A method and a system are described, for managing a mouth therapy in the Parkinson's disease, through the analysis of the motion status of patients, wherein the motion status can be in one of the following phases: OFF phase, wherein Parkinson's symptoms, such as rigidity, tremor and bradykinesia, emerge; ON phase wherein the symptoms markedly improve; DIS phase wherein involuntary movements emerge, called dyskinesias; as detectors of the symptoms the following parameters are used: voice analysis; face analysis; tremor analysis; movement analysis; levodopa and metabolite levels of monoamines in the sub-cutaneous interstitial liquid of the patients; an algorithm being provided, which provides to the patient the daily therapeutic scheme with its relates administration dosages and inter-times, computed by using the parameters as detectors.

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 and/or brain activity mapped 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.

A system utilizing five or less body worn sensors may be used to profile the motor function of Parkinson's disease patients, integrate the outcome with patient self-reported information and translate the results to clinically relevant information, valuable for the monitoring of Parkinson's disease progression and symptom manifestation. The hardware of the system may deploy algorithms for the offline processing of the sensors' data, once the wearable monitoring devices are docked for charging, with no intervention required by the user. The system may also allow patients to mount the wearable devices featuring the sensors to any of a limited number of body parts, without taking care to mount each monitoring device to a specific body part. Finally, the system may allow a physician to register for a subscription-based service, pairing him/her with patients using the system, and granting him/her permission to remotely review the disease progression of the patients, as calculated by the system.

Described are systems for spatial cognitive ability assessment wherein a subject is able to manipulate one or more object(s) that is monitored by a device for recording motion attached to the object(s), the subject, or both that is able to generate motion electronic data. Also described are methods for assessing spatial cognitive ability.

A portable hand tremor detection device has been reported. This device is composed of a detection unit, a flexible pressure sensor, a signal processing unit, and a power supply. The detection unit is composed of the sensor and press-knob. The flexible pressure sensor is a combination of usually spaced apart flexible polymer preferably polydimethylsiloxane, flexible substrate preferably paper, and a conductive coating preferably reduced graphene oxide (RGO). When the users put pressure on the press-knob through the fingers, the flexible pressure sensor actuates and bends so that conductive RGO layers on the flexible substrates come in contact with each other to generate an output current. The electrical circuits in the signal processing unit are laid down in such a manner that the magnitude of this output current varies with the tremor between the fingers, which eventually converted into an electrical signal showing the variation in the hand tremor with time.

The present invention relates to an AI (Artificial Intelligence) based device for providing brain information comprising: a brain signal measuring portion that collects a signal related to user's brain; a brain signal stimulating portion that stimulates the user's brain for an operation of collecting a signal of the brain signal measuring portion; and a diagnosing portion that determines the user's brain state on the basis of the collected signal.

A method receives EEG data from at least one electrode implanted in the brain of the subject. The method determines a current or predicted brain state from the EEG data using an artificial intelligence (AI) model

An analytical toilet comprising a bowl for receiving excreta from a user; a seat for the user; a sensor for detecting a symptom of the user; wherein the sensor transmits data to a processor that uses the data to measure a symptom of the user is disclosed. A method for monitoring a patient comprising providing an analytical toilet comprising: a bowl for receiving excreta from a user; a seat for the user; a sensor for detecting a symptom of the user; having a user use the toilet to deposit excreta; detecting a symptom of the user with the sensor; transmitting data from the sensor to a processor; and using the data to measure the symptom and compare it to the user's normal or expected symptoms is disclosed.

The exemplary embodiments disclose a system and method, a computer program product, and a computer system for assessing a user's gate. The exemplary embodiments may include collecting data corresponding to a walking user and assessing a gait of the user based on applying one or more models to the data.

Devices, systems, and techniques for analyzing video information to objectively identify patient behavior are disclosed. A system may analyze obtained video information of patient motion during a period of time to track one or more anatomical regions through a plurality of frames of the video information and calculate one or more movement parameters of the one or more anatomical regions. The system may also compare the one or more movement parameters to respective criteria for each of a plurality of predetermined patient behaviors and identify the patient behaviors that occurred during the period of time. In some examples, a device may control therapy delivery according to the identified patient behaviors and/or sensed parameters previously calibrated based on the identified patient behaviors.