A method for estimating a likelihood of a stroke condition of a subject, the method comprising: acquiring clinical measurement data pertaining to said subject, said clinical measurement data including at least one of image data, sound data, movement data, and tactile data; extracting from said clinical measurement data, potential stroke features according to at least one predetermined stroke assessment criterion; comparing said potential stroke features with classified sampled data acquired from a plurality of subjects, each positively diagnosed with at least one stroke condition, defining a positive stroke dataset; and determining, according to said comparing, a probability of a type of said stroke condition, and a probability of a corresponding stroke location of said stroke condition with respect to a brain location of said subject.

Introduced here are approaches to authenticating the identity of speakers based on the “liveness” of the input. To prevent spoofing, an authentication platform may establish the likelihood that a voice sample represents a recording of word(s) uttered by a speaker whose identity is to be authenticated and then, based on the likelihood, determine whether to authenticate the speaker.

An information processing device (14) extracts a feature value which is an acoustic parameter from audio data. The information processing device (14) generates a spectrogram image of the audio data. The information processing device (14) calculates, on the basis of the feature value and a calculation model, a first score which indicates the extent of a user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) inputs the spectrogram image to a learned model, and calculates a second score which indicates the extent of the user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) combines the first score and the second score to calculate a combined score which indicates the extent of the user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) estimates whether the user has any of the disorders or symptoms according to the combined score.

In order to determine a degree of dementia of a user, contents are output through a user terminal, a voice of the user for a content acquired by a microphone of the user terminal is received, a spectrogram image is generated by visualizing the voice, and the degree of dementia of the user is determined by means of a convolutional neural network (CNN) and a deep neural network (DNN) based on the spectrogram image.

Embodiments herein relate to ear-wearable systems and devices that can detect allergic reactions. In an embodiment, an ear-wearable device is included having a control circuit, a microphone, and a sensor package. The ear-wearable device can be configured to evaluate at least one of signals from the microphone, signals from the sensor package, signals from an external sensor, and contextual factor data, and detect an allergic reaction based on the evaluation. In an embodiment, an ear-wearable device system is included having a first ear-wearable device and a second ear-wearable device. In an embodiment, a method of predicting or detecting the onset or presence of an allergic reaction with an ear-wearable system is included. Other embodiments are also included herein.

A dynamic neuropsychological assessment tool according to an embodiment utilizes speech recognition, speech synthesis and machine learning to assess whether a patient is at risk for a neurological disorder, such as Alzheimer's disease. The dynamic neuropsychological assessment tool enables self-administration by a patient. The tool performs pre-test validation operations on the test environment, test equipment, and the patient's capability for performing the test at that time. The tool also enables dynamic modification of a questionnaire presented to the patient while the patient completes the questionnaire. Also provides the dynamic modification of which tests to present the patient with. The modification can be rule based or modified by a provider. The dynamic neuropsychological assessment tool enables providers and administrators to modify and improve tests and validate them using machine learning based on previously completed assessments and results.

Embodiments herein relate to ear-wearable systems and devices for detecting heat stress and related methods. In an embodiment, an ear-wearable heat stress risk assessment system is included having a control circuit, a microphone, and a sensor package. The system is configured to process signals of one or more sensors of the sensor package and/or the microphone, detect dehydration symptoms, environmental conditions, and activity levels of a device wearer based on the processed signals, and determine a heat stress risk level based on detected dehydration symptoms, environmental conditions, and activity levels of the device wearer. Other embodiments are also included herein.

Embodiments herein determine when to place a passive assistive call using personal artificial intelligence (AI) assistants. The present embodiments improve upon the base functionalities of the assistant devices by monitoring the usually discarded or filtered-out environmental sounds to identify when a person is in distress to automatically issue an assistive call in addition to or alternatively to monitoring user speech for active commands to place assistive calls. The assistant device may be in communication with various other sensors to enhance or supplement the audio assessment of the persons in the environment, and may be used in a variety of scenarios where prior call systems struggled to quickly and accurately identify distress in various monitored persons (e.g., patients) including falls, stroke onset, and choking.

A system for monitoring patients during a consciousness-altering therapeutic treatment session including a data collection module in electronic communication with network servers for storage of data on non-transitory computer readable media for monitoring a patient's well-being during and after the treatment session. A method of using the system in treating a patient, by continuously, continually, or at the healthcare professionals discretion monitoring the well-being of the patient during a consciousness-altering therapeutic treatment session through one or more wearable devices and a patient mobile device in electronic communication with a facilitator mobile device, and continuously monitoring the well-being of the patient after the treatment session with the wearable device.

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and/or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.