Abstract: In one example, a method includes determining, by a processor (104) of a wearable computing device (102) and based on motion data generated by a motion sensor (106) of the wearable computing device, one or more strokes. In this example, the method also includes generating, by the processor and based on the motion data, a respective attribute vector for each respective stroke from the one or more strokes and classifying, by the processor and based on the respective attribute vector, each respective stroke from the one or more strokes into at least one category. In this example, the method also includes determining, by the processor and based on a gesture library and the at least one category for each stroke from the one or more strokes, a gesture. In this example, the method also includes performing, by the wearable device and based on the gesture, an action.

Abstract: An example method includes displaying, by a display (104) of a wearable device (100), a content card (114B); receiving, by the wearable device, motion data generated by a motion sensor (102) of the wearable device that represents motion of a forearm of a user of the wearable device; responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is less than an acceleration of the supination, displaying, by the display, a next content card (114C); and responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is greater than an acceleration of the supination, displaying, by the display, a previous content card (114A).

Abstract: An example method includes displaying, by a display (104) of a wearable device (100), a content card (114B); receiving, by the wearable device, motion data generated by a motion sensor (102) of the wearable device that represents motion of a forearm of a user of the wearable device; responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is less than an acceleration of the supination, displaying, by the display, a next content card (114C); and responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is greater than an acceleration of the supination, displaying, by the display, a previous content card (114A).

Abstract: An example method includes displaying, by a display (104) of a wearable device (100), a content card (114B); receiving, by the wearable device, motion data generated by a motion sensor (102) of the wearable device that represents motion of a forearm of a user of the wearable device; responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is less than an acceleration of the supination, displaying, by the display, a next content card (114C); and responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is greater than an acceleration of the supination, displaying, by the display, a previous content card (114A).

Abstract: In one example, a method includes determining, by a processor (104) of a wearable computing device (102) and based on motion data generated by a motion sensor (106) of the wearable computing device, one or more strokes. In this example, the method also includes generating, by the processor and based on the motion data, a respective attribute vector for each respective stroke from the one or more strokes and classifying, by the processor and based on the respective attribute vector, each respective stroke from the one or more strokes into at least one category. In this example, the method also includes determining, by the processor and based on a gesture library and the at least one category for each stroke from the one or more strokes, a gesture. In this example, the method also includes performing, by the wearable device and based on the gesture, an action.