4 results about "Blink detection" patented technology

The application discloses an eye blinking detection method and system, a wearable device, and a computer readable storage medium. The method comprises: collecting an electromyography signal of an eye region of a user; detecting an eye blinking event based on the electromyography signal; in a first detection state, when an eye blinking event meeting a first interaction judgment condition is detected, identifying the eye blinking event as an interaction eye blinking event, and outputting a corresponding human-computer interaction instruction; in a second detection state, only when an eye blinking event meeting both the first interaction judgment condition and a second interaction judgment condition is detected, identifying the eye blinking event as an interaction eye blinking event, and outputting a corresponding human-computer interaction instruction; when the first detection state is currently used, after detecting an eye blinking event not meeting the first interaction judgment condition, switching to the second detection state. The application can improve the interaction judgment threshold when natural blinking occurs frequently, inhibit the interference of natural blinking on human-computer interaction, and improve the reliability in the human-computer interaction process.

This disclosure relates to the field of terminal control, specifically to an eye state detection method, device, wearable device, and storage medium. The eye state detection method includes: continuously collecting the wearer's periocular electromyography (EMG) signals and corresponding actual posture information during wearable device operation; determining the wearer's wearing displacement state based on a comparison of the actual signal characteristics of the periocular EMG signals with preset reference EMG signal characteristics, and / or the actual posture information; if a wearing offset is determined, correcting blink detection parameters or eye movement detection parameters based on the current periocular EMG signals; and performing eye state detection on the wearer based on the corrected blink detection parameters or eye movement detection parameters. It can autonomously trigger the correction of blink detection parameters or eye movement detection parameters without human intervention, effectively ensuring the wearable device's responsiveness and system stability.

The application provides an eye blinking detection method and device based on a dynamic threshold, equipment and a storage medium. The eye blinking detection method based on the dynamic threshold comprises the following steps: S1, acquiring an electromyographic signal set; the electromyographic signal set comprises electromyographic signals of an eye region of a user corresponding to a current time window and / or at least one time window before the current time window; S2, extracting an eye blinking feature parameter of each electromyographic signal in the electromyographic signal set; S3, updating an eye blinking detection threshold based on the eye blinking feature parameter; and S4, performing eye blinking detection by using the updated eye blinking detection threshold. The method can dynamically update the eye blinking detection threshold based on the electromyographic signals of the user, realize adaptive adjustment of the threshold, and help improve the accuracy of detecting eye blinking of the user.

The application provides a driver dry eye risk assessment method and device and electronic equipment. First, key point positioning is performed on the facial image data of the driver collected by the driver monitoring system, and then blink detection, eye rubbing detection and squeeze blinking detection are performed based on the key point positioning results. Then, core basic feature extraction is performed based on the blink detection results, eye rubbing detection results and squeeze blinking detection results, and a time sequence is constructed based on the core basic feature extraction results. Then, based on the time sequence, a dry eye risk score prediction is performed through a pre-trained deep learning model. Finally, based on the dry eye risk score prediction result, a dry eye risk level determination is performed according to a preset risk level determination rule. The driver dry eye disease risk can be quantitatively and accurately evaluated by using the application, thereby ensuring driving safety.