Abstract: A speech control method includes: receiving a target voice instruction; performing semantic parsing on the target voice instruction, and acquiring a semantic parsing result; and in response to that the semantic parsing result matches a keyword, responding to the target voice instruction by using an application corresponding to the keyword; or in response to that the semantic parsing result does not match a keyword, determining a target service type based on the semantic parsing result, and acquiring an application list corresponding to the target service type; determining a recommended application from the application list by using a first recommendation policy; and responding to the target voice instruction by using the recommended application.

Abstract: A video data-based system for blood pressure prediction. The video data-based system for blood pressure prediction comprises: a first device and a cloud server, where the first device comprises a first master control module, a first camera, a lighting module, a display screen, and a first communication; the cloud server comprises a second master control module, a validity validation module, a parameter validation module, a data preprocessing module, an artificial intelligence blood pressure prediction module, and a second communication module. The use of the video data-based system for blood pressure prediction provided in the embodiments of the present disclosure obviates the need to wear any specific collecting device for the real-time detection and continuous monitoring of the blood pressure, thus reducing the difficulty of implementing the real-time detection and monitoring of a test subject, and enriching the application scenarios of photoplethysmography in the field of guardianship.

Abstract: a method for predicting blood pressure by fusing calibrated photoplethysmography (PPG) signal data includes obtaining real-time PPG signal data, calibrated PPG signal data, calibrated diastolic blood pressure data, and systolic blood pressure data; using a CNN+ANN model to calculate the relative blood pressure data, and generating an ANN output tensor, according to the calibrated diastolic blood pressure and systolic blood pressure data, and the ANN output tensor, performing blood pressure data calculation and generating a blood pressure tensor; if the predicted type information is a first type, performing a mean value calculation according to the blood pressure tensor, and generating diastolic blood pressure prediction data and systolic blood pressure prediction data; if the predicted type information is a second type, performing data extraction processing on the blood pressure tensor, and generating a diastolic blood pressure prediction data sequence and a systolic blood pressure prediction data sequence.

Abstract: A blood pressure prediction method includes performing data fragment division on the pulse wave data; generating an input data tensor according to a total fragment number and the pulse wave data ; performing multilayer convolution and pooling calculation on the input data tensor by using a blood pressure CNN to generate a feature data tensor; generating an input data matrix according to the feature data tensor; performing feature data regression calculation on the input data matrix by using a blood pressure ANN to generate a blood pressure regression data matrix; when a prediction mode identifier is a mean prediction identifier, performing mean diastolic pressure data calculation and mean systolic pressure data calculation to generate mean diastolic pressure data and mean systolic pressure data; or, when the prediction mode identifier is a dynamic prediction identifier, extracting diastolic and systolic pressure data to generate a predicted blood pressure data sequence.

Abstract: Disclosed are a heartbeat data classification method and device based on a point R.