Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.

Aspects of the present disclosure provide methods, apparatuses, and systems for accurately determining sleep and wake onset based on a user's personalized physiological parameters for sleep and wake. First, a user is determined to be asleep using population level data. Thereafter, sensor collected data is used to determine the user's distribution of values of a physiological parameter when the user is asleep. This distribution of values is then used, instead of population-level data, to determine the user is asleep in real-time. As a result, the content and interventions are provided to help users get back to sleep. Further, the described techniques allow more accuracy in determining sleep statistics which can guide recommended interventions and therapies.

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.

To provide an electronic device capable of recognizing a user's emotion with a high accuracy. The electronic device includes a detection device, an arithmetic device, and a housing. The housing includes a space at a position overlapping with a user's nose when the user wears the electronic device. The detection device is located between the housing and the user's nose. The detection device has a function of obtaining user's data on an emotion of the user and outputting the user's data to the arithmetic device. The arithmetic device has a function of generating display data based on the user's data and outputting the display data.

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.

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

The present disclosure pertains to a wearable electronic device for the novel sensing of physiologically presented symptoms of stress corresponding to changes in a finger skin temperature biomarker.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

A method and system for generating an optimal audio stimulus for achieving a target brain state value for a brain state. The method and system can be used to generate one or more brain state models which can decode brain activity signals to predict brain state values. The brain state models can be applied to brain activity signals captured while users are performing tasks with an audio stimulus. Audio features of the audio stimulus can be extracted. An audio-brain model can be trained on the predicted brain state values and the audio features. From the trained audio-brain model, the optimal audio stimulus can be generated.

The present disclosure relates to materials and methods for evaluating acoustic startle response (ASR) and pre-pulse inhibition (PPI) in a subject. In particular, the present disclosure relates to a set of acoustic signals and their use in methods for evaluation and/or treatment of mental disorder in a subject. The methods comprise delivering a set of acoustic signals as described herein to the subject, and measuring the startle response in the subject. The startle response may be the blink reflex, pupil dilation, skin conductive response, and/or brain activity in fMRI. For example, measuring the blink reflex may involve measuring the speed, magnitude, and/or duration of the blink reflex in the subject.