Embodiments herein disclose computer-implemented methods, computer program products and computer systems for performing neurological diagnostic assessments. The computer-implemented method may include processors configured for receiving biometric activity data corresponding to user extremity movement from a mobile device associated with a user. Further, the computer-implemented method may include processors configured for transmitting the biometric activity data to a machine learning model. Furthermore, the computer-implemented may be configured for processing, using the machine learning model, the biometric activity data to generate first model output data corresponding to a first score. Even further, the computer-implemented method may include processors configured for determining that the first model output data corresponds to a neurological disorder classification based at least on the first score exceeding a predetermined threshold.

Systems and methods for detecting anatomical structures include, for each training subject of a plurality of training subjects, a corresponding MR image, and generating an initial anatomical template based on a first training subject of the plurality of training subjects. A computing device can map MR images of the other training subjects onto a template space by applying a global transformation followed by a local transformation. The computing device can average the mapped MR images with the initial anatomical template to generate a final anatomical template and boundaries of an anatomical structure of interest can be drawn in the final anatomical template. The computing device can fine tune the boundaries using an edge detection algorithm. The final anatomical template can be used to identify boundaries of the anatomical structure(s) of interest automatically (e g., without human intervention) in non-training subjects.

The present invention comprises a novel kit with a wearable unit along with associated intelligent entities and non-intrusive tremor relieving interventions. The interventions include an automatically controllable TENS intervention unit with associated cables and electrodes and a buzzer. The wearable unit along with associated intelligent entities detects involuntary movement signals (tremors) in the limbs by measuring the acceleration in multiple dimensions, and ingeniously analyzes the accelerations to identify and isolate occurrences of tremors. This information is analyzed and monitored over time to provide significant insight about the tremors such as the dominant frequency (or frequencies), percent time with tremor, median tremor duration, and intensity. The TENS unit provides electrical stimulation which can dampen the intensity of involuntary movement and/or temporarily suspend tremor activity. This (TENS) unit can be manually or automatically controlled. The automatic control mode controls the TENS unit depending on the onset of tremors, tremor duration, and intensity of tremors. Furthermore, the parameters of this automatic control can be adjusted.

A system comprises a mouth piece to receive exhaled air; a breath chamber to receive exhaled air; a valve to direct exhaled air along a desired flow path, direct purge gas along a desired flow path, control the rate of flow of purge gas, control the rate of flow of exhaled air, block the flow of purge gas, and/or block the flow of exhaled air; a source of purge gas; a CO2 cartridge to remove CO2; a water cartridge to remove water; a breath cartridge to capture VOCs from exhaled air; a temperature control system to control the temperature of CO2 cartridge, a water cartridge, and/or a breath cartridge; a cryostat to contain and limit heat flow to a cryogenic liquid; a flow meter designed to measure the flow of exhaled air and/or purge gas; and a pressure transducer to measure a pressure, a flow rate, and/or a flow volume.

A system and method for evaluation, detection, conditioning, and treatment of neurological functioning and conditions which uses data obtained while a person is engaged in simultaneously in a range of primary physical tasks combined with defined types of associative activity, such as listening, reading, speaking, mathematics, logic puzzles, navigation of a virtual environment, recall of past stimuli, etc. The data from the physical and associative activities are combined to generate a composite functioning score visualization indicating the relative functioning of areas aspects of neurological functioning; including those in which deficiencies may be present, which are early indicators of possible neurological conditions. Through algorithmic recommendations combined with expert and user input, a conditioning regimen targeting neurological aspects of interest paired with periodic testing allows the user to track their progress in these areas over time.

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.

The present disclosure provides an analyte sensor for use in detecting glutamate. In certain embodiments, a glutamate-responsive active site of a presently disclosed analyte sensor includes a glutamate oxidase and a redox mediator disposed upon a surface of a working electrode. The present disclosure further provides methods for detecting glutamate using the disclosed analyte sensors.

A continuous and self-calibration method and system for monitoring oxygen saturation of a patient are provided. An example system includes a wearable device having a first optical sensor to measure a first red wavelength photoplethysmography (PPG) signal and a first infrared wavelength PPG signal and a second optical sensor to measure a second red wavelength PPG signal and a second infrared wavelength PPG signal. The system further includes a processor configured to repeatedly determine that conditions for recalibration of the first optical sensor are satisfied, determine a first ratio for obtaining the oxygen saturation, a first parameter for modifying the first red wavelength PPG signal, a second parameter for modifying the first infrared wavelength PPG signal, and a second ration for obtaining the oxygen saturation. The processor is further configured to determine a value of the oxygen saturation and provide a message regarding a health status of the patient.

Provided is a pathological condition analysis system using voice, the pathological condition analysis system allowing anyone to perform measurement and estimate a disease anywhere, in a short time, non-invasively, and without being known to others. A pathological condition analysis system according to the present invention analyzes a pathological condition of a subject, and includes: an input means that acquires voice data of the subject; an estimation means that estimates a disease of the subject based on a voice feature amount extracted from the voice data acquired by the input means; and a display means that displays an estimation result by the estimation means, in which the voice feature amount includes the intensity of voice.

A rehabilitation work support apparatus, a rehabilitation work support method, and a program capable of efficiently managing patient's actual current ability values are provided. A rehabilitation work support apparatus (1) includes a change prediction unit (2) that predicts whether or not an actual current ability value of a patient during a rehabilitation period has changed from a latest ability value of that patient stored in a storage device based on an elapsed time from a time of an evaluation of the latest ability value of the patient stored in the storage device, and a change prediction output unit (3) that performs control so as to output a result of the prediction made by the change prediction unit (2).