Evidence map›Paper›PMID 42625201›Full record

ArticleJournal of translational medicine2026

EPI-Sense: an integrated hemodynamic sensing and analysis system for early pressure injury warning.

Ziyang Bao, Yan Jiang, Mingseng Guo, Hua Cao, Wei Cun, Ke Xu, Kesheng Wang

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Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Ziyang BaoSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.ORCID 0009-0009-9792-4835
Yan JiangNursing Department of West China Hospital of Sichuan University, Evidence Based Medicine, Sichuan University, Chengdu, Sichuan, China.
Mingseng GuoSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Hua CaoNursing Department of West China Hospital of Sichuan University, Evidence Based Medicine, Sichuan University, Chengdu, Sichuan, China.
Wei CunNursing Department of West China Hospital of Sichuan University, Evidence Based Medicine, Sichuan University, Chengdu, Sichuan, China.
Ke XuNursing Department of West China Hospital of Sichuan University, Evidence Based Medicine, Sichuan University, Chengdu, Sichuan, China.
Kesheng WangSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. keshengwang@uestc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPressure injury poses a critical challenge in geriatric care. Current computer-aided diagnosis is limited by an exclusive reliance on surface texture, neglecting subcutaneous hemodynamics essential for distinguishing Reactive Hyperemia from Stage 1 PI and detecting Deep Tissue Pressure Injuries. To address this, we constructed a customized cross-polarized acquisition system and a dedicated clinical dataset.

methodsWe present the Physio-Guided Network (PGNet), a multi-modal framework leveraging remote Photoplethysmography (rPPG) to provide complementary non-contact hemodynamic cues related to local microcirculatory dynamics. Specifically, we engineered a Frequency-Domain SNR-Weighted strategy to retrieve attenuated physiological signals from necrotic tissue, and a Physio-Visual Gating Unit for dynamic modality arbitration.

resultsPatient-level leave-one-out validation supports the feasibility of the proposed framework for differentiating morphologically similar early pressure injury states. The results indicate that rPPG-driven hemodynamic guidance provides complementary information beyond visual-only analysis.

conclusionsBy bridging superficial morphology with non-contact hemodynamic sensing, this study demonstrates the clinical relevance and technical feasibility of rPPG-guided multimodal analysis for early pressure injury warning, providing patient-level evidence that supports its potential value for differentiating clinically ambiguous RH, S1, and DTPI cases and laying the groundwork for future multicenter, device-independent validation.

Indexed as

HemodynamicsPressure UlcerHumansPhotoplethysmographyFeasibility analysisGeriatric care monitoringMulti-modal fusionPressure injuryRemote photoplethysmography (rPPG)

Identifiers

PMID42625201
PMCPMC13491974

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