Google Inc.

Abel Seleshi Mengistu

Ivan Poupyrev

Patrick M Amihood

Vignesh Sachidanandam

Leonardo Giusti

Brandon Charles Barbello

John David Jacobs

Tyler Reed Kugler

Devon James O'Reilley Stern

Jung Ook Hong

Alok Chandel

Selim Flavio Cinek

Lucas Dupin Moreira Costa

Artur Tsurkan

Johan Prag

Nicholas Edward Gillian

Abhijit Aroon Shah

Michal Matuszak

Devon James O'Reilly Stern

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing gesture recognition using a space time neural network. The space time neural network employs machine learning to recognize a user's gesture based on complex radar data. The space time neural network is implemented using a multi-stage machine-learning architecture, which enables the radar system to conserve power and recognize the user's gesture in real time (e.g., as the gesture is performed). The space time neural network is also adaptable and can be expanded to recognize multiple types of gestures, such as a swipe gesture and a reach gesture, without significantly increasing size, computational requirements, or latency.

Smart-device-based radar system performing gesture recognition using a space time neural network

This document describes techniques and systems for radar-based gesture-recognition with context-sensitive gating and other context-sensitive controls. Sensor data from a proximity sensor and/or a movement sensor produces a context of a user equipment. The techniques and systems enable the user equipment to recognize contexts when a radar system can be unreliable and should not be used for gesture-recognition, enabling the user equipment to automatically disable or 'gate' the output from the radar system according to context. The user equipment prevents the radar system from transitioning to a high-power state to perform gesture-recognition in contexts where radar data detected by the radar system is likely due to unintentional input. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

Context-sensitive control of radar-based gesture-recognition

Systems and techniques are described for robust radar-based gesture-recognition. A radar system detects radar-based gestures on behalf of application subscribers. A state machine transitions between multiple states based on inertial sensor data. A no-gating state enables the radar system to output radar-based gestures to application subscribers. The state machine also includes a soft-gating state that prevents the radar system from outputting the radar-based gestures to the application subscribers. A hard-gating state prevents the radar system from detecting radar-based gestures altogether. The techniques and systems enable the radar system to determine when not to perform gesture-recognition, enabling user equipment to automatically reconfigure the radar system to meet user demand. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

Robust radar-based gesture-recognition by user equipment

This document describes techniques and systems for radar-based gesture-recognition with context-sensitive gating and other context-sensitive controls. Sensor data from a proximity sensor () and/or a movement sensor () produces a context of a user equipment (). The techniques and systems enable the user equipment () to recognize contexts when a radar system () can be unreliable and should not be used for gesture-recognition, enabling the user equipment () to automatically disable or 'gate' the output from the radar system () according to context. The user equipment () prevents the radar system () from transitioning to a high-power state () to perform gesture-recognition in contexts where radar data detected by the radar system () is likely due to unintentional input. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

Systems and techniques are described for robust radar-based gesture-recognition. A radar system () detects radar-based gestures on behalf of application subscribers. A state machine () transitions between multiple states based on inertial sensor data. A no-gating state () enables the radar system () to output radar-based gestures to application subscribers. The state machine () also includes a soft-gating state () that prevents the radar system () from outputting the radar-based gestures to the application subscribers. A hard-gating state () prevents the radar system () from detecting radar-based gestures altogether. The techniques and systems enable the radar system () to determine when not to perform gesture-recognition, enabling user equipment () to automatically reconfigure the radar system () to meet user demand. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

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Abel Seleshi Mengistu