Fitbit, Inc.

Eko Health, Inc.

Eko Devices, Inc.

Subramaniam Venkatraman

Shelten Gee Jao Yuen

Heiko Gernot Albert Panther

Conor Joseph Heneghan

Logan Niehaus

Alexandros A Pantelopoulos

Jacob Antony Arnold

Connor Landgraf

Hao-Wei Su

Dan Freschl

James Park

Shelten Yuen

Jonathan David Charlesworth

Kevin Pu Weekly

Eric Van Albert

Peter W Richards

Timothy Roberts

Andrew Larsen Axley

Chris Hanrahan Sarantos

Daniel Joel Freschl

Sebastian Joseph Capella

Jonathan Wonwook Kim

Michael Thompson

John Prince

John Maidens

Theo Brower

Shanmugam Muruga Palaniappan

Michael Childs

Niladri Bora

Peter Duyan

Félix Antoine Turgeon

Eugene Gershtein

Eric M Foxlin

Philip Goolkasian

Tyler Crouch

Johnathan Charlesworth

Richard N Blair

Neraj Bobra

George Mathew

Daniel Barbosa

Bryan Hord

John Zorko

Jordan Crivelli-Decker

Ling Guo

Neal Donovan

Jaclyn Leverett Wasson

Darius Mostowfi

Daniel Freschl

Veera Venkata Sesha Sai Vaddi

Scott Suarez

Timothy M Roberts

Jackie Leverett Wasson

Steve L Pham

The present description relates generally to methods and systems for power management of a digital (e.g., electronic) stethoscope. In one example, an electronic stethoscope includes a chestpiece configured to be positioned on a patient, the chestpiece including a sensor, wherein the chestpiece further includes a computer processing unit (CPU) operatively coupled to a memory storing instructions that, when executed by the CPU, cause the CPU to automatically activate the electronic stethoscope in response to detecting a touch input via the sensor.

Systems and methods for power management of an electronic stethoscope

The present description relates generally to methods and systems for detecting cardiovascular conditions using a foundation model. In one example, a method includes obtaining a synchronized ECG signal and a PCG signal from a patient, converting the PCG signal to a PCG mel-spectrogram, entering the ECG signal and the PCG mel-spectrogram as input to a trained specialized model configured to output a classification output based on the ECG signal and the PCG mel-spectrogram, the trained specialized model trained with labeled ECG and PCG signal pairs using a foundation model trained with unlabeled ECG and PCG signal pairs, and storing the classification output in memory and/or displaying the classification output on a display device.

Systems and methods for a foundation model for cardiac data

In an embodiment, a data processing method comprises obtaining one or more photoplethysmography (PPG) signals from one or more PPG sensors of a monitoring apparatus, the PPG signals being generated based upon optically detecting pulsed variations in blood flow; obtaining a motion sensor signal from a motion sensor in the monitoring apparatus; identifying, based upon the motion sensor signal, one or more periods of motion (e.g., low motion) of the monitoring apparatus; and selectively obtaining and storing segments of the PPG signals based on a temporal relationship between the segments of the PPG signals and the identified periods of motion.

System and method for characterizing cardiac arrhythmia

A wearable device for tracking swim activities of a user is provided. The wearable device may include one or more sensors configured to generate sensor data, and based on the sensor data, the wearable device may determine swim metrics such as swim stroke count, swim stroke type, swim lap count, and swim speed. The determined swim metrics may be filtered based on one or more swim periods during which the user is likely to have been swimming. The wearable device may determine such swim periods based on the sensor data and/or the determined swim metrics.

Automatic detection and quantification of swimming

The present description relates to methods and systems for a medical dictation. In one example, a stethoscope includes a first microphone positioned to capture physiological sounds of a patient, a second microphone positioned to capture ambient sounds, one or more processors, and memory storing instructions executable by the one or more processors to: during a stethoscope mode, obtain a first signal from the first microphone and a second signal from the second microphone, process the first signal to capture a physiological sound signal, including performing noise cancellation on the first signal based on the second signal, and transmit the physiological sound signal to an external computing device and/or a speaker of the stethoscope; and during a dictation mode, obtain the second signal from the second microphone, process the second signal to capture a voice signal, and transmit the voice signal to the external computing device.

