Abstract:
In some examples, a method includes detecting, based at least in part on sampling a first set (122) of a plurality of sensors of a computing device (110) at a first rate, an indication that the user has initiated a fitness activity (112B), wherein the computing device (110) stores pre-defined identifiers of non-fitness and fitness activities in a set of pre-defined indications of activities; responsive to detecting the indication that the user has initiated the fitness activity, sampling, at a second rate that is greater than the first rate, a second set (120) of the plurality of sensors to determine a probability that the user is engaged in the fitness activity (112B); and responsive to determining that the probability satisfies a threshold, collecting, sensor data for the fitness activity using a particular set of the plurality of sensors that corresponds to a pre-defined identifier for the fitness activity (112B).
Abstract:
In some examples, a method includes detecting, based at least in part on sampling a first set (122) of a plurality of sensors of a computing device (110) at a first rate, an indication that the user has initiated a fitness activity (112B), wherein the computing device (110) stores pre-defined identifiers of non-fitness and fitness activities in a set of pre-defined indications of activities; responsive to detecting the indication that the user has initiated the fitness activity, sampling, at a second rate that is greater than the first rate, a second set (120) of the plurality of sensors to determine a probability that the user is engaged in the fitness activity (112B); and responsive to determining that the probability satisfies a threshold, collecting, sensor data for the fitness activity using a particular set of the plurality of sensors that corresponds to a pre-defined identifier for the fitness activity (112B).
Abstract:
In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes applying, as inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states, values of at least one of the first parameters and/or values of at least one parameter derived from one or more of the first parameters. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.
Abstract:
Aspects of the present disclosure relate to detecting and repairing permanently pauses on a flow controlled fabric. In one aspect, one or more computing devices, such as a switch or a centralized controller, may detect whether a port of a network device receives one or more pause messages. The pause messages may instruct the network device to pause data transmission. Further, the one or more computing devices may determine a period of time during which the port receives the one or more pause messages and identify the port as a permanently paused port based on the determined period of time. The one or more computing devices may then reconfigure the permanently paused port to stop complying with the one or more pause messages.