Existing wearable device-based approaches to capture tremor signal have accuracy limitations due to usage of accelerometer sensor with inherent noisy nature. The method and system disclosed herein taps characteristics of the PPG sensor of being sensitive to the motion artefact, as an advantage, to capture tremor signal present in the PPG sensor. The method disclosed herein describes an approach to extract tremor signal of interest from the PPG signal by performing a Singular Spectrum Analysis (SSA) followed by spectrum density estimation. The SSA comprises performing embedding on the acquired PPG signal, performing Principal Component Analysis (PCA) on the embedded signal and reconstructing the rest tremor signal from the significant principal components identified post the PCA. Further, the spectrum density estimation detects a dominant frequency present in the principal components, which is the dominant frequency associated with the rest tremor.

An exemplary hearing device configured to be worn by a user includes a microphone and a processor. The microphone detects an audio signal. The processor is configured to determine that the audio signal includes own voice content representative of a voice of the user and determine that an environmental noise level within the audio signal is below a threshold The processor is further configured to apply, based on the environmental noise level being below the threshold, a biomarker feature analysis heuristic to the own voice content.

There is disclosed a method and system for predicting a likelihood that a patient is subject to one or more conditions. Retinal signal data corresponding to the patient may be received. Retinal signal features may be extracted from the retinal signal data. Descriptors may be extracted from the retinal signal features. The descriptors may be applied to a first mathematical model and a second mathematical model. The first mathematical model may correspond to a first condition. The second mathematical model may correspond to second condition. A first predicted probability for the first condition may be generated. A second predicted probability for the second condition may be generated. The first predicted probability and the second predicted probability may be output.

A test to diagnose, monitor or stratify diseases directly or indirectly associated with cholin ergic system pathologies, including Parkinson's disease, which involves a comparison of supplied pupil light reflex data against known normative data of the same kind, characterised by the fact that the supplied data contain the pupil light reflex data, which include at least one sample comprising at least one supplied parameter measured by a known device used to measure pupil light reflexes, and data which include at least one characteristic of the examined patient. The probability of disease occurrence is determined using machine learning algorithms, which comprise at least one neuronal network algorithm (SSN) and/or at least one mathematical function which is not a part of the neuronal network (SSN).

The present invention relates to methods for remotely tuning treatment parameters in movement disorder therapy systems where the subject and clinician are located remotely from each other. The present invention still further provides methods of quantifying movement disorders for the treatment of patients who exhibit symptoms of such movement disorders including, but not limited to, Parkinson's disease and Parkinsonism, Dystonia, Chorea, and Huntington's disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, stroke, and the like. The present invention yet further relates to methods of remotely tuning a therapy device using objective quantified movement disorder symptom data to determine the therapy setting or parameters to be transmitted and provided to the subject via his or her therapy device. The present invention also provides treatment and tuning remotely, allowing for home monitoring of subjects.

The present invention relates to methods for remotely tuning treatment parameters in movement disorder therapy systems where the subject and clinician are located remotely from each other. The present invention still further provides methods of quantifying movement disorders for the treatment of patients who exhibit symptoms of such movement disorders including, but not limited to, Parkinson's disease and Parkinsonism, Dystonia, Chorea, and Huntington's disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, stroke, and the like. The present invention yet further relates to methods of remotely tuning a therapy device using objective quantified movement disorder symptom data to determine the therapy setting or parameters to be transmitted and provided to the subject via his or her therapy device. The present invention also provides treatment and tuning remotely, allowing for home monitoring of subjects.

In some embodiments, a display system comprising a head-mountable, augmented reality display is configured to perform a neurological analysis and to provide a perception aid based on an environmental trigger associated with the neurological condition. Performing the neurological analysis may include determining a reaction to a stimulus by receiving data from the one or more inwardly-directed sensors; and identifying a neurological condition associated with the reaction. In some embodiments, the perception aid may include a reminder, an alert, or virtual content that changes a property, e.g. a color, of a real object. The augmented reality display may be configured to display virtual content by outputting light with variable wavefront divergence, and to provide an accommodation-vergence mismatch of less than 0.5 diopters, including less than 0.25 diopters.

Methods and systems useful for detecting neurological disorders and for measuring general cognitive performance, in particular by measuring eye movements and/or pupil diameter during eye-movement tasks.

Systems and methods for detecting a motor developmental delay and/or neurodevelopmental disorder of an infant are described herein. An example method can include receiving motion data associated with the infant's gross motor activity; analyzing, using a machine learning algorithm, the motion data to detect a kinematic feature; comparing the kinematic feature to an expected relationship between the kinematic feature and infant age; and detecting the neurodevelopmental disorder based on the comparison. An infant sensor suit is also described herein. An example infant sensor suit can include an article of clothing; a plurality of sensors; a power source operably coupled to the sensors; and a wireless transmitter operably coupled to the sensors. The sensors, power source, and wireless transmitter can be incorporated into the article of clothing.

Exemplary system, method and computer-accessible medium for remotely initiating a medical imaging scan(s) of a patient(s), can include, for example, receiving, over a network, encrypted first information related to first parameters of the patient(s), determining second information related to image acquisition second parameters based on the first information, generating an imaging sequence(s) based on the second information, and initiating, remotely from the patient(s), the medical imaging scan(s) based on the imaging sequence(s). The medical imaging scan(s) can be a magnetic resonance imaging (“MRI”) sequence(s). The image acquisition second parameters can be MRI acquisition parameters, and the imaging sequence(s) can be a gradient recalled echo (“GRE”) pulse sequence(s).