ArticleBiomedical optics express2022
Heart rate estimation from facial videos with motion interference using T-SNE-based signal separation.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning.Biomedical engineering online · 2025Review
- Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.Frontiers in bioengineering and biotechnology · 2024Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Remote photoplethysmography (RPPG) can detect heart rate from facial videos in a non-contact way. However, head movement often affects its performance in the real world. In this paper, a novel anti-motion interference method named T-SNE-based signal separation (TSS) is proposed to solve this problem. TSS first decomposes the observed color traces into pulse-related vectors and noise vectors using the T-SNE algorithm. Then, it selects the vector with the most significant spectral peak as the pulse signal for heart rate measurement. The proposed method is tested on a self-collected dataset (17 males and 8 females) and two public datasets (UBFC-RPPG and VIPL-HR). Experimental results show that the proposed method outperforms state-of-the-art methods, especially on the videos containing head movements, improving the Pearson correlation coefficient by 5% compared with the best contrasting method. To summarize, this work significantly strengthens the motion robustness of RPPG, which makes a substantial contribution to the development of video-based heart rate detection.
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Registered trials
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