Abstract: 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.

Abstract: This document describes techniques for fine-motion virtual-reality or augmented-reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or sub-millimeter scale, for user control actions even when those actions are small, fast, or obscured due to darkness or varying light. Further, these techniques enable fine resolution and real-time control, unlike conventional RF-tracking or optical-tracking techniques.

Abstract: 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.

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular estimation using machine learning. In particular, a radar system 102 includes an angle-estimation module 504 that employs machine learning to estimate an angular position of one or more objects (e.g., users). By analyzing an irregular shape of the radar system 102's spatial response across a wide field of view, the angle-estimation module 504 can resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar system 102 to correctly identify the angular position of the object. Using machine-learning techniques, the radar system 102 can achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.

Abstract: 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.

Abstract: Various arrangements for performing ballistocardiography using a mobile device are presented. A radar integrated circuit of a mobile device may emit frequency-modulated continuous-wave (FMCW) radar. Reflected radio waves based on the FMCW radar being reflected off objects may be received and used to create a raw radar waterfall. The raw radar waterfall may be analyzed to create a ballistocardiography waveform. Data based on the ballistocardiography waveform may be output, such as to a machine-learning application installed on the mobile device.

Abstract: This document describes techniques and systems that enable a mobile device-based radar system (104) for providing a multi-mode interface (114). A radar field (110) is used to enable a user device (102, 702) to accurately determine a presence or threshold movement of a user near the user device. The user device provides a multi-mode interface having at least first and second modes and providing a black display or a low-luminosity display in the first mode. The user device detects, based on radar data and during the first mode, a presence or threshold movement by the user relative to the user device and responsively changes the multi-mode interface from the first mode to the second mode. Responsive to the change to the second mode, the user device provides visual feedback corresponding to the implicit interaction by adjusting one or more display parameters of the black display or the low-luminosity display.

Abstract: 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.

Abstract: Systems and methods of providing gesture-based control of a user interface are provided. For instance, a presence of a control article can be detected in a first proximity zone proximate a user device. Responsive to detecting the presence of the control article, presentation data corresponding to a presentation mode of a user interface associated with the user computing device can be provided for display. A presence of the control article can be detected in a second proximity zone proximate the user computing device. The second proximity zone can define a separate physical area than the first proximity zone. Responsive to detecting the presence of the control article in the second proximity zone, interactive data corresponding to an interactive mode of the user interface can be provided for display.

Abstract: A pre-fabricated sensor assembly for an interactive object including an object substrate is provided. The pre-fabricated sensor assembly includes a touch sensor comprising a plurality of flexible sensing elements, a first electronics module comprising sensing circuitry in electrical communication with the plurality of flexible sensing elements, a communication interface comprising a first end portion coupled to the first electronics module and comprising a second end portion, a receptacle coupled to the second end portion of the communication interface, and one or more flexible retaining layers that define a housing for a first portion of the pre-fabricated sensor assembly. The first portion of the pre-fabricated sensor assembly can include at least a portion of each of the plurality of flexible sensing elements.

Abstract: A removable electronics device and related pre-fabricated sensor assemblies having different sensor layouts are provided. The removable electronics module includes one or more processors, an inertial measurement unit, a first communication interface configured to communicatively couple the removable electronics device to one or more computing devices, a second communication interface configured to communicatively couple the removable electronics device to a plurality of pre-fabricated sensor assemblies, and a housing at least partially enclosing the processor, the inertial measurement unit, the first communication interface, and the second communication interface. The housing includes a first opening in at least one longitudinal surface and adjacent to at least a portion of the first communication interface and a plurality of second openings in a lower surface and adjacent to the plurality of contact pads of the second communication interface.

Abstract: Various embodiments dynamically learn user-customizable input gestures. A user can transition a radar-based gesture detection system into a gesture-learning mode. In turn, the radar-based gesture detection system emits a radar field configured to detect a gesture new to the radar-based gesture detection system. The radar-based gesture detection system receives incoming radio frequency (RF) signals generated by the outgoing RF signal reflecting off the gesture, and analyzes the incoming RF signals to learn one or more identifying characteristics about the gesture. Upon learning the identifying characteristics, the radar-based gesture detection system reconfigures a corresponding input identification system to detect the gesture when the one or more identifying characteristics are next identified, and transitions out of the gesture-learning mode.

Abstract: 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 a 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.

Abstract: 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 (108) and/or a movement sensor (108) produces a context of a user equipment (102). The techniques and systems enable the user equipment (102) to recognize contexts when a radar system (104) can be unreliable and should not be used for gesture-recognition, enabling the user equipment (102) to automatically disable or “gate” the output from the radar system (104) according to context. The user equipment (102) prevents the radar system (104) from transitioning to a high-power state (1910) to perform gesture-recognition in contexts where radar data detected by the radar system (104) 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.

Abstract: 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.

Abstract: This document describes techniques and systems that enable radar-based gesture enhancement for voice interfaces. The techniques and systems use a radar field to accurately determine three-dimensional (3D) gestures that can be used instead of, or in combination with, a voice interface to enhance interactions with voice-controllable electronic devices. These techniques allow the user to make 3D gestures from a distance to provide a voice input trigger (e.g., a “listen” gesture), interrupt and correct inaccurate actions by the voice interface, and make natural and precise adjustments to functions controlled by voice commands.

Abstract: 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.

Abstract: 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.

Abstract: A removable electronics device and related pre-fabricated sensor assemblies having different sensor layouts are provided. The removable electronics module includes one or more processors, an inertial measurement unit, a first communication interface configured to communicatively couple the removable electronics device to one or more computing devices, a second communication interface configured to communicatively couple the removable electronics device to a plurality of pre-fabricated sensor assemblies, and a housing at least partially enclosing the processor, the inertial measurement unit, the first communication interface, and the second communication interface. The housing includes a first opening in at least one longitudinal surface and adjacent to at least a portion of the first communication interface and a plurality of second openings in a lower surface and adjacent to the plurality of contact pads of the second communication interface.

Abstract: Systems and techniques are described for robust radar-based gesture-recognition. A radar system (104) detects radar-based gestures on behalf of application subscribers. A state machine (2000) transitions between multiple states based on inertial sensor data. A no-gating state (2002) enables the radar system (104) to output radar-based gestures to application subscribers. The state machine (2000) also includes a soft-gating state (2004) that prevents the radar system (104) from outputting the radar-based gestures to the application subscribers. A hard-gating state (2006) prevents the radar system (104) from detecting radar-based gestures altogether. The techniques and systems enable the radar system (104) to determine when not to perform gesture-recognition, enabling user equipment (102) to automatically reconfigure the radar system (104) to meet user demand.