Abstract: Embodiments are disclosed for estimating caloric expenditure using a heart rate model specific to a motion class. In an embodiment, a method comprises: obtaining acceleration and rotation rate from motion sensors of a wearable device; determining a vertical component of inertial acceleration and a vertical component of rotational acceleration from the acceleration and rotation rate, respectively; determining a magnitude of the rotation rate; determining a correlation between the inertial vertical acceleration component and rotational acceleration; determining a percentage of motion outside a dominant plane of motion; predicting a motion class based on a motion classification model that takes as input the motions, correlation and percentage; determining a likelihood the user is walking; in accordance with determining that the user is likely not walking, configuring a heart rate model based on the predicted motion class; and estimating, using the configured heart rate model, a caloric expenditure of the user.

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 obtains a database including a plurality of data records. Each data record includes an indication of a respective impact previously experienced by a user of the mobile device, and sensor data generated by one or more first sensors worn by the user during that impact. The mobile device obtains sensor data generated by one or more second sensors worn by the user over a period of time, and determines whether the user has fallen during the period of time based on the database and the additional sensor data. The mobile device generates one or more notifications based on the determination of whether the user has fallen during the period of time.

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: 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: 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: 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: Systems and methods of analyzing a user's motion during a swimming session are described. One or more motions sensors can collect motion data of the user. A processor circuit can make motion analysis based on the motion data. The processor circuit can determine if the user's arm swing is a genuine swim stroke. The processor circuit can also determine whether the user is swimming or turning. The processor circuit can also classify the user's swim stroke style. The processor circuit can also determine the user's swim stroke phase. The processor circuit can also determine the user's stroke orbit consistency.

Abstract: A system and method for collecting motion data using a fitness tracking device located on an arm of a user, detecting that the arm is constrained based on the motion data, estimating a stride length of the user based on the motion data and historical step cadence-to-stride length data, calculating fitness data using the estimated stride length, and outputting the fitness data to the user.

Abstract: Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models.

Abstract: Disclosed embodiments include wearable devices and techniques for detecting walking workouts. By accurately and promptly detecting the start of walking workouts activities and automatically distinguishing between walking workout and causal walking activities, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording walking workouts.

Abstract: Disclosed embodiments include wearable devices and techniques for detecting the end of hiking activities. By accurately and promptly detecting the end of hiking activities automatically, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording a hiking activity.

Abstract: Disclosed embodiments include wearable devices and techniques for detecting cycling activities and monitoring performance during cycling. By accurately and promptly detecting the end of cycling workouts automatically, the disclosure enables wearable devices to accurately calculate user performance information when users forget to stop recording a cycling activity session. In various embodiments, cycling activity detection techniques involve a cycling speed measure that incorporates terrain gradient determined based on pressure data. In various embodiments, the cycling activity detection techniques may distinguish between a temporary stop and an intentional stop using an estimated energy expenditure.

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: 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: 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: Embodiments are disclosed for indoor/outdoor detection using a mobile device, such as wearable computer (e.g., smartwatch). In an embodiment, a method comprises: receiving, by one or more processors of a wearable computer, wireless access point (AP) scan data, global navigation satellite system (GNSS) data and inertial sensor data; determining, by the one or more processors, a first state of the wearable computer based on the wireless AP scan data; determining, by the one or more processors, a second state of the wearable computer based on a comparison of the GNSS data and the inertial sensor data; and outputting, by the one or more processors, an indoor/outdoor signal indicating that the wearable computer is indoors or outdoors based on the first and second states.

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.