Google Inc.

Disney Enterprises, Inc.

Leland Stanford Junior University

Other

Carnegie Mellon University

Ivan Poupyrev

Patrick M Amihood

Leonardo Giusti

Jaime Lien

Carsten C Schwesig

Mustafa Emre Karagozler

Nicholas Edward Gillian

Brandon Charles Barbello

Shiho Fukuhara

Eiji Hayashi

Vignesh Sachidanandam

Nan-Wei Gong

Munehiko Sato

Gaetano Roberto Aiello

Kishore Sundara-Rajan

Jack Schulze

Timo Arnall

David Scott Allmon

Tyler Reed Kugler

Karen Elizabeth Robinson

Hakim K Raja

Alejandro Jose Kauffmann

Ali Israr

John David Jacobs

Erik M Olson

Devon James O'Reilley Stern

Durrell Grant Bevington Bishop

Jung Ook Hong

Abel Seleshi Mengistu

Mauricio E Gutierrez Bravo

Dongeek Shin

Alok Chandel

Olivier Bau

Gerard George Pallipuram

Johan Prag

Susan Jane Wilhite

Shozo Harada

Antonio Xavier Cerruto

Shwetak N Patel

Sofien Bouaziz

Shahram Izadi

Selim Flavio Cinek

Lucas Dupin Moreira Costa

Artur Tsurkan

Megan Grant

Tong Wu

Yoshio Ishiguro

Patricia Hayes-Danitz

Zhiquan Yeo

Jonas Loh

James Rolfe Yurchenco

Leonid Sigal

Christopher Harrison

Philip John Jackson

Gary Keith Fedder

Scott E Hudson

Steven L Goldberg

David Kim

Youenn Colin

Carlos Martin Terminel Iberri

Steven Benjamin Goldberg

Xiaoyu Guo

Adam Bernstein

Michael Holton

Bo Robert Xiao

Octavio Ponce Madrigal

Bryan Allen

Daniel Leithinger

Brendon Allen Beardsley

YungSheng Chang

Michal Matuszak

Karl D D Willis

Rajinder Sodhi

Matthew Feld Glisson

Chris Harrison

Philipp Schoessler

Joseph Martin Rohde

Kathryn Joy Klatt

Jeanette Lomboy

Jason Hintz Llopis

Chris Ioffreda

Mathieu LeGoc

Karl Darcy Daniel Willis

Karl D Willis

Daniel Kaufman

Chih Yu Chen

Shiho Tremmel

Yuri Suzuki

James H Auger

Yoshio Ishigu

Jimmy Chion

An interactive cord can include a plurality of non-conductive lines and a plurality of conductive lines arranged together in a first longitudinal portion to form a touch-sensitive area within a first longitudinal portion of the interactive cord. A non-touch-sensitive area can be formed in a second longitudinal portion such in which the plurality of conductive lines is not exposed along an outer surface of the outer layer. The plurality of conductive lines can be arranged together with the one or more of the plurality of non-conductive lines within a third longitudinal portion. The second longitudinal portion can be arranged between the first longitudinal portion and the third longitudinal portion with respect to a longitudinal direction of the interactive cord. The third longitudinal portion can be open along the longitudinal direction to form a pair longitudinal edges of the outer layer that extend in the longitudinal direction of the interactive cord.

Touch-sensitive cord

This document describes techniques for radio frequency (RF) based micro-motion tracking. These techniques enable even millimeter-scale hand motions to be tracked. To do so, radar signals are used from radar systems that, with conventional techniques, would only permit resolutions of a centimeter or more.

RF-based micro-motion tracking for gesture tracking and recognition

Techniques and apparatuses are described that implement a smart-device-based radar system capable of determining characteristics of objects external to a vehicle, occupants within a vehicle, and objects proximal to an open-air vehicle. In particular, the system enables a smart device to perform many vehicle operations such as collision avoidance, occupant detection, and parking assistance in vehicle and open-air vehicle environments without integrated radar technology. By using a smart device to perform such actions, existing vehicles and open-air vehicles without integrated radar functionality may be able to leverage radar-based vehicle operations.

Smart-device-based radar system for vehicle operations

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing location tagging. The radar system has sufficient spatial resolution to recognize different external environments associated with different locations (e.g., recognize different rooms or different locations within a same room). Using the radar system, the smart device can achieve spatial awareness and automatically activate user-programmed applications or settings associated with the different locations. In this way, the radar system enables the smart device to provide a location-specific shortcut for various applications or settings. With the location-specific shortcut, the smart device can improve the user's experience and reduce the need to repeatedly navigate cumbersome interfaces.

