Abstract: Techniques and devices for user distinction for radar-based gesture detectors are described in this document. These techniques enable a computing device to distinguish users using a radar system that may collect and analyze radar characteristics of a user to distinguish that user from other users. The radar characteristics may include radar-reflection features of the user such as topological, temporal, gestural, and/or contextual information. A user may be distinguished without determining personally identifiable information, and the computing device may record radar characteristics to distinguish each user at a later time and provide tailored experiences. When an unregistered person is detected, the radar system may assign the unregistered person an unregistered user identification that contains detected radar characteristics to distinguish this person from other users at a future time.

Abstract: This document describes techniques and devices for in-line learning based on user inputs. Through in-line learning, a computing device may store characteristics of ambiguous gestures based on subsequent commands from a user. For example, the ambiguous gesture may be associated to one or more known gestures, but the ambiguous gesture cannot be recognized as one of the known gestures with sufficient confidence for gesture recognition. When the computing device fails to recognize the ambiguous gesture, the user may perform or request the performance of a command. This command may be determined to be a first command associated with a first gesture of the known gestures with which the ambiguous gesture was associated. As such, the computing device may store a characteristic of the ambiguous gesture with the first gesture to improve recognition of the first gesture in the future.

Abstract: Techniques and devices for radar-based gesture determination at long ranges are described in this document. The techniques described herein enable a computing device to detect and recognize gestures at long-range extents of up to eight meters. The computing device of this disclosure does not require the user to perform a gestural command at a specific location, in a specific orientation, contingent upon a wake-up trigger, or at a specific time, enabling the user to freely provide commands whenever and wherever is most convenient. This continual recognition of gestures may be enabled by a machine-learned model, generation of augmented data, and inclusion of negative data.

Abstract: Techniques and devices for ambiguous gesture determination using contextual information are described in this document for radar-enabled computing devices. Contextual information may include a status of operations that are performed by the radar-enabled computing device or an associated device at a current time, past time, or future time. Contextual information may also or instead include foreground and background operations, a history of operations saved to a memory, scheduled or anticipated operations, a location of a user or device, room-related context, user habits, and so forth. Two or more computing devices may coordinate this contextual information across a communication network to form a computing system.