Abstract: To determine locations of ultra-wideband (UWB) anchor devices in an indoor positioning system, the indoor positioning system obtains distance measurements between each pair of N UWB anchor devices in the indoor positioning system. Each distance measurement is determined based on a round trip time of a UWB signal communicated between the pair of UWB anchor devices. The indoor positioning system also determines a location of each of the UWB anchor devices within the indoor positioning system using the distance measurements, and reconstructs an absolute network topology of the UWB anchor devices using the determined locations of the UWB anchor devices. The absolute network topology is used to determine a location of a client device within the indoor positioning system.

Abstract: Various arrangements for performing fall detection are presented. A smart-home device may transmit radar waves. Based on reflected radar waves, raw waveform data may be created. The raw waveform data may be processed to determine that a fall by a person has occurred. Speech may then be output announcing that the fall has been detected via the speaker of the smart home device.

Abstract: To provide indoor navigation directions to a user, a client device receives (181) a request for indoor navigation directions, and communicates (182) an ultra-wideband (UWB) signal with at least one anchor device in an indoor positioning system having a predetermined location within the indoor positioning system. The client device determines (183) a distance between the client device and the at least one anchor device based on a round trip time associated with the UWB signal, and determines (184) an indoor location of the client device based on (i) the distance between the client device and the at least one anchor device and (ii) the predetermined location for the at least one anchor device. Additionally, the client device generates (185) a set of indoor navigation directions for traversing the destination location, and provides (186) the indoor navigation directions via a user interface of the client device.

Abstract: Implementations relate to selecting a particular device, from an ecosystem of devices, to provide responses to a device-agnostic request of the user while a scenario is occurring. The user specifies a scenario and contextual features are identified from one or more devices of the ecosystem to generate scenario features indicative of the scenario occurring. The scenario features are stored with a correlation to a device that is specified by the user to handle responses while the scenario is occurring. When a subsequent device-agnostic request is received, current contextual features are identified and compared to the scenario features. Based on the comparison, the specified assistant device is selected to respond to the device-agnostic request.

Abstract: In general, techniques are described by which to enable a transparency mode in vehicles. A device comprising one or more microphones and one or more processors may be configured to perform the techniques. The microphones may capture audio data representative of a sound scene external to a vehicle. The processors may perform beamforming with respect to the audio data to obtain object audio data representative of an audio object in the sound scene external to the vehicle. The processors may next reproduce, by interfacing with one or more speakers included within the vehicle and based on the object audio data, the audio object in the sound scene external to the vehicle.

Abstract: Techniques of operating an AR system include determining hand gestures formed by a user based on a sequence of two-dimensional images through skin of the user's wrist acquired from a near-infrared camera. Specifically, an image capture device disposed on a band worn around a user's wrist includes a source of electromagnetic radiation, e.g., light-emitting diodes in the infrared (IR) wavelength band that emit the radiation into the user's wrist and an IR detector which produces the sequence of two-dimensional images of a region within a dermal layer in the user's wrist. From this sequence, gesture detection circuitry determines values of a biological flow metric, e.g., a change in perfusion index (PI) between frames of the sequence, based on a trained model that generates the metric from the sequence. Finally, the gesture detection circuitry maps the values of the biological flow metric to specific hand/finger movements that determine a gesture.

Abstract: According an embodiment, a computing device can: identify, in a chat view associated with a video chat session, a first authorized participant and a second authorized participant of the video chat session; render, in the chat view, first visual data indicative of the first authorized participant and second visual data indicative of the second authorized participant based at least in part on identification of the first authorized participant and the second authorized participant, respectively; define, in the chat view, a chat zone indicative of a reference location of the first authorized participant; determine that the first authorized participant moved outside the chat zone; and/or conceal, in the chat view, the first visual data indicative of the first authorized participant based at least in part on determination that the first authorized participant moved outside the chat zone.

Abstract: Computing systems and methods are provided for detecting skin conditions of humans. A computing device can authenticate a user via a fingerprint scan of a first skin region of the user using a three-dimensional (3D) sonic sensor. The device can generate a three-dimensional (3D) volumetric sonic measurement of a second skin region of the user based at least in part on one or more sonic pulses of the three-dimensional sonic sensor. The device can input data indicative of the 3D volumetric sonic measurement into one or more machine-learning models, generate one or more skin cancer condition identifications associated with the second skin region of the user based on one or more outputs of the one or more machine-learned models, and provide one or more outputs including the one or more skin cancer condition identifications associated with the second skin region of the user.

