Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Haithem Albadawi

Zongyi Liu

Han Yee Mimi Fung

Amish Patel

Larry Russell Butcher

Michael Tedesco

Christopher Nuesmeyer

Farah Shariff

David Robert Cole

Hanhan Wang

Maulik Majmudar

Jared Molton

William Scott Lamond

Pekka Pietari Pulkkinen

Yan Zhang

Vinod L Hingorani

Cyril Arokiaraj Arool Emmanuel

Steven Sensarn

Andreas Caduff

Sichao Shi

Anantharanga Prithviraj

Vashishtha Rajendra Patil

Qing Xie

Shunan Li

Abhishek Bafna

Samuel Marc Denton

Chaitra Hegde

Chris Reidy

Feross Salameh

A wearable device detects ambient ultraviolet light and provides output related to lengths of time for safe exposure and health benefits. Orientations of the device, determined by an orientation sensor, intensity of ambient light determined by ultraviolet and visible light sensors, or both are used to determine a current position of the sun or other source of ultraviolet light. Data from the sensors and position of the sun are used to select a gain level for the ultraviolet light sensor or a corrective factor to be applied to signals from the ultraviolet light sensor. At a subsequent time, detected intensity of ambient ultraviolet light may be used in combination with external data relating to the location of the device to determine an output, such as recommended times for exposure. Other sensors may determine physiological characteristics of the user, which may also be used to determine personalized recommended exposure times.

Systems for determining user-specific ultraviolet light exposure data

A photoplethysmogram (PPG) signal may be used to determine values representing a pulse rate, oxygen saturation, or respiration rate of a user by determining data points of the signal that correspond to local maxima (peaks) and local minima (valleys). Peaks and valleys are identified based on the directional components of sets of points that precede and follow the peak or valley, and values for one or more of those points relative to the value for the peak or valley. Once peaks and valleys of the signal are identified, lengths of time between adjacent peaks or valleys may be used to determine pulse rate. Differences in values represented by an adjacent peak and valley for different wavelengths of the PPG signal may be used to determine oxygen saturation. Changes in distances between adjacent peaks or valleys and amplitude of the PPG signal over time may be used to determine respiration rate.

Systems for determining physiological values based on signal characteristics

Devices, systems, and methods are provided for non-intrusive sleep efficiency analysis and recommendations using heart data. A method may include receiving, by a device, photoplethysmography (PPG) data associated with a heart signal. The method may include determining a first portion of the PPG data that fails to exceed a threshold frequency and a second portion of the PPG data that exceeds the threshold frequency. The method may include determining a ratio of the first portion to the second portion. The method may include determining, based on the PPG data, an inspiratory flow, and determining, based on the ratio and the inspiratory flow, a forecasted sleep efficiency score. The method may include determining a recommended activity associated with increasing the forecasted sleep efficiency score. The method may include presenting the forecasted sleep efficiency score and the recommended activity.

Non-intrusive sleep efficiency analysis and recommendations using heart data

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.

Volume and intensity-based activity evaluations for devices

Techniques for detecting user activity and repetitions (rep) counting are described. A system may dynamically determine a threshold value for counting reps. The threshold value may be based on an activity predicted by the system using sensor data. The threshold value may be based on a rep rate. Changes in the threshold value may enable more accurate rep counting when a user is performing an activity that involves taking reps, such as, walking, running, rowing bicycling, etc., and may further cease counting of reps when a user is performing an activity that does not involve taking repetitions but may result in sensor data that triggers rep counting.

Activity classification and repetition counting

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.

Sleep scores

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.

Performing utterance detection using convolution

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.

Heart rate correction for relative activity strain

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.

Blood pressure determinations

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.

Determining resting heart rate using wearable device

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.

Photoplethysmography (PPG) indication discontinuity detection and correction

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.

Intelligent assistant

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.

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Redmond, WA, United States of America

Haithem Albadawi