The present invention relates to a method of operating a smart mattress system for preventing snoring which can stop snoring of a user, wherein an air mattress and an air pillow interlock with a user terminal so that a user can easily control the air mattress and the air pillow.

The invention relates to a thermometer-shaped oral device for measuring respiratory sounds. The oral device comprises a mouthpiece with a metallic tip for placement under the tongue and a neck around which the lips seal the mouth closed, preventing outside noises from interfering with the measurement of the respiratory sounds. The oral device performs the auscultatory function traditionally performed by a stethoscope. The metallic tip may contain a thermistor for oral temperature measurement, and in various embodiments the oral device may further comprise other vital signs sensors. The oral device may be in communication with a cloud server, as part of a system for remote auscultation by a physician and/or analysis of auscultation and/or other vital signs by a healthcare bot for detecting progression of respiratory or other diseases.

A method for detecting wheeze from an audio respiratory signal comprises capturing the audio respiratory signal from a subject using a microphone. Further, the method comprises recognizing a plurality of breath cycles and a plurality of breath phases from the audio respiratory signal and detecting wheezing from the plurality of breath cycles and the plurality of breath phases. The detecting comprises analyzing a block of interest in the audio respiratory signal, wherein the block of interest comprises a plurality of frames. The detecting further comprises calculating an auto-correlation function (ACF) for each frame in the block and determining a maximum value of the ACF calculated for each frame in the block. Finally, the detecting comprises analyzing the maximum value to detect if wheezing is present in the block.

A contact sensor for monitoring breathing of a subject, comprising: a microphone housing defining a first acoustic cavity, a MEMS microphone disposed within the first acoustic cavity; a second acoustic cavity separated from the first acoustic cavity by a cavity wall having a front surface and a rear surface, the second acoustic cavity at least partially defined by the front surface of the cavity wall; an acoustic conduit formed between the first acoustic cavity and the second acoustic cavity through the cavity wall; and a pressure relief vent having a first end terminating at the second acoustic cavity and a second end terminating outside of the second acoustic cavity.

Example inventions detect an occurrence of disturbance during a sleep period of a user. Examples receive data corresponding to a single parameter of the user during the sleep period, determine patterns in the data, and based on the patterns, determine a sleep disturbance severity index (“SDSI”) over the sleep period.

The invention provides a magnetic inductive sensing device (30) comprising a loop antenna (10) for inductively coupling with electromagnetic (EM) signals emitted from a medium in response to stimulation of the medium with electromagnetic excitation signals. The device includes an electromagnetic shield (36) element which is arranged such as to intercept electromagnetic signals travelling to or from the antenna. The shield element is formed of conductive material such as to block electrical field components of incident signals but further incorporates a non-conductive gap in the material so as to prevent the formation of eddy currents. A loop of the antenna is broken by an opening, the opening being bridged by a capacitor, and the device comprises a signal processing means which is electrically coupled to the antenna via only a single point of the antenna, located to one side of the opening.

A method of characterizing a patient's disordered breathing during a sleeping period includes performing a first partial characterization of a time axis of an audio signal in order to learn the most prominent and highly relevant events. Only at a later stage, i.e., after sufficient observation of the highly relevant events, is a full segmentation of the entire time axis actually carried out. Linear prediction is used to create an excitation signal that is employed to provide better segmentation than would be possible using the original audio signal alone. Warped linear prediction or Laguerre linear prediction is employed to create an accurate spectral representation with flexibility in the details provided in different frequency ranges. A resonance probability function is generated to further characterize the signals in order to identify disordered breathing. An output includes a characterization in any of a variety of forms of identified disordered breathing.

In various embodiments, a system for cleaning, marking, and/or interpreting physiological data is disclosed. The system includes a memory having instructions stored thereon, and a processor configured to read the instructions to: receive a training data set comprising physiological data including labeled events corresponding to a predetermined portion of the physiological data, generate a trained artificial intelligence (AI) model configured to identify events within device data, and identify at least one physiological event within a target device data set based on the trained AI model. The trained AI model is generated using an iterative training process based on the training data set.

A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.

A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.