Synergistic Blood Pressure Estimation via Contactless mmWave Radar and Imaging Photoplethysmography: A Feasibility Study

[2606.13701] Synergistic Blood Pressure Estimation via Contactless mmWave Radar and Imaging Photoplethysmography: A Feasibility Study Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Signal Processing arXiv:2606.13701 (eess) [Submitted on 7 Jun 2026] Title: Synergistic Blood Pressure Estimation via Contactless mmWave Radar and Imaging Photoplethysmography: A Feasibility Study Authors: Boyuan Gu , Haiyang Sun , Yongjie Liu , Guangji Ma , Guanghao Sun , Xiaorong Ding View PDF HTML (experimental) Abstract: Continuous, non-contact blood pressure (NCBP) monitoring holds significant promise for pervasive cardiovascular care, yet single-modality approaches -- such as imaging photoplethysmography (iPPG) -- remain constrained by environmental artifacts, skin-tone sensitivity, and the absence of proximal cardiac mechanical information. This study investigates the feasibility of a dual-modality sensing paradigm that synergistically integrates facial iPPG with posterior-facing frequency-modulated continuous wave (FMCW) millimeter-wave radar to capture complementary hemodynamic cues: distal optical volumetric fluctuations and proximal cardiac micro-motions (radar motion signals, RMS). To bridge the morphological disparity between these heterogeneous streams, we develop an end-to-end deep learning architecture, BiLSTM-MS-DiCNN, which leverages multi-scale dilated convolutions for spatial feature extraction and bidirectional long short-term memory for temporal dependency modeling. In a controlled feasibility study involving 15 healthy participants across distinct hemodynamic states (resting, deep breathing, and post-exercise), the proposed framework achieved a Mean Absolute Difference (MAD) of 4.71 mmHg for systolic BP (SBP) and 4.60 mmHg for diastolic BP (DBP) under resting conditions, with consistent performance during physiological perturbations. These preliminary findings demonstrate the viability of mmWave-iPPG fusion as a promising pathway toward robust, unobtrusive NCBP monitoring. Comments: 24 pages, 8 figures Subjects: Signal Processing (eess.SP) Cite as: arXiv:2606.13701 [eess.SP] (or arXiv:2606.13701v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2606.13701 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE SENSORS JOURNAL 2026 Related DOI : https://doi.org/10.1109/JSEN.2026.3706110 Focus to learn more DOI(s) linking to related resources Submission history From: Boyuan Gu [ view email ] [v1] Sun, 7 Jun 2026 03:55:50 UTC (5,462 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-06 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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