Provided are systems and methods for medical diagnosis. The systems and methods may identify a coherence between paired sensor data respectively measured from a first sensor attached to a head of a subject and a second sensor attached to a body part of the subject. The systems and methods may determine an amount of volition in the subject's body based on the coherence. The systems and methods may determine a diagnosis or a treatment plan for a subject based on the amount of volition. The system and methods may be used to track interaction between individuals in a clinical setting or in a social group.

Example systems, methods, and apparatus, including cognitive platforms, are provided for applying signal detection metrics in computer-implemented adaptive response-deadline procedures to data collected based at least in part on user interaction(s) with computerized tasks and/or interferences. The apparatus can include a response classifier for generating a quantifier of the cognitive abilities of an individual. The apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual's cognitive abilities.

A medical lead with at least a distal portion thereof implantable in the brain of a patient is described, together with methods and systems for using the lead. The lead is provided with at least two sensing modalities (e.g., two or more sensing modalities for measurements of field potential measurements, neuronal single unit activity, neuronal multi unit activity, optical blood volume, optical blood oxygenation, voltammetry and rheoencephalography). Acquisition of measurements and the lead components and other components for accomplishing a measurement in each modality are also described as are various applications for the multimodal brain sensing lead.

Embodiments include systems, methods, and computer program products for monitoring progression of Parkinson's disease. Aspects include receiving pressure data from a plurality of pressure sensors, the pressure sensors being positioned on a chair. Aspects also include analyzing the pressure data to determine the severity of a unified Parkinson's disease rating scale factor for a patient.

Disclosed is an interactive gaze analysis kit for the early diagnosis of neurodegenerative pathologies essentially composed of a video camera with framing on the eyes of the patient and a screen both connected to a calculation unit through which the user is shown images specifically designed for being able to observe the presence of micronystagmus, the reaction times following an optical stimulations and measure the light sensitivity threshold, through which the diagnosis can be found. Since the test is particularly sensitive to the level of brightness to which the patient is subjected, it is necessary to have the presence of a dark tunnel through which the patient can look at the screen, an infrared, visible spectrum or infrared and visible spectrum lighting system, an image analysis software and input-output peripheral devices for allowing the interaction both with the doctor and with the patient. Other related embodiments are described.

Device for predicting the wearing-off conditions in a patient affected by Parkinson's disease, which comprises an electrodermal activity sensor that detects the skin conductance of a patient and generates corresponding measurement signals; an electronic control unit, which receives the above measurement signals and calculates a physiological parameter representative of the skin conductance; a communication unit, which is connected to the electronic control unit and is actuatable by the latter to emit an off-notification signal. The electronic control unit comprises a processing module which detects, within a movable time window, an overall time variation of the physiological parameter indicative of an increase of the skin conductance, and compares this overall time variation with a specific threshold value, so as to control the communication unit to emit an off-notification signal which allows warning, ahead of time, the verification of a probable proximate wearing-off, thus allowing suitable operation.

A method and system for remotely detecting a heart-related condition is presented. The method includes receiving a facial image of a patient, wherein the facial image is retrieved from a data store and captured from the patient; extracting at least one facial feature from the facial image of the patient, wherein the at least one facial feature is represented as numerical descriptors of facial attributes, wherein the numerical descriptors include surface features of the at least one facial feature; and classifying the extracted at least one facial feature using a classifier, wherein the classifier maps the extracted at least one facial feature to at least one score indicative of a stage of the heart-related condition.

The present invention is directed to a device and method for monitoring and assessment of movement disorder symptoms. The device and method disclosed herein are particularly amenable to remote monitoring of a subject's movement disorder symptoms. Briefly stated, in certain preferred embodiments of the present invention the movement disorder monitoring device accompanies a subject to a remote location where the device is used to record data relating to the severity of a subject's movement disorder symptoms over a period of time. This data is then subsequently used by physicians or other clinicians in optimizing and assessing treatment options directed at alleviating a subject's movement disorder symptoms. The method and device of the present invention can be used to monitor symptoms of a number of movement disorders including but not limited to dystonia, essential tremor, Huntington's disease, various ataxias, multiple sclerosis, psychogenic tremor, and Parkinson's disease.

Neurodegeneration can be assessed based on a gait signature, using a machine-learning model trained on gait metrics acquired for a patient, in conjunction with cognitive test data and neuropathology information about the patient. The gait signature can be derived from gait kinematic data, e.g., as obtained with a video-based, marker-less motion capture system.

In an embodiment, a wearable device for vibratory stimulation is presented. The wearable device includes a sensor configured to receive data and generate sensor output. The wearable device includes a processor in communication with the sensor and a memory communicatively connected to the processor. The memory includes instructions configuring the processor to receive the sensor output from the sensor. The processor is configured to determine a symptom of a movement disorder of a user based on the sensor output. The processor is configured to calculate a waveform output based on the symptom of the movement disorder. The processor is configured to command a transducer in communication with the processor to apply the waveform output to the user to reduce the symptom of the movement disorder.