A system includes a sensor for measuring a skin parameter. The sensor includes at least three spatially separated light sources for providing unpolarized visible light, a detector located at a first distance from each of the light sources selected from the range of 10-80 mm and at a second distance from the skin, and a polarizer including one or more of a segmented polarizer and a spatially varying polarizer. In a sensing mode, the light sources are configured to sequentially illuminate the skin with the light with optical axes at an angle of incidence selected from the range of 10°-80°, and the detector is configured to sequentially detect light reflected from the skin and generate corresponding detector signals.

A tissue hydration monitor and method includes a sensor module having a plurality of LEDs positioned to emit a plurality of different wavelengths of light toward the user's skin and a detector that detects light transmitted and reflected through the user's skin to generate signals corresponding to an intensity of detected light at each of the different wavelengths. A processor/controller module generates a baseline hydration level based on the received signals, calculates a relative hydration level, and generates an output indicative of relative hydration personalized to the user. The housing is secured against the user's skin by an adhesive patch or a strap.

An antioxidant sensor includes a pressure sensor configured to obtain a contact pressure between an object and an optical sensor; the optical sensor configured to, based on the obtained contact pressure exceeding a set threshold pressure, emit a first light of a first wavelength to the object, and receive the first light reflected or scattered from the object; and a processor configured to determine a contact portion of the object in contact with the optical sensor, set a threshold pressure, among different threshold pressures, according to the determined contact portion, and determine an antioxidant value based on the received first light.

A display device including a display panel that displays an image, an input sensor disposed on the display panel, and a readout circuit that receives sensing signals from the input sensor and outputs a moisture level signal, in a skin measurement mode. The readout circuit divides the input sensor into a plurality of blocks, selects a valid sensing block based on the sensing signals from the plurality of blocks, and outputs the moisture level signal based on the sensing signals from the valid sensing block.

A portable skin condition measuring apparatus according to an embodiment of the present disclosure is capable of complexly measuring and analyzing various skin condition indexes of a user by coming into contact with a user's face, body, or scalp. The portable skin condition measuring apparatus may solve inconvenience in measuring a user's skin condition and improve efficiency of measurement, determination, and care of the user's skin condition by measuring and analyzing various skin condition indexes of the user in a non-face-to-face manner.

A Raman sensor includes a light source assembly having a plurality of light sources configured to emit light to a plurality of skin points of skin, each of the plurality of skin points having a predetermined separation distance from a light collection region of the skin from which Raman scattered light is collected; a light collector configured to collect the Raman scattered light from the light collection region of the skin; and a detector configured to detect the collected Raman scattered light.

A system, method, and computer readable media are provided for obtaining a first set of skin data from an image capture system including at least one ultraviolet (UV) image of a user's skin. Performing a correction on the skin data using a second set of skin data associated with the user. Quantifying a plurality of skin parameters of the user's skin based on the first skin data, including quantifying a bacterial load. Quantifying the bacterial load by applying a brightness filter to isolate portions of the at least one UV image containing fluorescence, applying a dust filter, identifying portions of the at least one UV image that contain fluorescence due to bacteria, and determining a quantity of bacterial load in the users skin. Determining, using a machine learning model, an output associated with a normal skin state of the user and a current skin state of the user.

A skin state determination method includes: acquiring a first measurement result including data obtained by a spectral camera measuring the skin of a user at a first time point, second time points before the first time point being different from each other; estimating, on the basis of the first measurement result, a first estimation result including at least one of a skin moisture content or hidden blemish conditions of the user at the first time point; and determining a skin state of the user on the basis of the first estimation result and second estimation results based on second measurement results, each of the second measurement results including data obtained by the spectral camera measuring the skin of the user at each of the second time points.

The present disclosure provides methods and apparatus for evaluating tissue structure in damaged or healing tissue. The present disclosure also provides methods of identifying a patient at the onset of risk of pressure ulcer or at risk of the onset of pressure ulcer, and treating the patient with anatomy-specific clinical interventions selected, based on TEWL measurements. The present disclosure also provides methods of stratifying groups of patients based on risk of wound development and methods of reducing incidence of tissue damage in a care facility. The present disclosure also provides methods to analyze trends of TEWL values to detect tissue damage before it is visible, and methods to compare bisymmetric TEWL values to identify damaged tissue.

A system and method for precision skincare that uses a set of skin measurements to generate a skin profile and a skin need and the skin need is used to identify and predict an optimal skincare product formulation customized for each user based on the skin need of the user. The optimal skincare product formulation may include one or more active ingredients selected based on the skin profile and skin need of the user and a delivery mechanism for the one or more active ingredients. The optimal skincare product formulation may be generated using machine learning techniques and may include outcome feedback that optimizes the machine learning models.