Disclosed are methods, systems and non-transitory computer readable memory for gesture control. For instance, a system may include a wearable device configured to be worn on a portion of an arm of a user. The wearable device may include a plurality of electrodes disposed on an interior of the wearable device and configured to obtain biopotential signals from the user's arm; and a biopotential chip. The biopotential microchip may be configured to output, directly or indirectly, biopotential data, acceleration data, and/or angular rate data, or derivatives thereof (“gesture data”), to a machine learning classifier. The machine learning classifier may be configured to generate, based on the gesture data, a gesture output indicating a gesture performed by the user. In some cases, the plurality of electrodes may include one or more wristband electrodes and/or a plurality of hub electrodes in a hub. In some cases, the hub may be curved.

Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management.

A posture measuring device, an intelligent cushion and an intelligent seat are disclosed. The sitting posture measuring device comprises two fiber optic sensors; a signal processing unit electrically connected to the two fiber optic sensors respectively; and a power supply unit electrically connected to the signal processing unit. The sitting posture measuring device further comprises a prompting unit and/or a wireless communications unit electrically connected to the signal processing unit. The two fiber optic sensors are configured from left to right or from front to back. The fiber optic sensors detect changes in optical signals generated from changes in surface pressure on the sensors, and the signal processing unit analyzes a posture of a user on the basis of changes in optical signals generated from changes in surface pressure on the sensors.

Embodiments of the presently described device, system and method support upper extremity (UE) therapy through tracking the human hand. The system can include a hand-wearable sensor mounting system with hand-wearable components and a movement interpretation circuit. The device can include a hand-wearable component comprising a hook, and a sensing transducer comprising a clip, wherein the clip is detachably securable to the hook. In embodiments, one or more sensing transducers are translationally and rotationally restricted when secured to the hand-wearable components.

A system for monitoring medical conditions including pressure ulcers, pressure-induced ischemia and related medical conditions comprises at least one sensor adapted to detect one or more patient characteristic including at least position, orientation, temperature, acceleration, moisture, resistance, stress, heart rate, respiration rate, and blood oxygenation, a host for processing the data received from the sensors together with historical patient data to develop an assessment of patient condition and suggested course of treatment, including either suspending or adjusting turn schedule based on various types of patient movement. Compliance with Head-of-Bed protocols can also be performed based on actual patient position instead of being inferred from bed elevation angle. The sensor can include bi-axial or tri-axial accelerometers, as well as resistive, inductive, capacitive, magnetic and other sensing devices, depending on whether the sensor is located on the patient or the support surface, and for what purpose.

Systems and methods are provided for performing intraoperative fusion of two or more volumetric image datasets via surface-based image registration. The volumetric image datasets are separately registered with intraoperatively acquired surface data, thereby fusing the two volumetric image datasets into a common frame of reference while avoiding the need for complex and time-consuming preoperative volumetric-to-volumetric image registration and fusion. The resulting fused image data may be processed to generate one or more images for use during surgical navigation.

A system to perform remote oculography includes light-occluding goggles configured to be worn by a patient. The light-occluding goggles include an infrared camera positioned to capture one or more first images of a first eye of the patient. The light-occluding goggles also include a display positioned such that it is viewable by a second eye of the patient. The display is configured to display a pattern for the patient to view. The light-occluding goggles also include a sensor configured to detect information regarding a position of a head of the patient. The system also includes a visible light camera configured to capture one or more second images of the patient as the patient wears the light-occluding goggles.

A patient tracking device for insertion into an oral cavity includes a sensor housing comprising a first surface shaped to correspond to a pallet within the oral cavity. At least a portion of the first surface affixes the sensor housing to the oral cavity. An electromagnetic sensor is coupled to the sensor housing.

A system and/or method for diagnosing a neurologic condition. The system comprises a wearable head orientation sensor, a wearable eye imaging module, and a display with a visual target. The head orientation sensor is responsive to pitch or yaw head orientation information. The eye imaging module is configured for imaging a characteristic of the retina, sclera, cornea, limbus, or pupil to determine an eye position or eye movement. An electronic circuit in the system determines an ocular parameter measurement in response to the head orientation sensor and the eye imaging module. The neurologic condition is diagnosed in response to the ocular parameter measurement.

Systems and methods are disclosed for evaluating human eye tracking. One method includes receiving data representing the location of and/or information tracked by an individual's eye or eyes before, during, or after the individual performs a task; identifying a temporal phase or a biomechanical phase of the task performed by the individual; identifying a visual cue in the identified temporal phase or biomechanical phase; and scoring the tracking of the individual's eye or eyes by comparing the data to the visual cue.