Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: A motion sensor assembly may include a cover plate configured to be mounted to an electrical box. The cover plate may include a front surface configured to face away from the electrical box and a rear surface opposite the front surface. The cover plate may further include an aperture configured to receive a toggle or rocker type switch. A motion sensor may be coupled to the cover plate. A power source and processor may be operably coupled to the motion sensor. Furthermore, the processor may be operably coupled to a communication device.

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: A motion sensor assembly may include a cover plate configured to be mounted to an electrical box. The cover plate may include a front surface configured to face away from the electrical box and a rear surface opposite the front surface. The cover plate may further include an aperture configured to receive a toggle or rocker type switch. A motion sensor may be coupled to the cover plate. A power source and processor may be operably coupled to the motion sensor. Furthermore, the processor may be operably coupled to a communication device.

Abstract: Aspects of this disclosure describe a test strip that may include a sample input, assay circuitry, signaling circuitry, impedance circuitry, and/or energy harvesting circuitry. The sample input may be configured to receive a sample, such as a fluid sample. The assay circuitry of the test strip may be configured to perform an assay on at least a portion of the fluid sample. The signaling circuitry of the test strip may be configured to provide a modulated signal based on an output of the assay. The impedance circuitry of the test strip may be configured to present impedance changes to a touchscreen of an electronic device in accordance with the modulated signal. The test strip can then communicate data of the output of the assay (e.g., test results) to the electronic device using the touchscreen of the electronic device.

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: The techniques described herein relate to a head-mounted device that includes a frame, a motion sensor coupled to the frame, and a processor in communication with the motion sensor. The processor is configured by instructions to receive motion signals captured by the motion sensor, determine when to extract features from the motion signals, extract the features from the motion signals, generate a signal image from the features extracted from the motion signals, and process the signal image to output one or more health metrics.

Abstract: A method including transmitting, by a peripheral device communicatively coupled to a wearable device, a frequency-modulated continuous wave (FMCW), receiving, by the peripheral device, a reflected signal based on the FMCW, tracking, by the peripheral device, a movement associated with the peripheral device based on the reflected signal, and communicating, from the peripheral device to the wearable device, an information corresponding to the movement associated with the peripheral device.

Abstract: A system including a sensing device configured to (i) measure pressure of water in cold and hot water lines proximate to a fixture of a water system of a structure, and (ii) generate pressure measurement data representing the pressure of the water. The system also includes one or more processors. The system additionally includes one or more non-transitory computer readable media storing machine-executable instructions configured, when executed on the one or more processors, to perform detecting a non-cyclical pressure event corresponding to a water leak in the water system of the structure during a first time period based on an analysis of information comprising the pressure measurement data. Other embodiments are described.

Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

Abstract: A method for determining one or more vital signs of a person includes recording video images of a scene with an egocentric camera coupled to the person's body, detecting and magnifying image frame-to-image frame movements in the video images of the scene, representing the magnified image frame-to-image frame movements in the video images of the scene by a one-dimensional (1D) amplitude-versus-time series, and transforming the 1D amplitude-versus-time series representation into a frequency spectrum. The method further includes identifying one or more local frequency maxima in the frequency spectrum as corresponding to one or more vital signs of the person.

Abstract: A method for determining one or more vital signs of a person includes recording video images of a scene with an egocentric camera coupled to the person's body, detecting and magnifying image frame-to-image frame movements in the video images of the scene, representing the magnified image frame-to-image frame movements in the video images of the scene by a one-dimensional (1D) amplitude-versus-time series, and transforming the 1D amplitude-versus-time series representation into a frequency spectrum. The method further includes identifying one or more local frequency maxima in the frequency spectrum as corresponding to one or more vital signs of the person.

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: A method and system for detecting small leaks in a plumbing system is disclosed. A temperature sensor coupled to the water in the plumbing system is used to determine if there is a leak. During times of inactivity for fixtures in the plumbing systems, a flow sensor might measure usage of water that would indicate a leak. For very small leaks, the flow is below a minimum measurable flow of the flow sensor. Embodiments of the invention measure temperature of water within a pipe coupled to the plumbing system. Temperature will generally decay in a particular predicable way when there is flow as the temperature of water upon entry to the building is lower than the air temperature within the building. Signal processing, machine learning and/or statistical approaches are used to analyze the temperature and optionally flow and/or pressure over time to determine when a leak is likely.

Abstract: A computing device, such as a wearable device, may include at least two electrodes mounted on a body. The computing device may determine an electrical signal associated with a circuit that includes the at least two electrodes and the user. A pressure applied to at least one electrode of the at least two electrodes may be determined from the electrical signal, and at least one function of the computing device may be implemented, based on the pressure.

Abstract: By monitoring pressure transients in a liquid within a liquid distribution system using only a single sensor, events such as the opening and closing of valves at specific fixtures are readily detected. The sensor, which can readily be coupled to a faucet bib, transmits an output signal to a computing device. Each such event can be identified by the device based by comparing characteristic features of the pressure transient waveform with previously observed characteristic features for events in the system. These characteristic features, which can include the varying pressure, derivative, and real Cepstrum of the pressure transient waveform, can be used to select a specific fixture where a valve open or close event has occurred. Flow to each fixture and leaks in the system can also be determined from the pressure transient signal. A second sensor disposed at a point disparate from the first sensor provides further event information.

Abstract: A computing device, such as a wearable device, may include at least two electrodes mounted on a body. The computing device may determine an electrical signal associated with a circuit that includes the at least two electrodes and the user. A pressure applied to at least one electrode of the at least two electrodes may be determined from the electrical signal, and at least one function of the computing device may be implemented, based on the pressure.

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.