Abstract: One or more electronic device may use motion and/or activity sensors to estimate a user's 6 minute walking distance. In particular, because users typically walk at less than their maximum output and in imperfect conditions, control circuitry within the device(s) may rely on walks of shorter distances to estimate the 6 minute walking distance. For example, the control circuitry may gather activity information for the user, such as heart rate, calories burned, and step count, and analyze a distance component and a speed component for periods in which the user has walked. Individual 6 minute walk distance estimates may be generated based on each of the activity information, distance component, and speed component. The distance and speed estimates may be corrected for walking behaviors that deviate from an ideal testing environment, and may then be fused with the activity estimate to generate a final 6 minute walk distance estimate.

Abstract: Detecting a physiological parameter of a user at a first level during a first activity and at a second level during a second activity and displaying, based on the first level and the second level, a predictive change in the physiological parameter had the second activity been a third activity that is different from the second activity.

Abstract: Embodiments are disclosed for estimating heart rate recovery (HRR) after maximum or high-exertion activity based on sensor observations. In some embodiments, a method comprises: obtaining, with at least one processor, sensor data from a wearable device worn on a wrist of a user; obtaining, with the at least one processor, a heart rate (HR) of the user; identifying, with the at least one processor, an observation window of the sensor data and HR; estimating, with the at least one processor during the observation window, input features for estimating maximum or near maximum exertion HRR of the user based on the sensor data and HR; and estimating, with the at least one processor during the observation window, the maximum or near maximum exertion HRR of the user based on a machine learning model and the input features.

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 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 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: 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: Embodiments are disclosed for passive tracking of dyskinesia and tremor symptoms using a wearable computer. In an embodiment, a method comprises: obtaining, by one or more motion sensors of a computer attached to a user's limb, motion data; extracting, by one or more processors of the computer, one or more features from the motion data that are potentially indicative of dyskinesia or tremor; determining, by one or more processors of the computer and based on the one or more extracted features, the likelihood of dyskinesia or tremor; generating, by the one or more processors, data indicating the likelihood of dyskinesia or tremor; and outputting, by the one or more processors, the data through an output device of the computer.

Abstract: A method for ranking a plurality of systems based on their susceptibility to a selected risk that is determined from a plurality of risk indicators, is described herein. The method includes obtaining benchmark values for at least one benchmark system with a predetermined level of the predetermined risk; obtaining measured risk indicator values of the predetermined plurality of risk indicators in each of the plurality of systems, the predetermined plurality of risk indicators are the same in all of the plurality of systems; comparing the measured risk indicator values of each of the plurality of systems with the benchmark values of the at least one benchmark system; and ranking the plurality of systems based on the comparing to indicate the susceptibility of each of the plurality of systems to the predetermined risk.