# US 12,762,894 B2 · Electronic device and method for measuring degree of eyelid closure strength > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,894 B2 - Document ID: US12762894B2 - Status: Granted - Kind: B2 - Application number: 18/673,243 - Filed: 2024-05-23 - Published: 2025-11-27 - Granted: 2026-10-13 - Earliest claimed: 2024-05-23 - Assignees: HTC Corporation - Applicants: HTC Corporation - Inventors: Yu-Hsien Liao - Web page: https://patentgrep.com/patent/US12762894B2 ## CPC - G06V 40/197: {Matching; Classification} - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - G06V 10/74: Image or video pattern matching; Proximity measures in feature spaces - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An electronic device and a method for measuring a degree of eyelid closure strength are provided. The method includes: obtaining an image of an eye; detecting at least one wrinkle in the image; determining a degree of eyelid closure strength according to an area of the at least one wrinkle; and outputting the degree of the eyelid closure strength. ## Claims ### Claim 1 An electronic device for measuring a degree of eyelid closure strength, comprising: a transceiver; and a processor, coupled to the transceiver, wherein the processor is configured to: obtain an image of an eye via the transceiver; detect at least one wrinkle in the image; determine a degree of eyelid closure strength according to an area of the at least one wrinkle; and output the degree of eyelid closure strength via the transceiver, wherein the processor is further configured to: perform an edge detection on the image to detect the at least one wrinkle; detect the image to obtain a region of interest; perform Gaussian blurring on the region of interest to obtain a blurred image; calculate a gradient of each pixel of the blurred image; perform non-maximum suppression on the gradient of each pixel to obtain at least one edge; and set the at least one edge as the at least one wrinkle. ### Claim 2 (depends on 1) The electronic device according to claim 1, wherein the processor is further configured to: compare the area of the at least one wrinkle with a reference value to determine the degree of eyelid closure strength. ### Claim 3 (depends on 2) The electronic device according to claim 2, wherein the processor is further configured to: in response to the area being greater than the reference value, determine the degree of eyelid closure strength according to a difference between the area and the reference value. ### Claim 4 (depends on 2) The electronic device according to claim 2, wherein the processor is further configured to: in response to the area being less than or equal to the reference value, determine the degree of eyelid closure strength according to a default value. ### Claim 5 (depends on 1) The electronic device according to claim 1, further comprising: a storage medium, coupled to the processor and stores a machine learning model, wherein the processor is further configured to: input the image into the machine learning model to output the at least one wrinkle in the image. ### Claim 6 (depends on 5) The electronic device according to claim 5, wherein the processor is further configured to: train the machine learning model according a training image based on a supervised learning algorithm, wherein the training image is labeled with an actual wrinkle. ### Claim 7 (depends on 6) The electronic device according to claim 6, wherein a loss function of the supervised learning algorithm comprises a binary cross entropy. ### Claim 8 (depends on 1) The electronic device according to claim 1, wherein the processor is further configured to: calculate the area of the at least one wrinkle according to a number of pixels in the at least one wrinkle. ### Claim 9 A method for measuring a degree of eyelid closure strength, comprising: obtaining an image of an eye; detecting at least one wrinkle in the image; determining a degree of eyelid closure strength according to an area of the at least one wrinkle; and outputting the degree of the eyelid closure strength, wherein the step of performing the edge detection on the image to detect the at least one wrinkle comprising: performing an edge detection on the image to detect the at least one wrinkle; detecting the image to obtain a region of interest; performing Gaussian blurring on the region of interest to obtain a blurred image; calculating a gradient of each pixel of the blurred image; performing non-maximum suppression on the gradient of each pixel to obtain at least one edge; and setting the at least one edge as the at least one wrinkle. ### Claim 10 (depends on 9) The method according to claim 9, wherein the step of determining the degree of eyelid closure strength according to the area of the at least one wrinkle comprising: comparing the area of the at least one wrinkle with a reference value to determine the degree of eyelid closure strength. ### Claim 11 (depends on 10) The method according to claim 10, wherein the step of determining the degree of eyelid closure strength according to the area of the at least one wrinkle further comprising: in response to the area being greater than the reference value, determining the degree of eyelid closure strength according to a difference between the area and the reference value. ### Claim 12 (depends on 10) The method according to claim 10, wherein the step of determining the degree of eyelid closure strength according to the area of the at least one wrinkle further comprising: in response to the area being less than or equal to the reference value, determining the degree of eyelid closure strength according to a default value. ### Claim 13 (depends on 9) The method according to claim 9, wherein the step of detecting the at least one wrinkle in the image comprising: inputting the image into a machine learning model to output the at least one wrinkle in the image. ### Claim 14 (depends on 13) The method according to claim 13, further comprising: training the machine learning model according to a training image based on a supervised learning algorithm, wherein the training image is labeled with an actual wrinkle. ### Claim 15 (depends on 14) The method according to claim 14, wherein a loss function of the supervised learning algorithm comprises a binary cross entropy. ### Claim 16 (depends on 9) The method according to claim 9, further comprising: calculating the area of the at least one wrinkle according to a number of pixels in the at least one wrinkle.