A computer system for intra-ear sensing and stimulating receives, health data, from an earbud sensor. The system repeatedly calculates an exponential moving average (EMA) of a moving window for the received health data. The system compares each calculated exponential moving average with a lower threshold value and an upper threshold value. The upper threshold value and the lower threshold value are determined based, at least in part, upon a saturation level associated within an amplifier performing the adaptive gain control. When the calculated exponential moving average is larger than the upper threshold, the system decreases a gain associated with the amplifier. When the calculated exponential moving average is smaller than the lower threshold, the system increases a gain associated with the amplifier.

The present invention relates to an ear-wearable device (100) comprising: a plurality of neuro-buds (100a), each neuro-bud (100a) comprising: a housing (102), a hub (104) disposed in the housing (102), a plurality of springs (2, 2a-2h) disposed on the hub (104), and a biosensor electrode (1, 1a-1h) disposed on each spring (2, 2a-2h) and adapted to be in contact with an ear canal for detecting at least one physiological parameter of a user, wherein the plurality of springs (2, 2a-2h) are adapted to expand for extending the biosensor electrode (1, 1a-1h) to establish contact with the ear canal and to contract for retracting the biosensor electrode (1, 1a-1h) to break the contact; and a controller (100, 300) in communication with the biosensor electrode (1, 1a-1h) and adapted to: receive at least one value of the at least one physiological parameter detected by the biosensor electrode (1, 1a-1h), and generate health insights of the user based on the at least one physiological parameter.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

Each accessory device in a set of accessory devices may establish a respective communication link between the accessory device and an ear-wearable device. A particular accessory device in the set of accessory devices may receive data via the communication link between the particular accessory device and the ear-wearable device. The data comprise information generated based on sensor signals from sensors that monitor a user of the ear-wearable device. The accessory devices perform a health monitoring activity based on the data.

The body-worn wireless two-way communication system comprises a non-invasive and non-implanted system which allows for clear wireless two-way communications. This system is generally comprised of a mouthpiece component, relay component, infrastructure communication device, an optional earpiece component, and an optional system control which may interface with the relay component.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

An acquisition system includes a processor, one or more sensors operatively coupled to the processor where the one or more sensors acquire at the ear, on the ear or within an ear canal, one or more of acceleration, blood oxygen saturation, blood pressure or heart-rate, and the one or more sensors configured to monitor a biological state or a physical motion or both for an event. The event can be a detection of a discrepancy when compared with a set of reference data by the one or more sensors or the biological state or the event can be one of a detection of an abrupt movement of a headset operatively coupled to the processor, a change in location of an earpiece operatively coupled the processor, a touching of the headset, a recognizing of a voice command, a starting or ending of a phone call, or a scheduled time.

An OCT apparatus and method for characterization of a fluid adjacent to a tympanic membrane has a low coherence source which is coupled to a splitter which has a measurement path and a reference path. The reference path is temporally modulated for length, and the combined signals from the reference path and the measurement path are applied to a detector. The detector examines the width of the response and the time variation when an optional excitation source is applied to the tympanic membrane, the width of the response and the time variation forming a metric indicating the viscosity of a fluid adjacent to the tympanic membrane being measured.

Embodiments may provide multimodal diagnostic systems and methods for detecting neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD), depression, PTSD, schizophrenia, dementia and many others. For example, a system for monitoring brain activity may comprise a plurality of sensors, each adapted to monitor a physical or physiological parameter and output a signal representing the monitored physical or physiological parameter, wherein the plurality of sensors includes at least one sensor configured to monitor a brain activity parameter, a data collection device adapted to receive the plurality of signals from the plurality of sensors and to process the signals to form digital data representing the monitored physical or physiological parameters, and a data processing device adapted to process digital data representing the monitored physical or physiological parameters to determine presence of a neurological disorder or condition.