Disclosed is a multifunctional exercise chair for body stretching, comprising a wheelchair frame body, wherein a mounting frame is detachably mounted on an end of the wheelchair frame body away from the driving rear wheel and located under the seat cushion, a pedal gear is rotatably mounted at a bottom of the mounting frame and a pedal is provided thereon, a hand crank gear is rotatably mounted on a top of the mounting frame and a hand crank handle is provided thereon, a rotating shaft is provided between two driving rear wheels; a driven gear is fixedly mounted on the rotating shaft, a supporting chain gear is mounted on the mounting frame and arranged adjacent to the pedal gear, and the hand crank gear, the pedal gear, the driven gear and the supporting chain gear are wound and connected by a transmission chain.

Hearing device including a housing worn at a user's ear; a movement detector providing movement data indicating movement of the housing; and a processor identifying, based on the movement data, movement features in a sequence. The movement features represent movement activity by the user. The processor determines a temporal characteristic of the sequence of movement features and/or an amplitude characteristic of the movement data associated with a movement feature in the sequence. A method of operating the hearing device, and a computer-readable medium storing instructions to perform the method. The processor controls maintaining a data record representing the temporal characteristic and/or the amplitude characteristic determined at a plurality of times; determines a deviation measure indicating deviation between the data record and the temporal characteristic and/or the amplitude characteristic determined at a time later than the plurality of times; and controls operation of the hearing device depending on the deviation measure.

Methods for screening, diagnosing, or predicting presence, progression, or treatment effects of a cognitive dysfunction such as dementia based on an analysis of drawing behavior changes by a pre-trained Naïve Bayes method are provided. The methods include steps of obtaining drawing data of at least one image created by a test subject on a digital device and obtaining personal data of the test subject; reconstructing the at least one image based on the drawing data obtained; converting the drawing data to drawing features comprising a plurality of motion features and a plurality of geometric features; and determining probability that the test subject has a cognitive dysfunction based on the drawing features and the personal data by a pre-trained Naïve Bayes method with a greedy variable selection.

A system may access electroencephalography (EEG) data of a subject, the EEG data including responses of the subject to an activation procedure. A system may analyze the EEG data including a plurality of epochs corresponding to the responses to identify first peaks, second peaks, and third peaks in one or more epochs. A system may determine values of a parameter in the plurality of epochs, the parameter being a characteristic of the first peak, the second peak, and/or the third peak. A system may generate a visual representation of the EEG data of the subject, the visual representation including an illustration of the first peak, the second peak and/or the third peak of a representative epoch or a heatmap compiled from the plurality of epochs, and the visual representation further including a graphical representation of the parameter that is presented as evidence of whether the subject is cognitively impaired.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing EEG source localization. One of the methods includes obtaining brain data comprising: EEG data comprising respective channel data corresponding to each of a plurality of electrodes of an EEG sensor, and fMRI data comprising respective voxel data corresponding to each of a plurality of voxels; identifying, in a three-dimensional coordinate system, a respective location for each electrode; generating, using the respective identified locations of each electrode, data representing a location in the three-dimensional coordinate system of each voxel; determining, for each electrode, a region of interest in the three-dimensional coordinate system; and identifying, for each electrode, one or more corresponding parcellations in the brain of the subject, wherein each parcellation that corresponds to an electrode at least partially overlaps with the region of interest of the electrode.

Disclosed is a hierarchical diagnosis device of brain atrophy based on brain thickness information, including a brain structure modeling unit to generate a grey matter surface mesh and a white matter surface mesh by mesh modeling of multiple Magnetic Resonance Imaging (MRI) images; a brain thickness extraction unit to acquire brain thickness information by collecting and analyzing a distance between corresponding points of the grey matter surface mesh and the white matter surface mesh; a training data generation unit to generate and store multiple training data including the brain thickness information and diagnosis information when diagnosis model training is requested; a diagnosis model training unit to classify and analyze the brain thickness information into groups of a hierarchical structure according to the diagnosis information to acquire feature information for each group, and generate and train hierarchical classifiers based on the hierarchical structure of the groups and the feature information for each group; and a brain atrophy diagnosis unit to acquire new brain thickness information upon receiving a new input of an MRI image of a subject, and hierarchically identify and notify a type of brain atrophy corresponding to the new thickness information through the hierarchical classifiers.

In accordance with one embodiment, a method for determining changes in health of a user is disclosed. The method includes sensing multi-dimensional motion of a body part of a user to generate a first multi-dimensional motion signal at a first time and date; in response to the first multi-dimensional motion signal, generating a first neuro-mechanical fingerprint; generating a first health measure in response to the first NFP and user calibration parameters; sensing multi-dimensional motion of the body part of the user to generate another multi-dimensional motion signal at another time and date; in response to the another multi-dimensional motion signal, generating another neuro-mechanical fingerprint; generating another health measure in response to the another NFP and the user calibration parameters; and comparing the first health measure with the another health measure to determine a difference representing the health degradation of the user.

Systems and methods for performing a retinal angiogram, which can be used to non-invasively quantify retinal perfusion by measuring differences in retinal vascular density. The systems and methods can be used to measure a response of the vasculature, particularly in the choriocapillaris. The systems and methods can be used to diagnose neurodegenerative conditions, associated sleep and mood disorders, and measure metabolic reserve. In particular, the systems and methods can also be used for both diagnostics and prognostics.

A system for patient activity monitoring may include a wearable device and a personal terminal. The personal terminal may include a processor. The processor of the personal terminal may be configured to receive, from the wearable device, proximity detection information specifying proximity detected by the wearable device between the wearable device and a tag device and sensor data received by the wearable device from the tag device. The processor of the personal terminal may be further configured to determine, based on the proximity detection information and the sensor data, that an action was performed by a patient.

A method of diagnosing Alzheimer's disease using a positron emission tomography-computed tomography (PET-CT) image may include generating a standard brain CT template in a Montreal Neurological Institute (MNI) region based on a CT image calculated from a PET-CT apparatus, calculating a whole cortex volume of interest (VOI) for a plurality of detail regions capable of being used in .sup.18F-florbetaben (FBB) and .sup.18F-flutemetamol (FMM) in common within a cortex ROI region in which a deposition of beta amyloid protein is equal to or higher than a given value based on the standard brain CT template, and calculating a centiloid of each of the plurality of detail regions based on an amyloid standardized uptake value ratio (SUVR) of each of the plurality of detail regions.