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: Embodiments are disclosed for submersion detection and underwater depth and low-latency temperature estimation. In an embodiment, a method comprises: determining a first set of vertical accelerations obtained from an inertial sensor of a wearable device; determining a second set of vertical accelerations obtained from pressure data; determining a first feature associated with a correlation between the first and second sets of vertical accelerations; and determining that the wearable device is submerged or not submerged in water based on a machine learning model applied to the first feature. In another embodiment, a method comprises: determining a submersion state of a wearable device; and responsive to the submersion state being submerged, computing a forward estimate of water temperature based on measured ambient water temperature at the water surface, a temperature error lookup table, and a rate of change of the ambient water temperature.

Abstract: The present disclosure generally relates to displaying information related to a physical activity. In some embodiments, methods and user interfaces for managing the display of information related to a physical activity are described.

Abstract: Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone). In some embodiments, a method comprises: detecting, with at least one processor, a crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.

Abstract: An electronic watch may include a housing at least partially defining an interior cavity divided into at least a first volume and a second volume, a pressure-sensing component positioned within the first volume, a speaker positioned within the first volume, a processor positioned within the second volume, a battery positioned within the second volume, and a barometric vent that allows air pressure equalization between the first volume and an external environment.

Abstract: An electronic watch may include a housing at least partially defining an interior cavity divided into at least a first volume and a second volume, a pressure-sensing component positioned within the first volume, a speaker positioned within the first volume, a processor positioned within the second volume, a battery positioned within the second volume, and a barometric vent that allows air pressure equalization between the first volume and an external environment.

Abstract: An electronic device includes a housing sidewall defining an opening and a display component, such as a display cover, disposed in the opening to form a gap between the housing sidewall and the display component. In at least one example, the cavity is defined by the sidewall and the display cover with the cavity in fluid communication with an external environment through the gap. In at least one example, an epoxy component at least partially defines the cavity and can be in direct contact with the housing sidewall.

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 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 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 watch may include a housing at least partially defining an interior cavity divided into at least a first volume and a second volume, a pressure-sensing component positioned within the first volume, a speaker positioned within the first volume, a processor positioned within the second volume, a battery positioned within the second volume, and a barometric vent that allows air pressure equalization between the first volume and an external environment.

Abstract: An electronic watch may include a housing at least partially defining an interior cavity divided into at least a first volume and a second volume, a pressure-sensing component positioned within the first volume, a speaker positioned within the first volume, a processor positioned within the second volume, a battery positioned within the second volume, and a barometric vent that allows air pressure equalization between the first volume and an external environment.

Abstract: Embodiments described herein provide for an electronic device comprising a set of classifiers that can determine whether the electronic device is likely on an airplane. Upon a determination that the electronic device is likely on an airplane, one or more wireless radios on the electronic device (e.g., an ultra-wideband ranging radio, a cellular radio, etc.) may be disabled or a prompt can be displayed to enable a user to place the device into airplane mode.

Abstract: Described herein are techniques to enable a mobile device to perform multi-source estimation of an altitude for a location. A baseline altitude may be determined at ground level for a location and used to calibrate a barometric pressure sensor on the mobile device. The calibrated barometric pressure sensor can then estimate changes in altitude relative to ground level based on detected pressure differentials, allowing a relative altitude to ground to be determined. Baseline calibration for the barometric sensor calibration can be performed to determine an ambient ground-level barometric pressure.

Abstract: Embodiments described herein provide for an electronic device comprising a set of classifiers that can determine whether the electronic device is likely on an airplane. Upon a determination that the electronic device is likely on an airplane, one or more wireless radios on the electronic device (e.g., an ultra-wideband ranging radio, a cellular radio, etc.) may be disabled or a prompt can be displayed to enable a user to place the device into airplane mode.

Abstract: Disclosed embodiments include wearable devices and techniques for detecting swimming activities, classifying user motion, detecting water submersion, and monitoring performance during swimming activities. By accurately and promptly detecting swimming activities and automatically distinguishing between different swimming stroke type performed during a swimming activity, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording swimming activities. In various embodiments, swimming activity detection techniques may improve the selectivity of motion based methods of identifying swimming activities identification by confirming motion analysis with water immersion and pressure data analysis that detects when the wearable device is submerged in water.

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 may include a motion sensor for detecting movement of the electronic device and a pressure sensor for detecting changes in elevation of the electronic device. Applications that run on the electronic device such as health and fitness applications may use motion sensor and pressure sensor data to track a user's physical activity. For example, processing circuitry in the electronic device may use the motion sensor to track a user's steps and the pressure sensor to track changes in the user's elevation. The processing circuitry may determine whether the user is climbing stairs based on the user's step rate and the user's changes in elevation. When the processing circuitry determines that the user is climbing stairs, the processing circuitry may use the pressure sensor and motion sensor to track and store the number of flights of stairs climbed by the user.

Abstract: The present disclosure relates to systems and methods of estimating energy expenditure of a user while swimming. A processor circuit of a user device can estimate a speed of the user based on a stroke rate and a stroke length. The processor circuit can estimate an efficiency of the user. The processor circuit can classify a swimming style of the user. The processor circuit can determine energy expenditure of the user based on the speed, the efficiency, and the style. The processor circuit can also detect glides of the user and adjust the energy expenditure.

Abstract: Measurements can be obtained from sensors to determine a state of a device. The state can be used to determine whether to provide an alert. For example, after a first alert is provided, it can be determined that the device is not accessible to the user based on the determined state, and a second alert can be suppressed at a specified time after providing the first alert. The sensor measurements can be monitored after suppressing the second alert, and a state engine can detect a change in a state based on subsequent sensor measurements. If the state change indicates that the device is accessible to the user the second alert can be provided to the user. Alerts can be dismissed based on a change in state. A first device can coordinate alerts sent to or to be provided by a second device by suppressing or dismissing such alerts.