally relates to user interfaces for managing walking steadiness data. In some embodiments, a walking steadiness state is determined based on detected movement. When a first set of conditions are met, a notification is displayed. When a second set of conditions is met, the notification is not displayed.

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: Disclosed embodiments include wearable devices and techniques for detecting cardio machine activities, estimating user direction of travel, and monitoring performance during cardio machine activities. By accurately and promptly detecting cardio machine activities and automatically distinguishing between activities performed on different types of cardio machines, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording activities on a wide variety of cardio machines. In various embodiments, cardio machine activity detection techniques may use magnetic field data from a magnetic field sensor to improve the accuracy of orientation data and device heading measurements used to detect the end of a cardio machine activity.