Abstract: A wearable device may be used to perform a health or medical assessment of a user. The wearable device may detect user movements of the user. The wearable device generates signal data representing the user movements. The signal data is input into a model to identify a feature set. The feature set is converted into a score that corresponds to a medical condition of the user.

Abstract: A method including retrieving a set of first ultra-wide band (UWB) data representing locations in a physical space and device locations in the physical space, the first UWB data representing the locations being tagged as associated with a device, generating a set of first coordinates based on the set of first UWB data, generating second UWB data representing a current location of the UWB tag device in the physical space, generating a second coordinate based on the second UWB data, generating a tiled set of coordinates by partitioning a plane associated with the physical space based on the set of first coordinates and the second coordinate, determining whether the UWB tag device is proximate to a tagged coordinate in the tiled set of coordinates, and in response to determining the UWB tag device is proximate to a tagged coordinate, initiating an action by the device associated with the tagged coordinate.

Abstract: Implementations set forth herein relate to rendering, at a message application, certain messages with a particular sorting scheme (e.g., a classification-based sorting scheme) based on the certain messages having transfer times that satisfy a transfer time threshold and, optionally, based on the certain messages being unread. Other messages rendered by the message application can be rendered with an alternative sorting scheme, such as chronologically. Utilization of the particular sorting scheme for the certain messages enables a user, viewing the messages, to review (e.g., reply-to, delete, and/or view) the certain messages with reduced latency, thereby allowing the certain messages to be more quickly reviewed and actioned when received.

Abstract: A method can include determining, by a correlation device, that a correlation event has occurred based on at least a first device and a second device having corresponding directional profiles, and responsive to occurrence of the correlation event, causing the first device to transfer a state of the first device by sending a state information message to the second device.

Abstract: Various arrangements for performing fall detection are presented. A smart-home device (110, 201), comprising a monolithic radar integrated circuit (205), may transmit radar waves. Based on reflected radar waves, raw waveform data may be created. The raw waveform data may be processed to determine that a fall by a person (101) has occurred. Speech may then be output announcing that the fall has been detected via the speaker (217) of the smart home device (110, 201).

Abstract: Techniques of operating an AR system include determining hand gestures formed by a user based on a sequence of two-dimensional images through skin of the user's wrist acquired from a near-infrared camera. Specifically, an image capture device disposed on a band worn around a user's wrist includes a source of electromagnetic radiation, e.g., light-emitting diodes in the infrared (IR) wavelength band that emit the radiation into the user's wrist and an IR detector which produces the sequence of two-dimensional images of a region within a dermal layer in the user's wrist. From this sequence, gesture detection circuitry determines values of a biological flow metric, e.g., a change in perfusion index (PI) between frames of the sequence, based on a trained model that generates the metric from the sequence. Finally, the gesture detection circuitry maps the values of the biological flow metric to specific hand/finger movements that determine a gesture.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A wearable device, such as a head-wearable display (HWD), identifies a device pose based on a combination of angle-of-arrival (AOA) data generated by, for example, an ultra-wideband (UWB) positioning module, and inertial data generated by an inertial measurement unit (IMU). The HWD fuses the AOA data with the inertial data using data integration techniques such as one or more of stochastic estimation (e.g., a Kalman filter), a machine learning model, and the like, or any combination thereof. A computer device associated with the HWD can employ the fused data to identify a pose of the HWD (e.g., a six degree of freedom (6 DoF) pose) and can use the identified pose to modify virtual reality or augmented reality content implemented by the computer device, thereby providing an immersive and enjoyable experience for a user.

Abstract: A sensor on a wearable device may be further configured as a sensor for detecting and recognizing a gesture to control the wearable device. For example, light detected by a photoplethysmography (PPG) sensor of a smart watch may include (i) light back reflected from underneath the smart watch and (ii) light back reflected from a touch to a wrist adjacent to the smart watch. The detected light may be filtered to isolate the light back reflected from the touch. A waterfall image that includes information about how the isolated light changes with time and amplitude may be generated and used to detect and recognize gestures performed on the wrist, such as a touch. This additional touch area may help to supplement a touch area provided by a display to control the smart watch.

Abstract: A method including determining historical user data associated with an event occurring on a wearable device, determining a probability of interacting with an object on a display of the wearable device based on the historical user data, scaling a hitbox associated with the object to form a scaled hitbox, detecting a user input based on an eye tracking being within the scaled hitbox, and in response to detecting the user input, initiating an action corresponding to the object.

Abstract: Improved techniques of generating photographs using a camera or other image capture device includes the use of “soft” indicators such as haptics, audio, heads-up display, and other modalities to warn the user of a potential occlusion before a user activates a shutter trigger.

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.