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: 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: Enclosed are embodiments for estimating gait time events and GCT using a wrist-worn device. In some embodiments, a method comprises: obtaining, with at least one processor of a wrist-worn device, sensor data indicative of acceleration and rotation rate; and predicting, with the at least one processor, at least one gait event time based on a machine learning (ML) model with the acceleration and rotation rate as input to the ML model.

Abstract: Embodiments are disclosed for stride length estimation and calibration at the wrist. In some embodiments, a method comprises: obtaining sensor data from a wearable device worn on a wrist of a user; deriving features from the sensor data; estimating a form-based stride length using an estimation model that takes the features and user height as input; and calibrating the form-based stride length. In other embodiments, user cadence and speed are used to estimate speed-based stride length which, upon certain conditions, is blended with the form-based stride length to get a final estimated stride length of the user.

Abstract: Embodiments are disclosed for identifying poor cardio metabolic health using sensors of wearable devices. In an embodiment, a method comprises: obtaining estimates of maximal oxygen consumption of a user during exercise; determining at least one confidence weight based on context data; adjusting the maximal oxygen consumption estimates using the at least one confidence weight; aggregating the adjusted maximal oxygen consumption estimates to generate a summary maximal oxygen consumption estimate and corresponding confidence interval for the user; and classifying cardiorespiratory fitness of the user based on at least one of the summary maximum consumption estimate, the corresponding confidence interval, a population error model or a low cardiorespiratory fitness threshold.

Abstract: Embodiments are disclosed for assessing walking steadiness of a mobile device user. In some embodiments, a method comprises: obtaining, with at least one processor of a mobile device, one or more mobility metrics indicative of a user's mobility, the mobility metrics obtained at least in part from a time series of sensor data output by at least one sensor of the mobile device; evaluating, with the at least one processor, the one or more mobility metrics over one or more specified time periods to derive one or more longitudinal features indicative of variability of the user's gait; and generating, with the at least one processor, at least one walking steadiness indicator for the user based on one or more walking steadiness component models and the one or more longitudinal features. Also disclosed are embodiments for training the component models.

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.