Disclosed are techniques for determining a severity of motion disorder symptoms by receiving sensor data from one or more sensors, determining that the sensor data represents one or more activities of daily life (ADLs) of a user, assigning one or more probabilities to the one or more determined ADLs, each probability of the one or more probabilities indicating a confidence level that the sensor data represents a corresponding ADL, and providing the sensor data and the one or more probabilities to a motion disorder symptom scoring module that generates one or more scores for the one or more determined ADLs based on the sensor data, each score of the one or more scores indicating the severity of the motion disorder symptoms for a corresponding ADL, and combines the one or more scores and the one or more probabilities to generate an aggregated severity score for the motion disorder symptoms.

Provided is a method and apparatus for assisting spasticity and clonus evaluation using an inertia sensor, the apparatus including an acquirer configured to acquire a measured value from an inertia sensor attached to an object, a calculator configured to calculate an angle of a joint of the object based on the measured value, and a processor configured to evaluate a spasticity and a clonus based on the angle of the joint.

A method and system for predicting neurological disorders is provided. The method comprises receiving videos of individuals and detecting Regions of Interest (ROI) in video frames. The method further comprises determining a Motion Vector (MV) for each ROI in a set of successive frames and comparing value of the determined MV with pre-stored values. Furthermore, the method comprises identifying a MV matching a pre-stored value thereby identifying a ROI and a frame corresponding to the identified MV, wherein the pre-stored value indicates onset of an expression. Also, the method comprises determining MVs for the identified ROI in subsequent sets of successive frames and comparing value of the determined MVs with a pre-stored value of MV corresponding to peak and offset of the indicated expression. The method further comprises identifying the frame corresponding to the peak and offset of the indicated expression and generating pictorial representation for predicting neurological disorders.

The present invention relates to a method of gait measurement using tri-axial accelerometer/gyro in mobile devices. In particular, the present invention relates to algorithms for gait measurement using tri-axial accelerometer/gyro in mobile devices for monitoring and improving the physical movement of a moving subject.

A device for capturing and concentrating volatile organic compounds (VOCs) in a sample of breath air. The device includes an intake for accepting an air sample; a disposable mouth piece; a sensor array for measuring physical parameters of the air sample; an exhaled air sampler for capturing a pre-determined volume of air; a concentrator for concentrating VOCs in the air sample; and an ionic liquid collector, the latter of which may be removed from the device. The ionic liquid collector, which may have one compartment or multiple compartments, includes at least one ionic liquid. Analysis of VOCs in the ionic liquid or liquids may identify biomarkers that can provide a medical diagnosis for a human patient based on a sample of breath air.

An apparatus that can measure images of at least a portion of an eye and record data sets indicative of a neurological condition. A method interrelates an image and a data set to provide an interpretive result. The apparatus and method thereby provide guidance as to the presence of a medical condition in a patient. The apparatus and method can be used in an iterative measurement process, in which the apparatus attempts to discern normal health from a state of health that is not normal health. If the interpretive result is consistent with normal health, the process terminates, information is recorded, and an optional report is given. If the interpretive result is not consistent with normal health, the apparatus and method attempts to distinguish which condition is consistent with the data and images used, and can iterate with additional measurements and information to attempt to provide a useful interpretive result.

A neuromodulator accurately measuresin real time and over a range of frequenciesthe instantaneous phase and amplitude of a natural signal. For example, the natural signal may be an electrical signal produced by neural tissue, or a motion such as a muscle tremor. The neuromodulator generates signals that are precisely timed relative to the phase of the natural signal. For example, the neuromodulator may generate an exogenous signal that is phase-locked with the natural signal. Or, for example, the neuromodulator may generate an exogenous signal that comprises short bursts which occur only during a narrow phase range of each period of an oscillating natural signal. The neuromodulator corrects distortions due to Gibbs phenomenon. In some cases, the neuromodulator does so by applying a causal filter to a discrete Fourier transform in the frequency domain, prior to taking an inverse discrete Fourier transform.

Systems, methods, and computer programs for monitoring the disease progression of a Parkinson's patient are provided. Specifications for a Parkinson's patient may defined in a computer system by a physician. The specifications may contain threshold values. At repeating points in time, a message may including a request to perform a mobility test may be output by the computer system to the patient. Mobility parameters may be determined as a result of the performance of the mobility test. The mobility parameters may be compared with the threshold values to determine one or more deviations between the one or more mobility parameters and the one or more threshold values. The mobility parameters, the deviations, or a combination thereof may be made available to the physician by outputting or storing the mobility parameters or the deviations or by transmitting the mobility parameters or the deviations to a separate computer system.

In some embodiments, the systems and methods of the disclosure can efficiently and accurately classify neurodegenerative disorder(s) and/or movement disorder(s) of a subject (e.g., a patient) using at least quantitative features associated with one or more regions of interest determined from one or more sets of image data of the subject's brain. The method may include processing one or more sets of MRI image data of the subject's brain to extract one or more quantitative features for one or more regions. The one or more quantitative features may include a first quantitative and a second quantitative feature. The method may further include classifying at least the one or more quantitative features into one or more classes associated with neurodegenerative dementia disorder, neurodegenerative movement disorder, non-neurodegenerative movement disorder and/or heathy control. The method may include generating a report including a classification of at least the one or more quantitative features.

A method for facilitating a Parkinson's Disease (PD) assessment of a patient includes capturing first video of a patient performing first test movements while holding the mobile device; capturing second video of the patient performing second test movements while maintaining the mobile device on their person; capturing third video of the patient performing third test movements including standing and walking; capturing one or more IMU readings using an IMU of the mobile device; processing the first video, the second video, and the third video according to (i) a hand landmark model to generate one or more hand biomarkers, (ii) a face landmark model to generate one or more face biomarkers, and (iii) a body landmark model to generate one or more body biomarkers; and determining an assessment score based on a standardized PD assessment by processing the biomarkers.