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

Authors
Kasno, Mohammad AfifChoi, Yong-SikJung, Jin-Woo
Issue Date
Mar-2026
Publisher
MDPI
Keywords
drone-mounted camera; remote photoplethysmography (rPPG); moving-average rPPG (MA-rPPG); contactless heart-rate monitoring; unmanned aerial vehicle (UAV); smart mirror interface; feasibility study; real-time physiological sensing
Citation
Applied Sciences, v.16, no.5, pp 1 - 17
Pages
17
Indexed
SCIE
SCOPUS
Journal Title
Applied Sciences
Volume
16
Number
5
Start Page
1
End Page
17
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/64039
DOI
10.3390/app16052307
ISSN
2076-3417
Abstract
Featured Application A feasibility-stage experimental platform for evaluating real-time moving-average remote photoplethysmography (MA-rPPG) using a drone-mounted sensing assistant integrated with a smart mirror interface for flexible, contactless physiological monitoring. A previously validated smart mirror system is reused as a unified visualization and synchronization frontend, enabling controlled laboratory data acquisition and compared with MA-rPPG measurements obtained from a fixed desktop camera setup using the same model.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.
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College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)
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