Non-contact heart rate variability feature extraction method based on real application scene

Abstract

The invention discloses a non-contact heart rate variability feature extraction method based on a real application scene. The method comprises the following steps: 1) collecting an image containing a human face; 2) acquiring an image face area by using a face detection and tracking combined strategy; 3) enhancing skin color change through Euler amplification; 4) color space conversion and channel separation; 5) self-adaptive threshold skin detection; 6) extracting a source signal; (7) EEMD denoising; 8) carrying out five-point sliding smoothing filtering; 9) peak point detection and abnormal peak point correction; and 10) RR interval calculation and time domain, frequency domain and nonlinear HRV feature extraction. According to the method, the strategy for improving the feature extraction speed and the strategy for overcoming the influence of shaking and illumination and improving the feature extraction accuracy are integrated in the feature extraction method, the HRV features can be rapidly extracted in a non-contact mode in a real application environment, and it is guaranteed that the HRV features are consistent with contact type extraction results.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for faster and more accurate analysis of cardiovascular health by adding an algorithm called Non Contact HeartRate Variation (NFC) Analysis with Realism Applications scenarios. By combining this technique with various factors like shake or brightness during testing, it becomes possible to improve the quality of NFC data collected from these environments without affecting their effectiveness over time.

Problems solved by technology

This patented technical solution described in the text describes how cardiovascular function can vary over time depending upon external stimuli like exercise stressors. It suggests detecting these variations with cameras attached to people's bodies during physical activities. These techniques involve analyzing images captured while they actively participate in their own movements without being affected by other things around them.

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