Tissue surface tracking of tissue features is disclosed. First surface imaged features are tracked based on the first and second time spaced images at a first wavelength. Second surface imaged features are tracked based on the first and second time spaced tissue surface images at the second wavelength. Tracking metrics are obtained based on the tracking steps. The tracking steps are combined to provide a combined tracking metric. The combined tracking metric is used in a tissue surface navigation application.

Provided is a 10-20 system-based position information providing method performed by a computer. The method comprises the steps of: obtaining a head image of a subject; receiving, from a user, an input of at least four reference points on the basis of the head image; calculating central coordinates in the head image on the basis of the at least four reference points; and providing 10-20 system-based position information on the basis of the central coordinates.

Systems and methods for positioning one or more sensors on a user. The system has user sensors, apparatus sensors, and treatment sensors. A processing device, executing computer readable instructions stored in a memory, cause the processing device to: generate an enhanced environment representative of an environment; receive apparatus data representative of a location of the apparatus in the environment; generate an apparatus avatar in the enhanced environment; receive user data representative of a location of the user in the environment; generate a user avatar in the enhanced environment; receive treatment data representative of one or more locations of the treatment sensors in the environment; generate, treatment sensor avatars in the enhanced environment; calculate a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user; and generate instruction data representing an instruction for positioning the treatment sensors at the treatment location.

Embodiments of the present invention provide a security verification method and a device, and relate to the field of communications technologies, so as to verify a user identity based on a wearing status monitoring result and a pairing result of the device. The method includes: monitoring, by a first device, a pairing status of the first device and a second device and/or a wearing status of the first device; receiving a user operation instruction, where the instruction includes information about an interface operated by a user; when it is determined, based on the information about the interface, that the interface is an access-restricted interface, determining a wearing status and/or a pairing status of the first device within a verification time window; determining, based on the wearing status and/or the pairing status within the verification time window, whether the first device is in a secure state; and locking the interface if the first device is in an insecure state; or responding to the user operation instruction if the first device is in the secure state.

Systems and methods are disclosed for proximity sensing in physiological sensors, and more specifically to using one or more proximity sensors located on or within a physiological sensor to determine the positioning of the physiological sensor on a patient measurement site. Accurate placement of a physiological sensor on the patient measurement site is a key factor in obtaining reliable measurement of physiological parameters of the patient. Proper alignment between a measurement site and a sensor optical assembly provides more accurate physiological measurement data. This alignment can be determined based on data from a proximity sensor or sensors placed on or within the physiological sensor.

A method for adjusting the operation of a surgical instrument using machine learning in a surgical suite is disclosed. The method comprises the steps of gathering data during surgical procedures, wherein the surgical procedures include the use of a surgical instrument, analyzing the gathered data to determine an appropriate operational adjustment of the surgical instrument, and adjusting the operation of the surgical instrument to improve the operation of the surgical instrument.

Devices, systems, and methods for non-invasively detecting and monitoring medical conditions using multiple modalities of sensing include at least two electrodes configured to be positioned on a subject, an acoustic sensor configured to be positioned on a subject, a thoracic impedance measurement module connected to the electrodes, for measuring a first impedance between the electrodes, and a heart acoustic measurement module connected to the acoustic sensor, for detecting and measuring a heart sound from the acoustic sensor.

One variation of a method for testing contact quality of electrical-biosignal electrodes includes: outputting a drive signal through a driven electrode, the drive signal comprising an alternating-current component oscillating at a reference frequency and a direct-current component; reading a set of sense signals from a set of sense electrodes at a first time; calculating a first combination of the set of sense signals; calculating a first direct-current value comprising a combination of the first combination and the direct-current component of the drive signal at approximately the first time; and at a second time succeeding the first time, shifting the direct-current component of the drive signal output by the driven electrode to the first direct-current value.

A physiological monitor uses a light source and a number of detectors to determine whether a physiological monitor is positioned for acquisition of physiological data. More specifically, an intensity of the light source, as measured at two photodetectors at different distances from the light source, can be used to accurately detect whether the monitor is properly positioned for use. The disclosed methods may advantageously leverage existing physiological monitoring hardware (such as light emitting diodes and photodetectors), and may improve on the accuracy of prior art techniques using, e.g., capacitive sensors and/or other hardware to detect proper device positioning.

A big data artificial intelligence computer system is used for medical care connecting to sensor-equipped smartphones of users of footwear. The footwear has smartphone-connected soles with sensors and configurable structures. The smartphone is also connected to sensors located on the users' body, including proximate to its center of gravity and/or on the head. The web and/or cloud-based computer system is configured to use the big data techniques of machine learning in a database compiled from millions of smartphones to perform operations on billions of data sets from the smartphones of the footwear users. The correlations found from the big data operations provide solutions to medical problems of the footwear users involving their body structure and/or function. The solutions are implemented by configuring the users' footwear soles, including active configuration, including during running and/or walking to optimize corrections to the structure and/or function of their bodies.