Abstract: Various systems, devices, and methods for contactless sleep tracking are presented. Based on data received from a contactless sensor, such as a radar sensor, determine that a user has entered a sleep state. A transition time may be determined at which the user transitions from the sleep state to an awake state. An environmental event, based on data received from an environmental sensor, may be identified as occurring within a time period of the transition time. The user waking may be attributed to the environmental event based on the environmental event occurring within the time period of the transition time. An indication of the attributed environmental event as a cause of the user waking may be output.

Abstract: Various devices, systems and methods for performing contactless monitoring of the sleep of multiple users over a same time period are presented herein. Clustering may be performed based on data received from a radar sensor. Based on the clustering performed on the data received from the radar sensor, a determination may be made that two users are present within the region. In response to determining that two users are present, a midpoint location may be calculated between the clusters. A first portion of the data may be mapped to a first user and a second portion of the data may be mapped to a second user based on the calculated midpoint. Separate sleep analyses may be performed for the first user and the second user.

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: 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: This document describes techniques and devices to detect and classify human and animal presence and/or motion based on changes in the interaction of the human or animal with Wireless Local Area Network (WLAN) radio frequency (RF) signals within or about a building structure, such as a home or office. A first WLAN device transmits a sounding packet to a second WLAN device and receives an acknowledgement (ACK) of receiving the sounding packet by the second WLAN device. The first WLAN device uses the received ACK to determine Channel State Information (CSI) for an RF signal path between the first WLAN device and the second WLAN device, aggregates the determined CSI with additional CSI, and uses the aggregated CSI to determine a presence or a motion within the structure.

Abstract: A method for guiding installation of smart-home devices may include capturing, by a camera of a mobile computing device, a view of an installation location for a smart-home device; determining, by the mobile computing device, an instruction for installing the smart-home device at the location; and displaying, by a display of the mobile computing device, the view of the installation location for a smart-home device with the instruction for installing the smart-home device.

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 method for guiding installation of smart-home devices may include capturing, by a camera of a mobile computing device, a view of an installation location for a smart-home device; identifying a wire in the view of the installation location for the smart-home device; determining an instruction for connecting the wire to the smart-home device; and displaying the view of the installation location for a smart-home device with the instruction for connecting the wire to the smart-home device.

Abstract: The various embodiments described herein include methods, devices, and systems for ultrasonic sensing on electronic devices. In one aspect, a method is performed at an electronic device having memory, one or more processors, a speaker, and a microphone. The method includes, while audibly communicating with a user via the speaker and microphone: (1) sending one or more ultrasound pulses via the speaker; (2) receiving, via the microphone, one or more signals corresponding to the one or more ultrasound pulses; (3) determining positioning of the user based on the one or more received signals; and (4) adjusting one or more parameters of the speaker and/or the microphone based on the determined positioning.

Abstract: An electronic device has memory, one or more processors, a speaker, and a microphone. The device sends a first set of ultrasound chirps at a first rate via the speaker. It receives, via the microphone, a first set of signals corresponding to the first set of ultrasound chirps and being reflected from a person. The device determines based on the first set of signals that the person is in proximity to the electronic device. In accordance with the determination that the person is in proximity to the electronic device, the device sends a second set of ultrasound chirps at a second rate, faster than the first rate. It receives, via the microphone, a second set of signals corresponding to the second set of ultrasound chirps, and identifies a gesture from the person based on the second set of signals.

Abstract: A method for guiding installation of smart-home devices may include capturing, by a camera of a mobile computing device, a view of an installation location for a smart-home device; identifying a wire in the view of the installation location for the smart-home device; determining an instruction for connecting the wire to the smart-home device; and displaying the view of the installation location for a smart-home device with the instruction for connecting the wire to the smart-home device.

