Conventionally, a neuronal controller located inside the central nervous system governing the maintenance of the upright posture of the human body is designed from a control system perspective using proportional-integral-derivative (PID) controllers, wherein human postural sway is modeled either along a sagittal plan or along a frontal plane separately resulting in limited insights on intricacies of a governing neuronal controller. Also, existing neuronal controllers using a reinforcement learning (RL) paradigm are based on complex actor-critic on-policy algorithms. Analyzing human postural sway is critical to detect markers for progression of balance impairments. The present disclosure facilitates modelling the neuronal controller using a simplified RL algorithm, capable of producing postural sway characteristics in both sagittal and frontal plane together. The Q-learning technique of the RL paradigm is employed for learning an optimal state-action value (Q-value) function for a tuneable Markov Decision Process (MDP) model.

Systems and methods for diagnosis of Parkinson's disease (PD) using machine learning are disclosed. In one embodiment, a method may include receiving, on at least one processor, data, comprising one or more Magnetic Resonance Images (MRI) from a human subject; preprocessing the one or more MRIs; applying one or more Convolutional Neural Networks (CNNs) to perform image analysis of the one or more MRIs; applying one or more Generative Adversarial Networks (GANs) to augment a dataset of artificial scans for classification training; outputting, using the at least one processor, a classification based on the one or more MRI images a diagnosis of the subject for PD.

The present invention relates to the field of diagnostics. Specifically, it relates to a method for assessing Huntington's disease of the pre-manifest stage in a subject comprising the steps of determining at least one performance parameter from a dataset of fine motoric measurements from said subject, comparing the determined at least one performance parameter to a reference, and assessing Huntington's disease of the pre-manifest stage in the subject based on said comparison. Yet, the invention contemplates a device and a system for carrying out the aforementioned methods and the use of such device or system for assessing Huntington's disease of the pre-manifest stage in the subject.

Embodiments of the disclosure are drawn to implantable stimulator with machine learning based classifier. An implantable system includes sensors which provide sensor information to an implantable unit. The implantable unit uses a classifier on the sensor information to select a stimulation procedure which is applied via a stimulation electrode. The classifier may be generated by a trained machine learning model. The classifier may be trained on an external unit which is not implanted in the subject. The classifier may be trained based on sensor information from the implanted sensors as well as symptom information.

In order to help reduce the effects of motion sickness, there is provided a method for reducing motion sickness in a subject which comprises acquiring a sequence of video images, extracting measurements of a heart-rate of the subject over a first period of time from the sequence of video images using photoplethysmography (PPG), calculating at least one trend in the measurements, determining a presence of motion sickness when the at least one trend is positive over a first time window, the first time window being included in the first period of time, and generating an event arranged to generate a corrective action. It is often possible to detect the onset of motion sickness before the subject actually feels the symptoms. Indeed, by the time the symptoms appear, corrective action is much less effective. Therefore, by detecting the onset early and alerting the subject so that they can react, it is possible to avoid the attack of motion sickness or, at least, reduce significantly its effects.

A method and a related device to determine the kinetic state of a subject includes the steps of determining a signal indicative of the acceleration trend on the three Cartesian axes; processing the signal to limit the frequency band and preferably reduce artifacts and compensate the offset of the output signals from a multi-axial measurement system; analyzing frequency and spectrum through the transformation of the signal with the Fournier transform; computing the power spectral density for each Cartesian axis; and comparing the spectral density with a characteristic pattern of a movement.

The present disclosure provides methods for diagnosing and determining the disease progression of neurodegenerative disorders in patients using neurophysiological biomarkers.

Described are a system, method, and computer program product for detecting neurodegeneration using differential tractography and treating neurological disorders accordingly. The method includes obtaining a first diffusion magnetic resonance imaging (MRI) scan of the brain of the patient and obtaining a plurality of diffusion MRI scans of a group of other brains. The method also includes generating a control diffusion MRI scan based on the plurality of diffusion MRI scans of the group of other brains. The method further includes determining a first anisotropy of first neural tracks of the first diffusion MRI scan and a second anisotropy of second neural tracks of the control diffusion MRI scan. The method further includes determining a differential by comparing the first anisotropy to the second anisotropy and identifying at least one neurological disorder based on the differential and a location of the first neural tracks in the brain of the patient.

A training apparatus is capable of generating and presenting a suitable training menu for finger exercise to maintain or improve a cognitive function and/or an exercise function of a human and supporting training of the human. The training apparatus includes a measuring apparatus and a terminal device. Analysis evaluating data based on measurement of the finger exercise including finger tapping is obtained, and the analysis evaluating data contains an evaluation value of an index item related to an exercise function of a user. A training menu for the user is generated based on the analysis evaluating data and contains a training item for the finger exercise. The index item includes at least one of an amount of exercise, endurance, rhythmicity, cooperativeness of both sides, or marker trackability. The training item includes an exercise to carry out the finger tapping in accordance with teaching information, stimulation, or a marker.

Disclosed herein is a method for diagnosing a neurological disorder based on at least one magnetic resonance imaging (MRI) image. The method includes identifying brain image regions that contain a respective portion of diffusion index values of at least one diffusion index. For each of the brain image regions, a characteristic parameter based on the respective portion of the diffusion index values is calculated. a diagnoses is then made for the brain using one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.