2 results about "Heart rate average" patented technology

This application discloses a method and system for extracting heart rate and heart rate variability features based on electrocardiogram (ECG) signals. The process is as follows: acquiring ECG signals and preprocessing them to improve the accuracy of subsequent R-peak detection; performing R-peak detection; setting a time window and calculating heart rate and heart rate variability feature indicators based on the NN interval sequence. The time window is selected and set to a standard duration of 5 minutes or a short duration of 1-2 minutes. The heart rate feature indicators include instantaneous heart rate and average heart rate within the time window. The heart rate variability feature indicators include time-domain indicators and frequency-domain indicators. The time-domain indicators include the standard deviation SDNN of all NN intervals and the root mean square difference RMSSD of adjacent NN intervals. The frequency-domain indicators include low-frequency power LF, high-frequency power HF, and the LF / HF ratio. The heart rate and heart rate variability feature indicators are output at predetermined time intervals to form time-series data, and the changing trend is displayed in real time or analyzed offline. This application adapts to dynamic monitoring needs and can jointly calculate multiple types of feature indicators.

This invention discloses a multimodal dynamic weighted motion evaluation method, system, and storage medium. It acquires motion posture data from motion video streams; processes the acquired electrocardiogram (ECG) data to extract heart rate features, including average heart rate, heart rate variability, and respiratory periodicity; for each joint sequence, an improved DTW algorithm is used to calculate the similarity score with standard motion. This improved DTW algorithm incorporates a curved path optimization mechanism to reduce the computation of invalid paths; a dynamic weight model is constructed based on the random forest algorithm, dynamically allocating weights to each part according to motion complexity, part importance, and physiological indicators; and a comprehensive evaluation is performed by fusing motion posture data and heart rate data to classify evaluation levels. By allocating dynamic weights based on motion complexity and other factors, and fusing multimodal data to obtain a comprehensive evaluation, it achieves accurate and comprehensive motion evaluation.