Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An ear-wearable electronic device comprises a motion sensor configured to generate motion information and a first respiration rate estimated using the motion information. A PPG sensor is configured to generate PPG data and a second respiration rate estimate using the PPG data. A processor is configured to produce a respiration rate estimate using the first and second respiration rate estimates. A communication device is configured to communicate the respiration rate estimate to one or both of an external electronic device and a cloud database.

Abstract: Embodiments herein relate to embodiments herein relate to hearing assistance systems and methods for monitoring a device wearer's emotional state and status. In an embodiment, a hearing assistance system is included having an ear-worn device that can include a control circuit and a microphone in electronic communication with the control circuit. The ear-worn device can be configured to monitor signals from the microphone, analyze the signals in order to identify speech, and transmit data based on the signals representing the identified speech to a separate device. Other embodiments are also included herein.

Abstract: Embodiments herein relate to ear-wearable device systems and methods for monitoring a device wearer's emotional state and status. In an embodiment, an ear-wearable device is included having a control circuit, a microphone, and a power supply circuit. The ear-wearable device is configured to monitor signals from the microphone, identify signs of anxiety in the microphone signals, and provide a wearer of the ear-wearable device with feedback related to identified anxiety. In another embodiment, a method of monitoring anxiety with an ear-wearable device is included, the method including monitoring signals from a microphone, identifying signs of anxiety in the microphone signals, and providing a wearer of the ear-wearable device with feedback related to the identified anxiety. Other embodiments are also included herein.

Abstract: 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.

Abstract: A system for monitoring glucose blood level of a user is disclosed. The system comprising an illumination unit configured for providing coherent optical illumination on a selected inspection region on the user body, a collection unit configured for collecting light returning from the inspection region and generating a plurality of image data pieces associated with speckle patterns in the collected light, and a control unit comprising a processing utility and storage utility comprising pre-stored calibration data, the processing utility is configured for receiving input data on a plurality of image data pieces from the collection unit and for processing said input data for determining correlation functions between different image data pieces and using said correlation functions and said pre-stored calibration data for determining data on glucose blood level of the user.

Abstract: A system for monitoring glucose blood level of a user is disclosed. The system comprising an illumination unit configured for providing coherent optical illumination on a selected inspection region on the user body, a collection unit configured for collecting light returning from the inspection region and generating a plurality of image data pieces associated with speckle patterns in the collected light, and a control unit comprising a processing utility and storage utility comprising pre-stored calibration data, the processing utility is configured for receiving input data on a plurality of image data pieces from the collection unit and for processing said input data for determining correlation functions between different image data pieces and using said correlation functions and said pre-stored calibration data for determining data on glucose blood level of the user.