Smart-device-based radar system performing location tagging

This document describes techniques and devices for a radar recognition-aided search. Through use of a radar-based recognition system, gestures made by, and physiological information about, persons can be determined. In the case of physiological information, the techniques can use this information to refine a search. For example, if a person requests a search for a coffee shop, the techniques may refine the search to coffee shops in the direction that the person is walking. In the case of a gesture, the techniques may refine or base a search solely on the gesture. Thus, a search for information about a store, car, or tree can be made responsive to a gesture pointing at the store, car, or tree with or without explicit entry of a search query.

Radar recognition-aided search

This document describes apparatuses and techniques for radar-enabled sensor fusion. In some aspects, a radar field is provided and reflection signals that correspond to a target in the radar field are received. The reflection signals are transformed to provide radar data, from which a radar feature indicating a physical characteristic of the target is extracted. Based on the radar features, a sensor is activated to provide supplemental sensor data associated with the physical characteristic. The radar feature is then augmented with the supplemental sensor data to enhance the radar feature, such as by increasing an accuracy or resolution of the radar feature. By so doing, performance of sensor-based applications, which rely on the enhanced radar features, can be improved.

Radar-enabled sensor fusion

This document describes techniques and systems that enable a smartphone-based radar system for determining user intention in a lower-power mode. The techniques and systems use a radar field to enable the smartphone to accurately determine the presence or absence of a user and further determine the intention of the user to interact with the smartphone. Using these techniques, the smartphone can account for the user's nonverbal communication cues to determine and maintain an awareness of users in its environment, and only respond to direct interactions once a user has demonstrated an intention to interact, which preserves battery power. The smartphone may determine the user's intention by recognizing various cues from the user, such as a change in position relative to the smartphone, a change in posture, or by an explicit action, such as a gesture.

Smartphone-based radar system for determining user intention in a lower-power mode

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

Techniques are described herein that enable advanced gaming and virtual reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or submillimeter scale, for user control actions even when those actions are optically occluded or obscured.

Advanced gaming and virtual reality control using radar

Techniques of identifying gestures include detecting and classifying inner-wrist muscle motions at a user's wrist using micron-resolution radar sensors. For example, a user of an AR system may wear a band around their wrist. When the user makes a gesture to manipulate a virtual object in the AR system as seen in a head-mounted display (HMD), muscles and ligaments in the user's wrist make small movements on the order of 1-3 mm. The band contains a small radar device that has a transmitter and a number of receivers (e.g., three) of electromagnetic (EM) radiation on a chip (e.g., a Soli chip. This radiation reflects off the wrist muscles and ligaments and is received by the receivers on the chip in the band. The received reflected signal, or signal samples, are then sent to processing circuitry for classification to identify the wrist movement as a gesture.

Hand gesture recognition based on detected wrist muscular movements

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 that enable radar-based authentication status feedback. A radar field is used to enable an electronic device to account for the user's distal physical cues to determine and maintain an awareness of the user's location and movements around the device. This awareness allows the device to anticipate some of the user's intended interactions and provide functionality in a timely and seamless manner, such as preparing an authentication system to authenticate the user before the user touches or speaks to the device. These features also allow the device to provide visual feedback that can help the user understand that the device is aware of the user's location and movements. In some cases, the feedback is provided using visual elements presented on a display.

Radar-based authentication status feedback

An interactive cord includes one or more touch-sensitive areas configured to detect user input and one or more non-touch-sensitive areas. An outer cover of the interactive cord includes a set of conductive lines braided together with one or more of a plurality of non-conductive lines at the touch-sensitive area. The set of conductive lines defines a plurality of intersections that each form a capacitive touchpoint at the touch-sensitive area. An inner core of the interactive cord includes at least the set of conductive lines and at least one of the plurality of non-conductive lines at the non-touch-sensitive area.

Touch-sensitive braided cord

Embodiments of the disclosed technology are directed to classifying motion data collected by wearable sensors. Motion data collected by a first wearable motion sensor and a second wearable motion sensor during the performance of an activity can be obtained. The motion data from the first wearable motion sensor can include data associated with one or more first motion primitives and the second motion data collected by the second wearable motion sensor can include data associated with one or more second motion primitives. The first motion data and the second motion data can be synchronized based at least in part on time stamp information. Data associated with a signature motion classification associated with the activity can be determined based at least in part on the one or more first motion primitives and the one or more second motion primitives.

Motion pattern recognition using wearable motion sensors

Growing community of inventors

Sunnyvale, CA, United States of America

Ivan Poupyrev