Starkey Laboratories, Inc.

Nitzan Bornstein

Justin R Burwinkel

Roy Talman

Matan Sivan

Neta Aviel

Jingjing Xu

Brantly Sturgeon

Majd Srour

Gal Davidson

Vladimir Maligin

One or more processing circuits may obtain context information associated with a user of one or more hearing instruments, wherein the context information is based on a first set of sensor data generated by a plurality of sensors of the one or more hearing instruments and a second set of sensor data generated by a plurality of sensors of a computing device communicatively coupled to the one or more hearing instruments. The one or more processing circuits may determine, based on at least a portion of the context information, an auditory intent of the user for a given auditory context. The one or more processing circuits may associate the auditory intent with one or more actions, such as actions to adjust one or more settings of the one or more hearing instruments. The one or more processing circuits may invoke the one or more actions associated with the auditory intent.

Context-based situational awareness for hearing instruments

Disclosed herein, among other things, are systems and methods for humidity detection for hearing device applications. A method includes calculating a capacitance between a terminal of a processor within a housing of an ear-wearable device and a designated ground plane within the housing. A temperature measurement is obtained from a sensor within the housing, and a humidity level is determined within the housing using the calculated capacitance and the obtained temperature measurement. The determined humidity level is compared to one or more predetermined humidity trigger levels, and a notification is sent to a wearer of the ear-wearable device conditioned on the comparison.

Humidity detection for hearing devices

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.

Detection of physical abuse or neglect using data from ear-wearable devices

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Tel Aviv, Israel

Nitzan Bornstein