Resmed Sensor Technologies Limited

Resmed Pty Ltd

Resmed Limited

Redmond Shouldice

Stephen McMahon

Alberto Zaffaroni

Conor Joseph Heneghan

Graeme Alexander Lyon

Damien O'Rourke

Niall Fox

Paul Phillips

Gareth McDarby

Michael Wren

Luke Gahan

Marc Lavelle

Przemyslaw Szkot

David Mulligan

Matthew Norton

Colin Lawlor

Emer Doheny

Emma Marie Meade

Philip De Chazal

Ciaran Gerard McCourt

Gregory Robert Peake

Priyanshu Gupta

Roxana Tiron

Anna Rice

Conor Heneghan

Kieran Grennan

Stephen Mcmahon

Ryan Hernandez

Timothy Atkinson

Stephen Dodd

Tony Fagan

Ryan Mathew Hernandez

Colin Bradley Kennedy

Niall O'Mahony

Shawna Wilkerson

Sam Coffey

Dylan Hermes Da Fonseca Beadle

Matt Norton

Gareth Mcdarby

Jay Kapadia

Ciaran Gerard Mccourt

Katherine Molony

Niall O'MAHONY

Luca Cerina

Rehana Nathwani

Marta Perez Denia

A method for canceling noises generated by a respiratory system that is configured to supply pressurized air to a user during a sleep session comprises generating sound data using a microphone; generating, using or one or more sensors, respiration data associated with respiration of the user of the respiratory system; analyzing the sound data to determine if noise associated with operation of the respiratory system is occurring; and causing a speaker to emit sound waves based at least in part on (i) the sound data, (ii) the respiration data, (iii) data related to the operation of the respiratory system, or (iv) any combination of (i), (ii), and (iii), the emitted sound waves being configured to acoustically cancel at least a portion of the noise associated with operation of the respiratory system.

Systems and methods for active noise cancellation

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

Methods and devices provide physiological movement detection with active sound generation. In some versions, a processor may detect breathing and/or gross body motion. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the sound signal from the user. The processor may process the reflected sound, such as by a demodulation technique. The processor may detect breathing from the processed reflected sound signal. The sound signal may be produced as a series of tone pairs in a frame of slots or as a phase-continuous repeated waveform having changing frequencies (e.g., triangular or ramp sawtooth). Evaluation of detected movement information may determine sleep states or scoring, fatigue indications, subject recognition, chronic disease monitoring/prediction, and other output parameters.

Apparatus, system, and method for detecting physiological movement from audio and multimodal signals

Methods and apparatus provide monitoring of a sleep disordered breathing state of a person such as for screening. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) () may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of sleep disordered breathing. The processor may generate a sleep disordered breathing indicator based on the measure(s) of sleep disordered breathing. Some versions may evaluate sensing signal(s) to generate indication(s) of cough event(s) and/or cough type.

Methods and apparatus for detection of disordered breathing

Methods and devices provide physiological movement detection, such as breathing, cardiac and/or gross body motion, with active sound generation using electronic processing device(s). The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique. The sound signal may be produced as a dual tone frequency modulation continuous wave signal. Evaluation of detected movement information may determine sleep states or scoring, fatigue indications, subject recognition, chronic disease monitoring/prediction, and other output parameters.

Apparatus, system, and method for health and medical sensing

A system for predicting adoption of a prescribed treatment plan by an individual includes a data repository, a memory storing instruction, and a control system to execute the instructions. The data repository is communicatively coupled to a network and includes a plurality of storage devices storing data. The control system receives at least a portion of the data stored in the data repository. The at least a portion of the data is associated with the individual. The control system uses the machine learning adoption prediction algorithm to process the received at least a portion of the data to determine a likelihood that the individual will adopt the prescribed treatment plan. Based at least in part on (i) the prescribed treatment plan and (ii) the determined likelihood that the individual will adopt the prescribed treatment plan, the control system generates a personalized treatment adoption plan for the individual.

Systems and methods for predicting adoption of a therapy

Disclosed are methods, systems, and devices for providing a personalized humidification level. A control system receives, from a first sensor, one or more environmental parameters regarding conditions of the environment. The control system receives, from a second sensor, one or more physiological parameters associated with a user within the environment. The control system determines an action associated with a desired change in the humidity within the environment based, at least in part, on the one or more environmental parameters and the one or more physiological parameters. The control system causes, at least in part, a performance of the action associated with the change in the humidity in the environment based, at least in part, on moisture outputted by a humidifier module. The methods and devices perform the same functionality as the control system.

Systems, methods and devices for smart humidification

Methods and apparatus provide physiological movement detection, such as gesture, breathing, cardiac and/or gross motion, such as with sound, radio frequency and/or infrared generation, by electronic devices such as vehicular processing devices. The electronic device in a vehicle may, for example, be any of an audio entertainment system, a vehicle navigation system, and a semi-autonomous or autonomous vehicle operations control system. One or more processors of the device, may detect physiological movement by controlling producing sensing signal(s) in a cabin of a vehicle housing the electronic device. The processor(s) control sensing, with a sensor, reflected signal(s) from the cabin. The processor(s) derive a physiological movement signal with the sensing signal and reflected signal and generate an output based on an evaluation of the derived physiological movement signal. The output may control operations or provide an input to any of the entertainment system, navigation system, and vehicle operations control system.

Apparatus, system, and method for physiological sensing in vehicles

Systems and methods are disclosed for categorizing and/or characterizing a user interface. The systems and methods include generating acoustic data associated with an acoustic reflection of an acoustic signal, the acoustic reflection being indicative of, at least in part, one or more features of a user interface coupled to a respiratory therapy device via a conduit. The systems and methods further include analyzing the generated acoustic data. The systems and methods further include categorizing and/or characterizing the user interface based, at least in part, on the analyzed acoustic data.

Systems and methods for categorizing and/or characterizing a user interface

Devices, systems, and methods are disclosed. The devices, systems, and methods detect one or more parameters with respect to movement of a user, cardiac activity of the user, audio associated with the user, or a combination thereof during a sleep session of the user; process the one or more parameters to determine a sleep status of the user, the sleep status being at least one of awake, asleep, or a sleep stage; and calculate an apnea-hypopnea index for the user during the sleep session based, at least in part, on the sleep status.

Sleep status detection for apnea-hypopnea index calculation

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

Methods and devices provide physiological movement detection, such as gesture, breathing, cardiac and/or gross body motion, with active sound generation such as for an interactive audio device. The processor may evaluate, via a microphone coupled to the interactive audio device, a sensed audible verbal communication. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the generated sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique, to derive a physiological movement signal. The processor may generate, in response to the sensed audible verbal communication, an output based on an evaluation of the derived physiological movement signal.

Apparatus, system, and method for motion sensing

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

Systems and methods assist with managing a chronic disease of a user such as a chronic respiratory or cardiac disease. The system may include a physiological monitor adapted to be carried by the user and operative to sense a physiological parameter of the user by generating one or more signals. The system may include a management device operatively coupled with the physiological monitor to receive the signal(s) and derive the physiological parameter(s) of the user. The management device, such as with an included processor, may be configured to analyze the physiological and/or environmental parameters to detect a trigger pattern of the parameters, the trigger pattern indicative of a probable event of exacerbation of the chronic respiratory and/or cardiac condition. The management device may then generate automated responses based on the trigger pattern such as by providing instructions for activities and/or treatment for the chronic condition.

Systems and methods for monitoring and management of chronic disease

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Dublin, Ireland

Redmond Shouldice