Abstract: Systems and methods of detecting human movement with a sensor are provided, including generating a motion event signal in response to movement detected by the sensor, and generating a parameterized curve to represent the detected motion. The parameterized curve is fit to a predetermined window of sensor data captured by the sensor to filter the motion event signal. A noise magnitude estimate and a curve fit error is determined based on the fitted parameterized curve to the predetermined window. A detection threshold value is determined based on the curve fit error, a noise source signal estimate of known noise, and zero or more noise magnitudes from other sources. Human motion is determined by correlating a true motion event signal with human motion based on a comparison between a value of a point on the parameterized curve and the detection threshold value.

Abstract: A method generates depth maps at a camera having illuminators, a lens assembly, an image sensing element, a processor, and memory. The illuminators operate in a first mode to provide illumination, the lens assembly focuses incident light on the image sensing element, the memory stores programs for execution by the processor, and the processor executes the programs to control operation of the camera. The method reconfigures the illuminators to operate in a second mode, where each of a plurality of subsets of the illuminators provides illumination of a scene separately. For each subset, the process activates the illuminators in the subset without activating illuminators not in the subset and receives reflected illumination from the scene incident on the lens assembly and focused onto the image sensing element. The measured light intensity values of the received reflected illumination at the image sensing element are transmitted to a remote server for processing.

Abstract: A method for optimizing the placement of smart-home devices may include receiving, by a mobile computing device, a location for a smart-home device, where the mobile computing device comprises a display and a camera; rendering a view of a virtual object that represents a field-of-view of the smart-home device, where the view of the virtual object is rendered based on a position corresponding to a position of the mobile computing device; and displaying, by the mobile computing device, the view of a virtual object that represents a field-of-view of the smart-home device on the display of the mobile computing device.

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 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 method generates depth maps at a camera having illuminators, a lens assembly, an image sensing element, a processor, and memory. The illuminators operate in a first mode to provide illumination, the lens assembly focuses incident light on the image sensing element, the memory stores programs for execution by the processor, and the processor executes the programs to control operation of the camera. The method reconfigures the illuminators to operate in a second mode, where each of a plurality of subsets of the illuminators provides illumination of a scene separately. For each subset, the process activates the illuminators in the subset without activating illuminators not in the subset and receives reflected illumination from the scene incident on the lens assembly and focused onto the image sensing element. The measured light intensity values of the received reflected illumination at the image sensing element are transmitted to a remote server for processing.

Abstract: A method for optimizing the placement of smart-home devices may include receiving, by a mobile computing device, a location for a smart-home device, where the mobile computing device comprises a display and a camera; rendering a view of a virtual object that represents a field-of-view of the smart-home device, where the view of the virtual object is rendered based on a position corresponding to a position of the mobile computing device; and displaying, by the mobile computing device, the view of a virtual object that represents a field-of-view of the smart-home device on the display of the mobile computing device.

Abstract: A camera system includes non-volatile memory, a lens, an image sensor to capture images of a scene within view of the lens, an IR illuminator, and a processing system. The processing system transitions the camera system from day mode to night mode when the scene is dark, and in the night mode, two operations are performed. The first operation uses the IR illuminator, the image sensor, and the memory to capture a first IR image or video of the scene and store the first IR image or video in the non-volatile memory. The second operation uses the IR illuminator, the image sensor, and the memory to identify a specular surface in the scene. This includes capturing a second IR image or video, identifying a first region of low intensity pixels, and storing, in the non-volatile memory, position parameters of the first region and designating the first region as specular.

Abstract: A method generates depth maps at a camera having illuminators, a lens assembly, an image sensing element, a processor, and memory. The illuminators operate in a first mode to provide illumination, the lens assembly focuses incident light on the image sensing element, the memory stores image data from the image sensing element, and the processor executes programs to control operation of the camera. The method reconfigures the illuminators to operate in a second mode, where each of a plurality of subsets of the illuminators provides illumination of a scene separately. For each subset, the process activates the illuminators in the subset without activating illuminators not in the subset and receives reflected illumination from the scene incident on the lens assembly and focused onto the image sensing element. The measured light intensity of the received reflected illumination at the image sensing element is stored in association with activation of the subset.