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 a crash event on a crash device; extracting multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features, wherein at least one multimodal feature is a rotation rate about a mean axis of rotation; and determining that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.
Abstract:
In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the machine data; determining a heart rate caloric expenditure based on the heart rate data and the calibrated maximal oxygen consumption of the user; and providing information corresponding to the heart rate caloric expenditure for presentation.
Abstract:
The present disclosure relates to methods and systems of determining swimming metrics of a user during a swimming session. The method can include receiving, by a processor circuit of a user device, motion information from one or more motion sensors of the user device; determining, by the processor circuit using the motion information, a first set of rotational data of the user device, wherein the first set of rotational data is expressed in a first frame of reference; converting, by the processor circuit, the first set of rotational data into a second set of rotational data, wherein the second set of rotational data is expressed in a second frame of reference; determining, by the processor circuit, one or more swimming metrics of the user; and outputting the one or more swimming metrics.
Abstract:
Methods, non-transitory machine-readable mediums, and system to provide location services are described. In an embodiment, a method provides receiving at least two position fixes for a trajectory of an electronic device, where the at least two position fixes are obtained intermittently, matching the at least two position fixes to points on a path indicated in map data, and computing a distance for the trajectory using distance information using the map data.
Abstract:
Embodiments are disclosed for user posture transition detection and classification. In an embodiment, a method comprises: obtaining, using one or more processors, motion data from a headset worn by a user; determining, using the one or more processors, one or more windows of motion data that indicate biomechanics of one or more phases of a user's postural transition; and classifying, using the one or more processors, as the user's postural transition based on the one or more windows of data.
Abstract:
One or more electronic device may use motion and/or activity sensors to estimate a user's maximum volumetric flow of oxygen, or VO2 max. In particular, although a correlation between heart rate and VO2 max may be linear at high heart rate levels, there is not a linear correlation at lower heart rate levels. Therefore, for users without extensive workout data, the motion sensors and activity sensors may be used to determine maximum calories burned by the user, workout data, including heart rate data, and body metric data. Based on these parameters, a personalized relationship between the user's heart rate and oxygen pulse (which is a function of VO2) may be determined, even with a lack of high intensity workout data. In this way, a maximum heart rate and therefore a VO2 max value may be approximated for the user.
Abstract:
Techniques are disclosed for facilitating disabling an alarm in response to particular types of activity-indicative data. More specifically, activity-indicative data (e.g., sensor data or input(s) can be detected prior to a preset alarm time. Upon determining, based on the activity-indicative data, that a wakefulness condition is satisfied (e.g., that the activity-indicative data corresponds to one or more predefined characteristics), a disablement query can be displayed that includes an option to disable the alarm. In response to detecting a selection of the option, the alarm can be disabled such that the alarm stimuli is not to be presented at the preset alarm time.
Abstract:
A digitally stored map can indicate the signal quality for each of the map's regions. A device can determine its location, speed, and direction using global positioning system (GPS) and other sensors. Based on this information, the mobile device can predict a field of locations within which the device will probably be located within a specified future time frame. Based on both the information indicating signal quality and the probable future field of locations, the device can estimate a moment at which the device will probably begin to suffer from low-quality or absent signal. Using this prediction, the device can proactively perform a variety of anticipatory remedial actions. For example, the device can begin allocating a greater portion of currently available wireless network communication bandwidth to the reception of data packets that represent content that is being streamed to the device, so that the device can proactively buffer those packets.
Abstract:
Throttling of transition attempts to connected mode based on user context. A wireless device may camp on a serving cell. A motion state of the wireless device may be monitored. One or more connected mode transition procedures on the serving cell may be attempted. If at least a threshold number of connected mode transition procedures fail on the serving cell while the wireless device is stationary, further connected mode transition attempts may be throttled for up to a certain amount of time. Alternatively, or in addition, the wireless device may bar itself from camping on that cell for up to a certain amount of time. Either or both of throttling connected mode transition attempts or barring cells may also be based on other aspects of user context, such as display state.
Abstract:
Throttling of transition attempts to connected mode based on user context. A wireless device may camp on a serving cell. A motion state of the wireless device may be monitored. One or more connected mode transition procedures on the serving cell may be attempted. If at least a threshold number of connected mode transition procedures fail on the serving cell while the wireless device is stationary, further connected mode transition attempts may be throttled for up to a certain amount of time. Alternatively, or in addition, the wireless device may bar itself from camping on that cell for up to a certain amount of time. Either or both of throttling connected mode transition attempts or barring cells may also be based on other aspects of user context, such as display state.