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: Enclosed are embodiments for estimating vertical oscillation (VO) at the wrist. In some embodiments, a method comprises: obtaining, with a wearable device worn on a wrist of a user, sensor data indicative of the user's acceleration and rotation rate; estimating centripetal acceleration based on the user's acceleration and rotation rate; calculating a modified user's acceleration by subtracting the estimated centripetal acceleration from the user's acceleration; estimating center of mass (CoM) acceleration by decoupling an arm swing component of the user's acceleration from the modified user's acceleration; and computing vertical oscillation of the user's CoM using a machine learning model with at least the CoM acceleration as input to the machine learning model, or by integrating vertical acceleration derived from the CoM acceleration and a gravity vector.

Abstract: Embodiments are disclosed for real time estimation of DoT. In some embodiments, a method comprises: obtaining inertial acceleration data from a motion sensor of a mobile device worn or held by a user; computing a frequency spectrum of the acceleration data to generate horizontal and vertical frequency components at a step frequency of the user; constructing an acceleration trajectory of the horizontal frequency components in a horizontal reference frame, the horizontal frequency components forming an ellipse in the horizontal reference frame; determining a maximum radius vector of the ellipse; determining an angle to DoT based on the maximum radius vector; disambiguating a sign of the angle; and determining the disambiguated angle as the DoT.

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: 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.