5 results about "Closed eyes" patented technology

This invention relates to the field of intelligent driving assistance technology, specifically disclosing a driver state monitoring system based on intelligent image recognition. Built on a Raspberry Pi embedded platform, it includes a video acquisition module, a core algorithm processing module, and a peripheral control module. The video acquisition module is compatible with USB or CSI cameras, acquiring and preprocessing driver facial images. The core algorithm processing module incorporates a lightweight facial key point detection model to extract facial features and identify dangerous behaviors such as closed eyes and head tilting, and features a feature calibration and fatigue grading mechanism. The peripheral control module connects to alarm and display components, triggering corresponding warning actions and can be expanded to include multiple alarm methods. The system can also track continuous driving time and expand remote monitoring functionality. This invention features low hardware cost, low power consumption, convenient deployment, fast edge-side inference response, and accurate monitoring. It is adaptable to multiple scenarios, requires no manual intervention, comprehensively prevents fatigue driving, and is easy to deploy on a large scale.

A method includes receiving, by a processor, a video of an illuminated face. Video frames in the video are associated with different time instants of a time series, respectively. The method includes determining a region of interest (ROI) that includes a closed eye on the face based on at least one frame in a plurality of video frames in the video. The method includes slicing each ROI based on slice parameters for selecting a vertical slice that includes a eye centroid of the closed eye, and selecting a horizontal slice that intersects the vertical slice at a pivot (P) located lp pixels from the eye centroid. The method includes determining an eyeball motion signal according to the time series, based on a measured value of a shadow intensity of the eye within at least one of the vertical slice or the horizontal slice at each of the different time instants.

The application relates to a line-of-sight calibration, motion tracking and precision testing method based on adaptive time sequence analysis prediction, which specifically comprises the following steps: through image color recognition technology, color blocks and calibration point positions in a screen are recognized in a scene image, the actual size of the screen is combined, and the screen position is roughly positioned; distortion calibration is carried out based on the calibration point information, the screen position is precisely positioned, and the mapping relationship between the scene image and the precisely positioned screen is determined; eye control type nine-point calibration is carried out, the mapping model of the eyeball physical information and the gazing space position of the tester is determined, the screen gazing point coordinates are obtained in combination with the mapping relationship; line-of-sight positioning precision quantitative testing is carried out; through an adaptive time sequence analysis prediction algorithm, the current line-of-sight positioning is optimized and the future gazing point position is predicted in combination with historical line-of-sight information, meanwhile, the parameters in the time sequence analysis prediction algorithm are adaptively adjusted according to real-time precision or the intensity of shaking; through an eye state detection algorithm, the state of the human eye in the open and closed eye state is detected, and eye movement control is carried out.

The application discloses a meditation posture adjustment method based on a light neural network and an acoustic sense, and has the characteristics that the method specifically comprises the following steps: collecting real-time video data in a user's meditation process, detecting and extracting features of human body skeleton key points of an image by using a light neural network model, analyzing key point position information to calculate the deviation of a current posture from a standard meditation posture, matching corresponding voice action instructions according to the deviation type, playing voice guidance to guide the user to adjust the body action in real time, and cyclically monitoring the posture change and dynamically updating the voice feedback until the posture is corrected. The application effectively reduces the calculation complexity of the model to adapt to mobile terminal deployment, and innovatively uses voice instructions to replace visual feedback, solves the pain point that the user needs to open eyes to confirm the posture in the meditation process and destroys the immersion, realizes real-time and accurate posture correction in a closed eye state, and has good application prospect.

The application discloses a kind of based on brain-heart coupling feature's children hyperactivity autism comorbid auxiliary evaluation system, comprising: signal acquisition module, for synchronous acquisition of the subject under the two resting states of open eye rest and closed eye rest Electroencephalogram and electrocardiogram signal;Signal pre-processing module is used to filter and artifact removal to the collected electroencephalogram, and R wave detection and marking are carried out to electrocardiogram;Heartbeat evoked potential feature extraction module is used to extract heartbeat evoked potential feature;Heartbeat evoked brain network feature extraction module is used to construct heartbeat evoked brain network based on the functional connection change in R wave before and after time window, and graph theory feature is extracted;Auxiliary evaluation module is used to output the auxiliary evaluation result of brain-heart coupling state based on heartbeat evoked potential feature and graph theory feature.Using the application, the objective auxiliary evaluation of children attention deficit hyperactivity disorder comorbid autism spectrum disorder related brain-heart coupling abnormality can be realized.