Resmed Sensor Technologies Limited

Fitbit, Inc.

University College Dublin

Biancamed Limited

Conor Joseph Heneghan

Alberto Zaffaroni

Philip De Chazal

Redmond Shouldice

Stephen McMahon

Conor M Hanley

Subramaniam Venkatraman

Niall Fox

Gareth McDarby

Chris Hanrahan Sarantos

Paul Phillips

Xi Zhang

Shelten Gee Jao Yuen

Andrew Larsen Axley

Alexandros A Pantelopoulos

Logan Niehaus

Peter W Richards

Jonathan David Charlesworth

Keith Adam Wong

Dan Stefan Tudose

Jaclyn Leverett Wasson

Atiyeh Ghoreyshi

Belen Lafon

Peter Colin Dess

Zachary Todd Beattie

Stephen James Redmond

Hao-Wei Su

Damien O'Rourke

Luke Gahan

Lindsey Michelle Sunden

Michael Wren

Kieran Conway

Allison Maya Russell

Daniel Joel Freschl

Anjian Wu

Alexander Statan

Philip Foeckler

Lin Yang

Steven Paul Farrugia

Jeffrey Andrew Fisher

Klaus Henry Schindhelm

Jacob Antony Arnold

Nicholas Myers

Thomas Samuel Elliot

Lukas Bielskis

Jaydip Das

Man-Chi Liu

Michael Waclaw Colefax

Jose Roberto Melgoza

Faizan Javed

Aniket Sanjay Deshpande

Matthew Norton

David Mulligan

Marc Lavelle

Colin Lawlor

Emer O'Hare

Trevor Murray

Spencer Terry Wood

Hamed Vavadi

Jonathan Wonwook Kim

Suraj Gowda

Delisa Lopez

Adrienne M Tucker

Elaine Sheridan

Ciaran Gerard McCourt

Corey Stephen O'Connor

Radu Dobroiu

Allison Shih Wu

Rami Khushaba

Patrick Celka

Elena Perez

Amir Bahador Farjadian

Jacob Anthony Arnold

Stephen Mcmahon

Eric C-p Chua

Stephen Redmond

Alexandru-Mihai Ş Olot

Priya Vijay Sheth

Benjamin B Perkins

Juan Ignacio Correa Ramírez

Samuel Barry Tellman

Eric Chern-Pin Chua

Johnathan Charlesworth

Alexandru-Mihai Şolot

Aravind Natarajan

Emer O'hare

Gareth Mcdarby

Ciaran Gerard Mccourt

Samy Ahmed Abdel-Ghaffar

Stuart Blanché Truax

Julia Marie Dorothea Thomsen

Adrienne Mason Tucker

Alexandru-Mihai Solot

Danel Joel Freschl

An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

Apparatus, system and method for chronic disease monitoring

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

An apparatus, system, and method monitors the motion, breathing, heart rate and sleep state of subjects, e.g., humans, in a convenient, non-invasive/non-contact, and low-cost fashion. More particularly, the motion, breathing, and heart rate signals are obtained through processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Periods of sleep disturbed respiration, or central apnea can be detected through analysis of the respiratory signal. The mean heart rate, and derived information, such as the presence of cardiac arrhythmias can be determined from the cardiac signal. Motion estimates can be used to recognize disturbed sleep and periodic limb movements. The sleep state may be determined by applying a classifier model to the resulting streams of respiratory, cardiac and motion data. A means for display of the sleep state, respiratory, cardiac, and movement status may also be provided.

Apparatus, system, and method for monitoring physiological signs

An aspect of the disclosure pertains to detecting a position of a blood pressure measurement device. An inflatable bladder of the blood pressure measurement device defines, at least in part, a pressurizable volume. The inflatable bladder may be inflated to pressurize a user's appendage and temporarily occlude blood flow in the user's appendage. A pump may initiate inflation of the inflatable bladder when one or more accelerometers and/or one or more proximity sensors determine that the blood pressure measurement device is within sufficient proximity or elevation to a user's heart and stationary.

Position detection of blood pressure device

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

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 system monitors fatigue of a user. The system () may include one or more data sources, such as a non-obtrusive sleep sensor, configured to generate objective sleep measures of the user. The system may also include a fatigue monitoring module, which may be configured to generate an assessment, such as in one or more processors, of the fatigue state of the user based on the data from the one or more data sources.

Fatigue monitoring and management system

Disclosed herein is a ring-shaped wearable device for detecting biometrics with a light source and a photodetector directed towards a digit wearing the ring-shaped device. The ring can thus detect oxygen saturation of a wearer based on light transmitted through the wearer's finger. The ring can include power saving measures to extend the battery life. A motion sensor can help determine opportune moments for data collection such as when the wearer is still. The motion sensor can be used to remove noise from the data caused by motion. After data is collected or during data collection, the ring can wirelessly communicate the data to another portable electronic device such as a phone or watch.

Ring for optically measuring biometric data

A processing system includes methods to promote sleep. The system may include a monitor such as a non-contact motion sensor from which sleep information may be determined. User sleep information, such as sleep stages, hypnograms, sleep scores, mind recharge scores and body scores, may be recorded, evaluated and/or displayed for a user. The system may further monitor ambient and/or environmental conditions corresponding to sleep sessions. Sleep advice may be generated based on the sleep information, user queries and/or environmental conditions from one or more sleep sessions. Communicated sleep advice may include content to promote good sleep habits and/or detect risky sleep conditions. In some versions of the system, any one or more of a bedside unitsensor module, a smart processing device, such as a smart phone or smart device, and network servers may be implemented to perform the methodologies of the system.

Methods and systems for sleep management

Health information for a woman can be used to predict timing of events related to the woman's menstrual cycle. If available, historical cycle information for a woman can be used to predict upcoming cycle events, such as the start and stop of menstruation. To improve the accuracy of those predictions, one or more health metrics are monitored for the woman that can be correlated with the menstrual cycle. These can include, for example, the resting heart rate (RHR), blood oxygen concentration (SpO) level, and hemoglobin concentration, among other such options. The metrics are monitored over time to determine patterns that can be correlated with menstrual cycle. This information can then be used to update the predictive model, as well as to update individual event predictions. Information about the predictions, and updates to the predictions, can be surfaced accordingly.

Menstrual cycle tracking

A system, computer-readable storage medium, and a method capable of, directly or indirectly, estimating sleep states of a user based on sensor data from movement sensors and/or optical sensors.

Methods and systems for labeling sleep states

Physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly.

Determining mental health and cognitive state through physiological and other non-invasively obtained data

Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor () may access sensor data signals related to bodily movement and/or respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include one or more of variability of respiration rate and variability of respiration amplitude. A processor may then determine a sleep stage based on one or more of respiration variability and bodily movement, such as with a combination of both. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

System and method for determining sleep stage

Growing community of inventors

Campbell, CA, United States of America

Conor Joseph Heneghan