An apparatus, module, methods and systems for automated, standardized assessment and analysis of a human olfactory system's odor detection ability as an indicator or predictor of cognitive impairment or change in cognitive health, and other health conditions such as diabetes. Notably, the present invention is operable for use across all age groups of humans and provides quantitative detection and analysis of a human olfactory system's detection ability compared to a relevant demographic population.

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 system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals. Decision fusion is used to integrate classification results from feature-based classifier and deep learning AI to reach optimal decision.

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.

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.

A writing instrument that includes a writing element for depositing a writing material on a support and a plurality of sensors including at least a force sensor and a movement sensor, a communication unit that exchanges data with a remote device a control unit connected to the sensors and to the communication unit in order to transmit to the remote device the measurements provided by the sensors, a memory unit connected to the control unit that stores one or more measurements from the sensors; and a hollow casing that contains at least part of the writing element so that the writing end is exposed, and also houses the sensors, the control unit, the memory unit and the communication unit.

The present disclosure is directed to interactively counseling a user with respect to supervised content. In particular, the methods and systems of the present disclosure may: determine, based at least in part on one or more machine learning (ML) models, that one or more interfaces displayed to a user include content of a content type designated by a content supervisor of the user for identification; and, responsive to determining that the interface(s) displayed to the user include content of the content type designated for identification, generate data representing a graphical user interface (GUI) for presentation to the user, the GUI indicating detection of the content of the content type and comprising interactive educational material counseling the user with respect to the content type.

A device for analyzing sleep breathing using a radar includes a transceiver configured to transmit a radar signal toward a subject and receive the radar signal reflected from the subject; an average breathing signal calculation unit configured to calculate an average breathing signal of the subject based on the radar signal; a sleep breathing pattern information generation unit configured to generate sleep breathing pattern information of the subject by comparing the radar signal with the average breathing signal; and a sleep breathing event detection unit configured to detect a sleep breathing event based on the sleep breathing pattern information.

A system for assessment of neurocognitive and neuromotor control performance, the system comprising a portable force plate configured to collect force plate data indicative of movement and postural control of a subject as the subject performs a task, a depth sensing device configured to, simultaneously with the collection of the force plate data, collect depth data of the subject as the subject performs the task, an interface board configured to, simultaneously with the collection of the force plate data and the collection of the depth data, generate stimuli to instruct the subject to perform a particular task and to generate interface board data indication of input received from the subject in response to the stimuli, and a computer-based controller configured to execute one or more neurocognitive and neuromotor control performance assessment program to analyze the force plate data, the depth data, and the interface board data.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.