Disclosed is a wearable device for modulating a sensory dysfunction or relieving from a physical symptom associated with a neurological condition or disease, such as Parkinson's disease or multiple sclerosis. The wearable device comprises at least one stimulating element configured to provide at least one mechanical stimulus to a user. The wearable device further comprises a dissipating portion configured to increase an effective area of a mechanical stimulus provided by the at least one stimulating element. The dissipating portion is coupled with the at least one stimulating element, for example by an adhesive. The wearable device is configured to provide a direct contact with (or placed against) a body part of a user, for example sternum.

A machine learning system (110) for determining at least one analysis model for predicting at least one target variable indicative of a disease status is proposed. The machine learning system (110) comprises: at least one communication interface (114) configured for receiving input data, wherein the input data comprises a set of historical digital biomarker feature data, wherein the set of historical digital biomarker feature data comprises a plurality of measured values indicative of the disease status to be predicted; at least one model unit (116) comprising at least one machine learning model comprising at least one algorithm; at least one processing unit (112), wherein the processing unit (112) is configured for determining at least one training data set and at least one test data set from the input data set, wherein the processing unit (112) is configured for determining the analysis model by training the machine learning model with the training data set, wherein the processing unit (112) is configured for predicting the target variable on the test data set using the determined analysis model, wherein the processing unit (112) is configured for determining performance of the determined analysis model based on the predicted target variable and a true value of the target variable of the test data set.

Method and system for determining the localization of wearable sensors on the body of a user among a number of predefined attachment sites, comprising collecting kinematic data from at least two Inertial Measurement Units (IMUs) embedded in wearable devices attached to a user, transferring all the signals collected to a separate processing unit, comprising a memory and a comparator engine, and comparing signal characteristics to determine the sites of attachment to the user. Said system and method are useful for the monitoring of movement disorders such as Parkinson's disease.

A treatment efficacy of a treatment for treating a parkinsonian disease is determined. A first set of imaging information/data associated with a dMRI scan of an individual's brain captured at a first time and a second set of imaging information/data associated with a dMRI scan of the individual's brain captured at a second time are received. The individual underwent the treatment for a time period comprising at least part of the time between the first and second times. An expected change between the first and second times is determined based on a natural progression of the parkinsonian disease. The first and second sets of imaging information/data are analyzed to determine a first free-water pattern and a second free-water pattern. Based on the first and second free-water patterns, a disease progression score is determined. Based on the disease progression score and the expected change, a treatment efficacy for treating the individual with the treatment is determined.

A system and kit for capturing a 3D image of a body a user includes a plurality of pillar segments being configurable between an assembled configuration and a disassembled configuration. In the assembled configuration, the pillar segments are joined to form one or more upstanding sensing pillars. A plurality of sensors operable to capture image data are distributed along the one or more sensing pillars. The plurality of sensors have fields of view that are overlapping when supported on the sensing pillars. In the disassembled configuration, transportation of the pillar segments is facilitated. The system and kit may be suitable for use at a remote location. Additional functionalities may include a power storage unit, solar charging panels, climate control subsystem, and wireless communication submodule. In operation, the sensing pillars may be enclosed within an enclosure.

Embodiments can relate to a method for detecting a physiological condition by generating a Magnetic Resonance Image (MRI) contrast image comprising a T1 weighted (T1W) image/T2 weighted (T2W) ratio. Embodiments can further include using the T1W/T2W ratio to identify changes in substantia nigra pars compacta within a region of the brain.

A system for motion analysis of a subject includes two or more sensor units that are attachable at respective attachment points of the subject to detect motion of the attachment points relative to each other, each sensor unit including a time-of-flight (TOF) ranging sensor in communication with at least one processor. The at least one processor is configured to: cause the sensor units to execute a two-way ranging protocol at a succession of times, said two-way ranging protocol including transmission of one or more signals from, and reception of one or more signals at, said TOF ranging sensors, to determine TOF distance data indicative of one or more respective distances between the sensor units at respective times; and determine, from at least the TOF distance data, one or more motion metrics.

A neuromelanin sensitive magnetic resonance imaging (“MRI”) technique, method and computer-accessible medium for measuring the extent of, providing a diagnosis of, monitoring the treatment of, assessing novel treatments for, or determining a prognosis related to Parkinson's disease.

A method and user device for determining a unified Parkinson's disease rating scale (UPDRS) value associated with a user of the user device include obtaining video data associated with a movement of a body part of the user. The UPDRS value is determined using a model and the video data associated with the movement of the body part of the user. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

The exemplary embodiments disclose a system and method, a computer program product, and a computer system for assessing one or more Parkinson's disease symptoms. The exemplary embodiments may include collecting data of a user's motion, extracting one or more features from the collected data, and assessing one or more Parkinson's disease symptoms of the user based on applying one or more models to the data.