Abstract: Described are systems and methods for generating sleep scores based at least in part on the visual appearance of hypnograms. The disclosed implementations generate an embedding vector representative of the visual appearance of a hypnogram and utilize that embedding to determine a sleep score of the sleep session represented by the hypnogram. For example, a deep neural network, such as a convolutional neural network (“CNN”), may be trained with input data that includes hypnograms that have been labeled according to their sleep quality, or sleep score. The trained network may then be provided with an unlabeled hypnogram, generate an embedding representative of that hypnogram, and determine a sleep score corresponding to that hypnogram.

Abstract: A system configured to perform utterance detection using data processing techniques that are similar to those used for object detection is provided. For example, the system may treat utterances within audio data as analogous to an object represented within an image and employ techniques to separate and identify individual utterances. The system may include one or more trained models that are trained to perform utterance detection. For example, the system may include a first module to process input audio data and identify whether speech is represented in the input audio data, a second module to apply convolution filters, and a third module configured to determine a boundary identifying a beginning and ending of a portion of the input audio data along with an utterance score indicating how closely the portion of the input audio data represents an utterance.

Abstract: Devices, systems, and methods are provided for performing volume and intensity-based activity evaluations. A method may include determining, by a device, a heart rate. The method may include determining, based on the heart rate, a motion threshold amount for a time period. The method may include determining motion data. The method may include comparing the motion data to the motion threshold for the time period. The method may include determining, based on the comparison of the motion data to the motion threshold for the time period, an activity intensity level associated with the heart rate and the motion data. The method may include determining, based on the activity intensity level, an activity score. The method may include sending data indicating the activity score for presentation at a second device.

Abstract: Devices, systems, and methods are provided for performing volume and intensity-based activity evaluations. A method may include determining, by a device, a heart rate. The method may include determining, based on the heart rate, a motion threshold amount for a time period. The method may include determining motion data. The method may include comparing the motion data to the motion threshold for the time period. The method may include determining, based on the comparison of the motion data to the motion threshold for the time period, an activity intensity level associated with the heart rate and the motion data. The method may include determining, based on the activity intensity level, an activity score. The method may include sending data indicating the activity score for presentation at a second device.

Abstract: A wearable heart rate monitoring device includes an optical sensor configured to translate test light reflected from a wearer of the wearable heart rate monitoring device into a machine-readable heart rate signal. The wearable heart rate monitoring device also includes an elevation sensor configured to translate an elevation of the wearable heart rate monitoring device into a machine-readable elevation signal. The wearable heart rate monitoring device further includes a heart rate reporting machine configured to output an estimated heart rate based on at least the machine-readable heart rate signal and the machine-readable elevation signal.

Abstract: Methods and devices for blood pressure monitoring may include receiving one or more sensor measurements from the at least one sensor. The methods and devices may further include determining at least one of a first blood pressure indication using a first regression representation based on the one or more sensor measurements, a second blood pressure indication using a second regression representation based on the one or more sensor measurements, or a third blood pressure indication using a third regression representation based on the one or more sensor measurements. The methods and devices may include performing a blood pressure selection procedure using the first blood pressure indication, the second blood pressure indication, and the third blood pressure indication to determine an estimated blood pressure indication based on one or more classification characteristics. The methods and devices may further include transmitting the estimated blood pressure indication to the output device.

Abstract: A wearable device is described. The wearable device comprises: a device body configured to be secured in contact with a subject; a first sensor borne by the device body that is activatable to measure a heart rate of the subject; and control logic configured to activate the first sensor during a monitoring period during which the subject is determined to be in a sleep period.

Abstract: Methods and devices for heart rate monitoring may include determining whether an adjustment triggering condition has been met. Moreover, the methods and devices may include, in accordance with a determination that the adjustment triggering condition has been met, adjusting the second sensor indication to obtain an adjusted second sensor indication based at least on the difference between the first sensor indication and the second sensor indication. The methods and devices may further include, in accordance with a determination that the adjustment triggering condition has not been met, transmitting the second sensor indication to the signaling filter.

Abstract: Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.

Abstract: Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.

Abstract: A computationally-efficient method for a smart assistant computer to track a human includes receiving data from one or more sensors configured to monitor a physical environment. The data is computer-analyzed to recognize presence of a human in the physical environment, and upon confirming an identity of the human, a first level of computational resources of the smart assistant computer is dedicated to track the human. Upon failing to confirm the identity of the human while a known user is present, a second level of computational resources of the smart assistant computer, greater than the first level, is dedicated to determine the identity of the human. Upon failing to confirm the identity of the human while the known user is absent, a third level of computational resources of the smart assistant computer, is dedicated to determine the identity of the human.

Abstract: Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.

Abstract: Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.

Abstract: Methods and devices for correcting electrocardiogram (EKG) indication saturation may include receiving an EKG indication from at least one sensor and determining that a saturation condition has been met in response to receiving the EKG indication. Additionally, the methods and devices may include incrementing an EKG saturation level based on a determination that the saturation condition has been met. Moreover, the methods and devices may include determining whether the EKG saturation level satisfies an EKG saturation threshold. The methods and device may include disregarding at least a second EKG indication in accordance with a determination that the saturation level satisfies the EKG saturation threshold. The methods and devices may further include transmitting the second EKG indication to an output device in accordance with a determination that the saturation level does not satisfy the EKG saturation threshold.

Abstract: A wearable device is described. The wearable device comprises: a device body configured to be secured in contact with a subject; a first sensor borne by the device body that is activatable to measure a heart rate of the subject; and control logic configured to activate the first sensor during a monitoring period during which the subject is determined to be in a sleep period.

Abstract: An optical heart rate sensor includes an optical source configured to illuminate one or more blood vessels through a user's skin, an optical sensor configured to measure reflected illumination from the blood vessels, and one or more energy storage cells. A controller operates the optical source and optical sensor at a first rate of energy consumption during a first condition, and operates the optical source and optical sensor at a second rate of energy consumption during a second condition.

Abstract: A wearable device is described. The wearable device comprises: a device body configured to be secured in contact with a subject; a first sensor borne by the device body that is activatable to measure a heart rate of the subject; and control logic configured to activate the first sensor during a monitoring period during which the subject is determined to be in a sleep period.

Abstract: Computing devices and methods for controlling light output of a display are disclosed. In one example, a default brightness setting is set to an indoor light output level. A UV light sensor is activated to detect UV radiation levels. Based on determining that one or more of the UV radiation levels exceed a UV threshold, the default brightness setting is updated to correspond to an outdoor light output level that is greater than the indoor light output level. Without using information from an ambient light sensor, the display is activated from a deactivated state to illuminate at the updated default brightness setting corresponding to the outdoor light output level.

Abstract: A method to execute computer-actionable directives conveyed in human speech comprises: receiving audio data recording speech from one or more speakers; converting the audio data into a linguistic representation of the recorded speech; detecting a target corresponding to the linguistic representation; committing to the data structure language data associated with the detected target and based on the linguistic representation; parsing the data structure to identify one or more of the computer-actionable directives; and submitting the one or more of the computer-actionable directives to the computer for processing.

Abstract: Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.