journal article·2026-02-27Open access

Feasibility of Drone-Mounted Camera for Real-Time MA-rPPG in Smart Mirror Systems

Mohammad Afif Kasno, Yong-Sik Choi, Jin-Woo Jung

Applied Sciences· Volume 16· Issue 5

Abstract

Remote photoplethysmography (rPPG) enables contactless estimation of cardiovascular signals from video, but most existing studies assume a fixed, stationary camera. This study investigates the feasibility of performing real-time moving-average rPPG (MA-rPPG) using a drone-mounted camera, where platform motion, vibration, and viewing distance introduce additional challenges. Building on our previously validated real-time MA-rPPG smart mirror platform, we reuse the smart mirror interface as a unified frontend for visualization, synchronization, and logging while adapting the MA-rPPG pipeline to operate on live video streamed from an off-the-shelf DJI Tello micro-drone. Feasibility experiments were conducted with 10 participants under controlled indoor lighting and constrained flight conditions, where the drone maintained a stable hover in front of a standing subject and facial video was processed in real time to estimate heart rate from a forehead region of interest. To avoid cross-modality bias and clarify the effect of the aerial imaging platform, drone-derived MA-rPPG outputs were compared against a fixed desktop-camera MA-rPPG reference using the same trained model, enabling a controlled, like-for-like evaluation. The results indicate that continuous heart-rate estimation from a drone camera is feasible in our controlled hover-only setup, while agreement tended to vary with hover stability and effective facial resolution. This work is presented strictly as a feasibility-stage investigation and does not claim clinical validity. The findings provide an experimental baseline and operating-envelope insight for future motion-robust rPPG on mobile and aerial health-sensing platforms.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Non-Invasive Vital Sign Monitoring · Optical Imaging and Spectroscopy Techniques · Photoacoustic and Ultrasonic Imaging

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Computer Science and Artificial Intelligence, College of Advanced Convergence Engineering, Dongguk University, Seoul 04620, Republic of Korea

Faculty of Electrical Technology and Engineering, Universiti Teknikal Malaysia Melaka, Melaka 76100, Malaysia

Funding

Ministry of Trade, Industry and Energy · MSIT · Artificial Intelligence Convergence Innovation Human Resources Development · Korea government

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