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.

A light output device for caring for a skin of a user using artificial intelligence includes a plurality of light sources configured to irradiate light, a memory configured to store a skin care model learned using a deep learning algorithm to infer a facial skin state of the user, a camera configured to capture an image of a face of the user, and a processor configured to acquire a skin state of each part of the face based on a first-type face image captured through the camera and the skin care model, and control light output of the plurality of light sources based on the acquired skin state.

Automated tissue stiffness measurement devices and methods can identify cancerous lesions with high sensitivity and specificity. Systems and methods are presented to measure tissue stiffness using applied force, illumination and imaging techniques. The systems and methods can use structured illumination to characterize a tissue surface.

Disclosed is a wearable device including a sensor array having a plurality of sensors each configured to detect a physical change in epidermis of a corresponding body area; and a body motion determination unit configured to determine movement of a body part based on sensing signals from the plurality of sensors, and determine whether the determined movement corresponds to one of at least one next motion which is able to be derived from a current motion state.

Embodiments of the disclosed technologies include a method of capturing, using a mobile device, a best-focused image of a skin surface of a subject, the method including: setting a camera of the mobile device to a fixed focal length; capturing, using the camera, a current image of a plurality of images of the skin surface, the plurality of images having a sequence and including a first previous image captured, using the camera, previously to the current image and a second previous image captured, using the camera, previously to the first previous image; producing a modified image from the current image; transforming the modified image, using a Laplacian pyramid, to produce a plurality of first luminance values from the modified image and a plurality of second luminance values from the plurality of first luminance values; averaging a plurality of first squared values, each including a square of a corresponding first luminance value of the plurality of first luminance values, to produce a first energy value; averaging a plurality of second squared values, each including a square of a corresponding second luminance value of the plurality of second luminance values, to produce a second energy value; calculating a first ratio of the first energy value to the second energy value; calculating, as an average first energy value of the first previous image, an average of the first energy value, a corresponding first energy value of the first previous image, and a corresponding first energy value of the second previous image; calculating, as an average first ratio of the first previous image, an average of the first ratio, a corresponding first ratio of the first previous image, and a corresponding first ratio of the second previous image; determining that the first previous image is one of a plurality of valid images, where each valid image of the plurality of valid images is an image of the plurality of images and has: a corresponding average first energy value above an energy threshold value; and a corresponding average first ratio approximately equal to 1.0; determining that a first valid image of the plurality of valid images is the best-focused image, where the first valid image has a corresponding average first energy value that is greater than the corresponding average first energy values of: a previous valid image captured immediately before the first valid image; and a subsequent valid image captured immediately after the first valid image; and performing an action associated with the best-focused image.

A wearable device includes an elastically compliant body having a side for attaching to skin of a patient, a plurality of accelerometers for receiving acoustic wave data from acoustic waves transmitted by a transducer, a short-range radio transmitter for transmitting the acoustic wave data to a computing device, a processor for receiving the acoustic wave data from the plurality of accelerometers and providing the acoustic wave data to the short-range radio transmitter, and a battery for providing power to the processor, the plurality of accelerometers, and the short-range radio transmitter. A distance between each accelerometer of the plurality of accelerometers is predetermined. The processor, the plurality of accelerometers, the short-range radio transmitter, and the battery are embedded within the elastically compliant body.

Device logic in a mobile device configures a processor to capture a series of images, such as a video, using a consumer-grade camera, and to analyze the images to determine the best-focused image, of the series of images, that captures a region of interest. The images may be of a textured surface, such as facial skin of a mobile device user. The processor sets a focal length of the camera to a fixed position for collecting the images. The processor may guide the user to position the mobile device for capturing the images, using audible cues. For each image, the processor crops the image to the region of interest, extracts luminance information, and determines one or more energy levels of the luminance via a Laplacian pyramid. The energy levels may be filtered, and then are compared to energy levels of the other images to determine the best-focused image.

Apparatuses and methods are disclosed for generating a quantitative indication of the degree of oiliness of skin. In exemplary embodiments, a difference image generated from parallel- and a cross-polarized images of an area of skin is subjected to an intensity thresholding operation. An oiliness metric is generated based on the average intensity of those pixels whose intensities do not exceed the threshold, and/or on the average intensity of those pixels whose intensities exceed the threshold. An indication based on the metric is generated and output.

Apparatuses and methods are disclosed for assessing the texture of skin using images thereof. In exemplary embodiments, a texture map of an area of skin is generated from a combination of a standard white light image, a parallel-polarized image, and a cross-polarized image of the area of skin. The texture map is then flattened to remove the underlying curvature of the skin. A texture roughness metric is then generated based on the flattened texture map. An image of the texture map and the metric can be displayed to provide visual and alphanumeric representations of the texture of skin, thereby facilitating the comparison of baseline and follow-up images of the skin, such as those taken before and after treatment.

Retinal diseases, such as age-related macular degeneration (AMD), are the leading cause of blindness in the elderly population. Since no known cures are currently present, it is crucial to diagnose the condition in its early stages so that disease progression is monitored. Systems and methods for detecting and mapping the mechanical elasticity of retinal layers in the posterior eye are disclosed herein. A system including confocal shear wave acoustic radiation force optical coherence elastography (SW-ARF-OCE) is provided, wherein an ultrasound transducer and an optical scan head are co-aligned to facilitate in-vivo study of the retina. In addition, an automatic segmentation algorithm is used to isolate tissue layers and analyze the shear wave propagation within the retinal tissue to estimate mechanical stress on the retina and detect early stages of retinal diseases based on the estimated mechanical stress.