Critical Patient Image Data Acquisition Strategy by Exploiting Edge Intelligence and Dynamic-Static Synergy in Smart Healthcare

Kiran Deep Singh¹, Prabh Deep Singh², Narinder Kaur³, Jawad Khan^4,*, Dildar Hussain⁵, Yeong Hyeon Gu^5,*
1 Department of Computer Science & Engineering, Chitkara University Insitute of Engineering and Technology, Chitkara University, Punjab, India
2 Department of Computer Science & Engineering, Graphic Era (Deemed to be University), Dehradun, Uttrakhand, India
3 Department of Computer Science & Engineering, Chandigarh University, Mohali, Punjab, India
4 School of Computing, Gachon University, Seongnam, Republic of Korea
5 Department of AI and Data Science, Sejong University, Seoul, Republic of Korea
* Corresponding Author: Jawad Khan. Email: email ; Yeong Hyeon Gu. Email: email
(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.080915

Received 09 March 2026; Accepted 15 April 2026; Published online 11 May 2026

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Abstract

In smart healthcare systems, Image data of critical patients is essential in controlling and diagnosing the disease development. To acquire the medical images, traditional methods encountered the difficulty of generating cost-effective data. This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations. Initially, a new Internet of Medical Things (IoMT) image collection architecture is introduced. This design uses edge intelligence and motion-static synergy to make it easier to record both coarse-grained and fine-grained patient images. This study introduces an image acquisition technique that leverages edge intelligence and collaborative static-dynamic monitoring, exemplified in intensive care units, to improve the efficiency and data value of image acquisition in healthcare IoMT settings. This approach revolves around the three distinct steps. To begin with, an advanced YOLO-based clinical abnormality detection is implemented by the edge server to identify patients affected by abnormal physiological conditions. The images from affected patients are captured by static monitoring nodes. In the next phase, coordinate calculation methods for the localization of abnormal patients and quantification techniques for severity assessment are introduced. The final step involves the intervention of a path optimization algorithm for mobile medical assistive robots using severity metrics and principles of ant colony optimization. Ultimately, algorithmic performance evaluations at every phase indicate that acquisition efficiency and image data value surpass traditional methodologies.