Non-Contact Heart Rate Monitoring Method Based on Multi-Source Data Fusion
Source abstract
Abstract
This paper proposes a non-contact heart rate long-time monitoring system based on multi-source data fusion. Microwave radar cannot associate the identity of a target with its signal, whereas rPPG can achieve this through facial recognition. Additionally, rPPG technology is unable to monitor heart rate in completely dark environments, while visible light is not a prerequisite for radar-based heart rate monitoring. Consequently, this paper proposes a method for heart rate monitoring that fuses microwave and video data. The methodology involves preprocessing both microwave and video data, extracting specific features of different types of data, and identifying the heart rate by the signal features. In the experiments, the identification accuracy for heart rates ranging from 57 to 171 bpm was 73.1%, with accuracies of 75.8% for heart rates below 60 bpm and 89.9% for heart rates above 120 bpm. Compared to single-source data, the accuracy increased by 25.4% and 28.6%, respectively. The monitoring duration is approximately 30 s and achieves model optimization through algorithm deployment. These results validate the effectiveness and timeliness of the proposed method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Non-Invasive Vital Sign Monitoring · Heart Rate Variability and Autonomic Control · ECG Monitoring and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
Systems Engineering Institute, Academy of Military Science, People’s Liberation Army, Tianjin 300161, China
Funding
Tianjin Education Commission
References (34)
Show source-provided references
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