Disclosed herein is a gait analysis apparatus that is configured to provide multidimensional measures of the gait of an individual as the individual traverses the gait analysis apparatus. The gait analysis apparatus may be configured to provide a gait measuring processing device with the multidimensional measurements. Based on the multidimensional measurements, the gait measuring process device may, for example, diagnose the test subject with lameness or particular neuromuscular dysfunctions (NM) disease and/or injury, monitor progression of lameness or a particular NM disease and/or injury over time, determine a static weight as the test subject is traversing, monitor the static weight of the test subject over an extended period time, and/or determine which measurements may be used as biomarkers to identify lameness or the particular NM disease and/or injury. A system including a gait analysis apparatus is also disclosed.

The invention relates to a system 1 of characterizing the gait of a user to obtain a value representative of the evolution of the gait of said user, including a pair 10 of soles, the soles 11, 12, constituting said pair 10 of soles, each including an electronic box 100, 101, 102, each electronic box 100, 101, 102 comprising: an inertial platform 110, 111, 112, a data processing module 120, 121, 122, a data storage module 130, 131, 132, and a power source 160, 161, 162; said system including a data comparison module 140, 141, 142 configured to compare the at least one biomechanical parameter to reference biomechanical parameters and calculate a value representative of the evolution of the gait of the user; said data comparison module being carried by the electronic boxes or the external terminal; and each electronic box including a first communication means.

There are provided herein, a method and system for creating a speech/language pathologies classifier, the method comprising: producing a pathological speech repository of pathological speech samples of multiple impairments; computing speech qualities/pathologies, based on data receive from the pathological speech repository; producing a text repository, the text repository comprises multiple known text passages; converting each one of a selection of the text passages from the multiple known text passages, to a speech segment, while introducing to the speech segment one or more of the computed speech pathologies, thereby creating multiple synthetic impaired speech segments; and training a classifier with the multiple synthetic impaired speech segments thereby creating a speech/language pathologies classifier.

In some aspects, a device wearable by a person includes a transducer configured to apply to the brain of the person acoustic signals.

The present invention relates to an artificial intelligence-based Parkinson's disease diagnosing apparatus and method. An artificial intelligence-based Parkinson's disease diagnosing apparatus which is an aspect of the present disclosure includes an image acquiring unit which acquires a first image related to a multi-echo magnitude and phase from an MRI obtained by capturing a brain of a patient; an image processing unit which post-processes the acquired first image so as to observe a substantia nigra and a nigrosome-1 region used as imaging biomarkers of the Parkinson's disease; an image analyzing unit which analyzes the post-processed first image to classify a second image including the nigrosome-1 region and detects the nigrosome-1 region from the classified second image; and a diagnosing unit which diagnoses whether the patient has the Parkinson's disease by analyzing whether the detected nigrosome-1 region is normal, in which the image processing unit generates a susceptibility map weighted imaging image by applying a quantitative susceptibility map mask to the first image based on a quantitative susceptibility mapping algorithm to perform the post-processing and the image processing unit further performs at least one operation of angle adjustment, image enlargement, and reslice, on the generated susceptibility map weighted imaging image.

Disclosed are Parkinson's disease diagnosing apparatus and method and a configuration which includes an image acquiring unit which acquires a multi-echo magnitude and a phase image from MRI obtained by capturing a brain of a patient, an image processing unit which post-processes only substantia nigra and a nigrosome-1 region proposed as an imaging biomarker of the Parkinson's disease from the acquired image to be observed; an image analyzing unit which classifies images including the nigrosome-1 region by analyzing the processed images and detects the nigrosome-1 region from the classified image, and a diagnosing unit which determines whether the nigrosome-1 region is normal in the classified image to diagnose the Parkinson's disease is provided so that only the image which includes the nigrosome-1 region is classified in the MRI and the nigrosome-1 region is analyzed from the classified image to diagnose the Parkinson's disease.

Methods, computer program products, and systems for testing of multisensory integration of a user are provided as well as their association with cognitive and motor outcomes. In various embodiments, the method includes providing a first plurality of signals to the user from a first signaling modality. A second plurality of signals are provided to the user from a second signaling modality. Indications are received at a response sensor from the user in response to each of the first and second plurality of signals. Reaction times between each of the first and second plurality of signals and the respective indication are determined. An observed curve is generated based on the reaction times. A differential curve is determined from the observed curve and a predicted curve. An area under at least a portion of the differential curve is determined. When the area is above a predetermined threshold, an indication that the user has satisfactory integrated sensory information is provided. When the area is below the predetermined threshold, an indication that the user has unsatisfactory integrated sensory information is provided.

There is included an apparatus and system including image segmentation code, configured to cause at least one hardware processor to segment an image of a person's hand from an input image, and classification code configured to cause the at least one processor to classify the segmented image of the person's hand according to at least one predefined pose.

An audio processing method and system configured to output at least one clinically-validated cue or combination of cues targeting at least one symptom or aspect of a CNS disease, disorder or condition to at least one user. In accordance with certain embodiments, the at least one clinically-validated cue or combination of cues comprises an audio cue. An audio processing method of the present disclosure comprises one or more steps or operations for selecting at least one audio file comprising a clinically-validated audio cue from a plurality of audio files comprising a plurality of clinically-validated audio cues, applying one or more audio rendering parameters to the at least one audio file, and outputting an acoustic output of one or more audio files to a user for a specified duration.

The present invention relates to methods for remotely and intelligently tuning movement disorder of therapy systems. 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 and intelligently or automatically 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 intelligently, automatically and remotely, allowing for home monitoring of subjects.