Described here is a deep brain stimulation (DBS) approach that targets several relevant nodes within brain circuitry, while monitoring multiple symptoms for efficacy. This approach to multi-symptom monitoring and stimulation therapy may be used as an extra stimulation setting in extant DBS devices, particularly those equipped for both stimulation and sensing. The therapeutic efficacy of DBS devices is extended by optimizing them for multiple symptoms (such as sleep disturbance in addition to movement disorders), thus increasing quality of life for patients.

In some examples, a system may include a plurality of electrodes, electrical stimulation circuitry, and a controller. The controller may be configured to select one or more parameters of therapy to be delivered to a brain of a patient and to control the electrical stimulation circuitry to deliver the therapy to the brain of the patient based on the selected parameters and via a first one or more electrodes of the plurality of electrodes. The parameters may be defined based on a first plurality of electrical signals sensed at a plurality of different positions within the brain of the patient when electrical stimulation is not delivered at each of the positions and a second plurality of electrical signals sensed at each of the plurality of different positions within the brain of the patient in response to electrical stimulation delivered at each of the positions at a plurality of different intensities.

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.

A sensor includes a bridge circuit including one or more strain gauges mounted on a nail plate, the bridge circuit outputting a voltage signal, an amplifier circuit amplifying the voltage signal output by the bridge circuit to generate an amplified signal, an analog-to-digital (A/D) converter converting the amplified signal into a digital signal, a controller receiving the digital signal and facilitating communication with a receiver, and an antenna configured to transmit the digital signal.

Systems and methods for digitographic measurement of Parkinson's disease in accordance with embodiments of the invention are illustrated. One embodiment includes a digitographic measurement device, including a housing, a plurality of keys attached to the housing, a plurality of sensors, sensor in the plurality of sensors is associated with a key in the plurality of keys and is capable of measuring the amplitude of a key press, and an input/output interface within the housing, where the digitographic measurement device is configured to transmit key press data generated by the sensors to a controller via the input/output interface, and where the key press data is used to generate a plurality of digitographic metrics that describe motor symptoms of a user of the digitographic measurement device.

Systems, methods, and devices for automatic generation of a stimulation therapy that mimics electrographic activity in the brain at natural seizure termination define a stimulation therapy to be generated by an implanted component of a medical device system and delivered to a subject through identifying data characterizing a patient's seizures, especially at termination. A machine learning model identifies the seizures or seizure types from which to establish a canonical seizure or seizure type, and an algorithm translates the canonical seizure or seizure type into data that can be used to characterize a stimulation therapy. The systems, methods, and devices, include those configured to deliver the stimulation therapy that emulates the canonical seizure or seizure type when the seizure is detected, with the aim of terminating the seizure sooner than it would terminate without intervention.

A sleeve that may be worn on a limb of a user is disclosed. The sleeve may be used to assess shape, position, and movement of the limb, as well as biometric properties of the user. The sleeve may include a geometric pattern of conductive elastic material interspersed with non-conductive elastic material. A plurality of sensor units may be integrated in the sleeve with portions of the conductive elastic material coupled between sensor units. The sensor units may assess resistances of the portions of the conductive elastic material between the sensor units. A positioning component may be positioned at a joint of the limb when the sleeve is worn. The sleeve may include inertial measurement units integrated in the sleeve and positioned on upper and lower portions of the joint. A processor may receive and assess data from the sensor units and the first and second inertial measurement units.

Disclosed embodiments describe techniques for motion analysis based on human body mounted sensors. The motion analysis is based on human body mounted sensors using mapping and motion analysis. The wearable sensors include inertial measurement sensors, muscle activation sensors, stretch sensors, or linear displacement sensors. Data is obtained from two or more sensors attached to a body part of an individual, where the two or more sensors enable collection of motion data of the body part, and where the two or more sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation. The data is processed to determine locations of each of the two or more sensors. The locations of each of the two or more sensors are mapped into a coordinate reference system. The mapping is provided to a motion analysis system. Additional data is obtained to further calculate body part motion.

Disclosed embodiments describe techniques for motion analysis based on human body mounted sensors. The motion analysis enables diagnosis and treatment using mapping and motion analysis. The wearable sensors include inertial measurement sensors, muscle activation sensors, stretch sensors, or linear displacement sensors. Data is obtained from two or more sensors attached to a body part of an individual, where the two or more sensors enable collection of motion data of the body part, and where the two or more sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation. The locations of each of the two or more sensors are mapped into a coordinate reference system, based on the data. Motion of the human body is calculated based on the mapping. A movement signature is determined based on the calculated motion. The movement signature is used to analyze a movement disorder of the individual.

Systems and methods for tracking unintentional muscle movements of a user and stabilizing a handheld tool while it is being used by the user are described. The method may include detecting motion of a handle of the handheld tool manipulated by a user while the user is performing a task with a user-assistive device attached to an attachment arm of the handheld tool. Furthermore, the method may include storing the detected motion in a memory of the handheld tool as motion data. The method may also include controlling, based on the motion data, a motion-generating mechanism of the handheld tool that moves the attachment arm relative to the handle in a degree of freedom.