Systems and methods for dictation with a digital stethoscope

Methods and systems for a hybrid analog-digital stethoscope is herein provided. In one example, an electronic stethoscope comprises a chestpiece including a sound collecting interface and a plurality of electronic components, wherein the plurality of electronic components includes one or more microphones and one or more speakers; an output tube coupled to the chestpiece via a connector comprising a mode switching port, wherein the output tube, when the mode switching port is in a first position for digital mode, is in acoustic communication with the one or more speakers and, when the mode switching port is in a second position for analog mode, is in acoustic communication with the sound collecting interface.

Systems and methods for a hybrid analog-digital stethoscope

The present description relates generally to methods and systems for wireless communication between a digital (e.g., electronic) stethoscope and other electronic devices (e.g., computing and listening devices). In one example, a method comprises operating an electronic stethoscope in one of an internal digital mode and a wireless digital mode based on a detected user action. In this way, the electronic stethoscope may be quickly adjusted between projecting sound via integrated speakers and transmitting sound to an external electronic device, enabling efficient remote monitoring of a patient.

Systems and methods for electronic stethoscope wireless auscultation

Various methods and systems are provided for monitoring a pulmonary artery pressure and cardiac synchronization of a subject. In one example, a method includes acquiring at least one of electrocardiogram (ECG) data, phonocardiogram (PCG) data, and seismocardiogram (SCG) data from a subject via a digital stethoscope, inputting one or more of the ECG data, the PCG data, and the SCG data into a machine learning algorithm, and estimating at least one of a pulmonary artery pressure and a cardiac synchronization of the subject using the machine learning algorithm. In this way, the pulmonary artery pressure and the cardiac synchronization may be estimated using artificial intelligence and inputs that are non-invasively measured by the digital stethoscope, allowing conditions like heart failure and pulmonary hypertension to be more simply and reliably detected and monitored.

Methods and systems for pulmonary artery pressure and cardiac synchronization monitoring

Various methods and systems are provided for an automated clinical exam workflow. In one example, method comprises performing a signal quality check of an electronic stethoscope at a first recording location on a subject, recording physiological data for an exam at the first recording location via the electronic stethoscope in response to the signal quality check satisfying a quality threshold, and outputting a signal quality alert in response to the signal quality check not satisfying the quality threshold. In this way, clinically relevant data may be obtained with reduced user effort and fewer manual inputs.

Methods and systems for automated clinical workflows

Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.

Detecting and measuring snoring

A method and apparatus for providing biofeedback during a meditation exercise are disclosed. In one aspect, the wearable device includes one or more biometric sensors and a user interface. The method may involve prompting the user, via the user interface, to perform a meditation exercise, the meditation exercise being associated with a target physiological metric related to the physiology of the user. The method may involve measuring, based on output of at least one of the one or more biometric sensors, a physiological metric of the user during the meditation exercise. The method may involve determining a performance score indicating the user's performance during the meditation exercise based on comparing the measured physiological metric with the target physiological metric. The method may involve providing, via the user interface, based on the performance score, feedback information indicative of the user's performance during the meditation exercise.

Method and apparatus for providing biofeedback during meditation exercise

Approaches described herein can determine one or more breathing phase patterns over a period of time using audio data captured by at least one microphone. The audio data can include one or more snores. A breathing phase pattern included within the period of time can be determined based at least in part on sensor data captured by one or more sensors in the electronic device. A determination can be made that a first breathing phase pattern represented by the audio data and a second breathing phase pattern represented by the sensor data are correlated. A determination can be made that the first breathing phase pattern represented by the audio data and the second breathing phase pattern represented by the sensor data both correspond to a user wearing the electronic device.

A wearable computing device includes an electronic display with a configurable brightness level setting, a physiological metric sensor system including a light source configured to direct light into tissue of a user wearing the wearable computing device and a light detector configured to detect light from the light source that reflects back from the user. The device may further include control circuitry configured to activate the light source during a first period, generate a first light detector signal indicating a first amount of light detected by the light detector during the first period, deactivate the light source during a second period, generate a second light detector signal indicating a second amount of light detected by the light detector during the second period, generate a physiological metric based at least in part on the first light detector signal and the second light detector signal, and modify the configurable brightness level setting based on the second light detector signal.

Ambient light determination using physiological metric sensor data

Growing community of inventors

Walnut Creek, CA, United States of America

Subramaniam Venkatraman