Abstract: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

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., measurements obtained by an electronic device) can be detected prior to a preset alarm time. Upon determining, based on the activity-indicative data, that a wakefulness condition is satisfied, the alarm can be disabled such that the alarm stimuli is not to be presented at the preset alarm time.

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: An electronic device may use motion and/or activity sensors to estimate a user's maximum volumetric flow of oxygen (VO2 max). In particular, the electronic device may use the user's heart rate, speed, and grade to determine the VO2 max. However, in indoor environments, it may be difficult to accurately measure the user's speed and grade. Therefore, the device may receive the speed and grade from external equipment, such as exercise equipment. To ensure that the user is moving at the reported speed, a discordance detector may compare the user's cadence to an expected cadence based on the speed and grade reported by the external equipment. If the user's cadence is within an acceptable range of the expected cadence, the user's VO2 max may be estimated based on the speed and grade. If the user's cadence is not within the acceptable range, the speed and grade may be discarded or corrected.

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 work rate caloric expenditure by applying a work rate calorie model to the machine data; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

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: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Abstract: Embodiments are disclosed for posture and motion monitoring using mobile devices. In an embodiment, a method comprises: obtaining motion data obtained from at least one motion sensor worn by a user; obtaining at least one frame of skeletal data of the user based on at least one of camera data or depth data; calibrating, with the at least one processor, the motion data and skeletal data to determine a calibration offset; synchronizing the motion data and skeletal data; generating, with the at least one processor, a body pose of the user by: aligning reference frames of the synchronized motion data and the synchronized skeletal data using the calibration offset; estimating the body pose of the user based on the aligned motion data and skeletal data; and classifying, using a machine learning model, the estimated body pose of the user.

Abstract: Embodiments are disclosed for disabling/re-enabling head tracking for spatial audio applications. In an embodiment, a method comprises: obtaining, using one or more processors of an auxiliary device worn by a user, motion data; tracking, using the one or more processors, the user's head based at least in part on the motion data; determining, using the one or more processors, whether or not the user is walking based at least in part on the motion data; in accordance with determining that the user is walking, determining if a source device configured to deliver spatial audio to the auxiliary device is static for a specified period of time; and in accordance with determining that the user is walking and the source device is static for the specified period of time, disabling the head tracking.

Abstract: Embodiments are disclosed for disabling/re-enabling head tracking for spatial audio applications. In an embodiment, a method comprises: obtaining, using one or more processors of an auxiliary device worn by a user, motion data; tracking, using the one or more processors, the user's head based at least in part on the motion data; determining, using the one or more processors, whether or not the user is walking based at least in part on the motion data; in accordance with determining that the user is walking, determining if a source device configured to deliver spatial audio to the auxiliary device is static for a specified period of time; and in accordance with determining that the user is walking and the source device is static for the specified period of time, disabling the head tracking.

Abstract: In an example method, a mobile device receives motion data obtained by one or more sensors over a time period, where the one or more sensors are worn by a user, The mobile device determines, based on the motion data, an impact experienced by the user during the time of period, and determines one or more of characteristics of the user. The mobile device determines, based on the motion data and the one or more characteristics of the user, a likelihood that the user requires assistance subsequent to the impact, and generates one or more notifications based on likelihood.

Abstract: In an example method, a mobile device receives motion data obtained by one or more sensors worn by a user. The mobile device determines, based on the motion data, that the user has fallen at a first time and whether the user has moved between a second time and a third time subsequent to the first time. Upon determining that the user has not moved between the second time and the third time, the mobile device initiates a communication to an emergency response service at a fourth time after the third time. The communication includes an indication that the user has fallen and a location of the user.

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 work rate caloric expenditure by applying a work rate calorie model to the machine data; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

Abstract: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

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: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

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.