4 results about "Physiological significance" patented technology

The invention belongs to the technical field of computer vision and aquaculture intelligence, and particularly relates to a living fish activity detection method based on behavior characteristic capture and analysis. According to the method, two attention mechanisms of CBAM and SE are innovatively integrated into the YOLOv11 model for optimization, so that the model can better focus on key features of a fish body, the detection precision and robustness of a small target and fine motion in a complex underwater environment are remarkably improved, and compared with an original model, the mAP of the optimized model is improved to 99%, and the size of the model is reduced to 342 MB. According to the method, the limitation of single feature evaluation is broken through, 'movement speed ', 'movement distance' and'stress jump frequency 'are creatively fused, and a dual calculation and prediction model from behaviors to activity scores is constructed, so that the activity evaluation result is more objective, accurate and comprehensive, and has definite physical and physiological significance.

The invention provides an in-vitro liver perfusion function evaluation method, device and medium based on hyperspectral imaging, a hyperspectral multi-index quantitative model of the liver in the normal-temperature mechanical perfusion process is established, and mapping calculation of oxygen saturation, hemoglobin, perfusion, moisture and hemoglobin index physiological parameters is achieved; spectral features are directly converted into functional parameters with clear physiological significance through a wave band absorption difference ratio and an integral reflection algorithm; a multi-index fusion model (LVI) is provided, and quantitative coupling between image parameters and metabolic parameters (lactic acid and pH) is achieved; a dynamic time sequence evaluation mechanism is formed, the activity change of the liver tissue can be tracked noninvasively, the function recovery trend can be predicted, and evaluation is provided for liver perfusion maintenance and subsequent liver transplantation.

This invention discloses an intelligent state analysis method for anesthetized patients based on physiological data, comprising the following steps: S1, organizing and annotating historical anesthesia data to form executable state recognition rules and establishing a standardized historical case reference library. The beneficial effects of this invention are: by decomposing key interactive substructures such as nerve-circulation, brain-circulation, and respiration-perfusion from dynamic network profiles, coordinating indicators with clear physiological significance, such as baroreflex function index, brain oxygen-blood pressure coupling coefficient, and ventilation-perfusion matching degree, are calculated; these indicators can quantify the coordination of physiological systems under anesthesia from multiple dimensions; the system performs multi-dimensional similarity matching of these real-time indicators with an internalized anesthesia state knowledge base (containing multiple preset state modes), realizing accurate and interpretable independent determination of the anesthesia depth state in three dimensions: sedation, analgesia, and muscle relaxation, overcoming the limitations of traditional single index monitoring.

The invention discloses a multi-modal feature decoupling and classification method and device for foot physiological statuses, and belongs to the technical field of data mining and medical artificial intelligence. The method comprises the following steps: based on a foot multi-modal physiological feature vector provided by a preorder patent, mapping the vector to a decoupling feature space by using a pre-trained feature decoupling network; obtaining at least one decoupling feature subset related to the specific physiological factor; the physiological state category of the foot is then determined through a classifier based on the decoupled feature subset. According to the method, hidden variables with independent physiological significance can be separated from coupled physiological features, interpretable and high-precision automatic classification of foot physiological states is achieved, and a complete technical closed loop from collection to classification is formed by the method and preorder patents.