Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, sensor data for a first window of time and additional sensor data for a second window of time overlapping the first window of time. The sensor data and the additional sensor data are provided as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture, predicted gesture start time, and predicted gesture end time based on the sensor data. A predicted gesture is determined based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.

Abstract: Some embodiments described in this disclosure are directed to a first electronic device that operates in a remote interaction mode with a second electronic device, where user interactions with images displayed on the first electronic device cause the second electronic device to update display of the images and/or corresponding user interfaces on the second electronic device.

Abstract: The embodiments set forth techniques for managing workout profiles on computing devices. In particular, the techniques include a method that is implemented by a fitness application executing on a client device, and include the steps of (1) receiving a workout object from an entity that is external to the fitness application, (2) generating, within the fitness application, a workout profile that is based on the workout object, where the workout profile is associated with at least one condition, (3) monitoring information gathered from at least one sensor that is communicatively coupled to the client device, and (4) in response to determining, based on the information, that the at least one condition is satisfied: causing at least one user interface to reflect that the at least one condition is satisfied. The techniques also include a method for generating workout objects from which workout profiles can be derived on client devices.

Abstract: The present disclosure generally relates to communicating between computer systems, and more specifically to techniques for communicating user interface content.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: Some embodiments described in this disclosure are directed to a first electronic device that operates in a remote interaction mode with a second electronic device, where user interactions with images displayed on the first electronic device cause the second electronic device to update display of the images and/or corresponding user interfaces on the second electronic device.

Abstract: The present disclosure generally relates to displaying user interfaces with device controls.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.

Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.