An electronic endoscope processor includes a converting means for converting each piece of pixel data that is made up of n (n≥3) types of color components and constitutes a color image of a biological tissue in a body cavity into a piece of pixel data that is made up of m (m≥2) types of color components, m being smaller than n; an evaluation value calculating means for calculating, for each pixel of the color image, an evaluation value related to a target illness based on the converted pieces of pixel data that are made up of m types of color components; and a lesion index calculating means for calculating a lesion index for each of a plurality of types of lesions related to the target illness based on the evaluation values calculated for the pixels of the color image.

A footwear system includes a sensorized insole and a charger. The sensorized insole has an insole bulk having a foot-facing upper surface. A sensor is embedded in the insole bulk for measuring a parameter of a user's foot, a battery is embedded in the insole bulk for providing energy to the sensor, and a receiver pod is embedded in the insole bulk and is spaced from the foot-facing upper surface for wirelessly receiving energy and providing energy to the battery. The charger provides energy to the receiver pod, and includes a cable for connecting to an energy source, and a transmitter pod electrically connected to the cable for receiving energy from the cable and wirelessly transmitting energy to the receiver pod. The transmitter pod is positionable against the foot-facing upper surface to wirelessly provide energy to the receiver pod through the insole bulk.

An article of footwear includes an upper and a sole structure with a sole member. The sole member can include a set of apertures that is formed along various surfaces of the sole member. The sole member can be manufactured using a customized cushioning sole system, forming generally circular patterns throughout the sole member. A user's foot morphology and/or preferences may be used to design the sole member.

There is described support structures for a bed, and various embodiments of a bed for use in a hospital. In an example, a support structure comprises a plurality of sections, each configured to support a respective part of a body, and a plurality of resilient members that extend in a longitudinal direction from an upper end of the support structure to a lower end of the support structure, wherein a shape and/or profile of the support structure is determined by a shape and/or profile of the resilient members.

An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

The present invention relates to a system and method of skin detection of a human subject using a textile product. The textile product (10, 31, 43) is made from or comprising textile including near-infrared, NIR, absorbing pigments. It supports and/or partially covers the human subject while skin detection and/or detection/monitoring vital signs of the human subject is carried out. A increased contrast between the textile product and skin in the NIR wavelength range is thus achieved.

Certain aspects of the present disclosure provide methods and apparatus for measuring an injection catheter needle insertion depth and/or injection solution efficacy. An example injection catheter may include a catheter tube and a retractable, electrically conductive needle disposed in the catheter tube and configured to extend from the catheter tube. The injection catheter may also include one or more electrodes disposed at a distal portion of the catheter tube, an electrical lead coupled to the needle, and electrical leads coupled to the electrode(s). An example method includes deploying such an injection catheter adjacent to the tissue, extending a needle into the tissue, receiving electrical signals from an electrical lead coupled to the needle and from other electrical leads coupled to the electrode(s), determining a bioelectrical parameter based on the received electrical signals, and determining a depth of the needle inserted into the tissue based on the bioelectrical parameter.

A method for estimating a metabolic rate of a user includes obtaining pulse oximetry data for the user for a period of time. The method includes determining a rate of decline in oxygen saturation of blood of the user that is associated with a breathing rate of the user for the period of time based, at least in part, on the pulse oximetry data. The method includes estimating the metabolic rate of the user for the period of time based, at least in part, on the rate of decline in the oxygen saturation of the blood that is associated with the breathing rate of the user. The method includes providing a notification indicative of the metabolic rate for the period of time.

Aspects relate to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.

Aspects relate to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.