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6 pages, 628 KB

Open AccessProceeding Paper

Digital Twin-Orchestrated IoT Architecture for Patient Wayfinding in Complex Healthcare Facilities

by Tudor-Costin Bizu and Adrian Gligor

Eng. Proc. 2026, 148(1), 37; https://doi.org/10.3390/engproc2026148037 - 17 Jul 2026

Viewed by 250

In healthcare facilities, patient wayfinding challenges—especially in complex, multi-unit or campus-scale healthcare environments—extend the patient journey and increase front-desk workload. This work investigates the integration of a Digital Twin-orchestrated, IoT-enabled architecture that links digital scheduling to in-clinic guidance through standardized tokens. The proposed approach relies on (i) an administrative mapping layer that binds unique QR identifiers to cabinets, specialties, clinicians, and human-readable location labels, (ii) an appointment confirmation workflow that issues a confirmation code and delivers an e-mail package including a QR token and an RFC 5545-compliant (Internet Calendaring and Scheduling Core Object Specification) attachment, and (iii) a kiosk-like model (embedded, single-board computer with camera-based QR scanning) that resolves tokens via REST endpoints and presents deterministic guidance using a finite-state-machine workflow with explicit fallback from appointment resolution to cabinet-level QR mapping. An optional biometric module is included for recurrent visits via a database linkage layer, enforced by a single-owner rule for the serial sensor interface to prevent concurrency faults. Scenario-based validation confirms end-to-end operability and robustness, with time–motion quantification scheduled for future on-site evaluation. Full article

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27 pages, 2928 KB

Open AccessArticle

ML-RASPF: A Machine Learning-Based Rate-Adaptive Framework for Dynamic Resource Allocation in Smart Healthcare IoT

by Wajid Rafique

Algorithms 2025, 18(6), 325; https://doi.org/10.3390/a18060325 - 29 May 2025

Cited by 13 | Viewed by 1888

Abstract

The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable service demands, network congestion, and end-to-end delay constraints. Consistently meeting the stringent QoS requirements of smart healthcare, particularly for life-critical applications, requires new adaptive architectures. We propose ML-RASPF, a machine learning-based framework for efficient service delivery in smart healthcare systems. Unlike existing methods, ML-RASPF jointly optimizes latency and service delivery rate through predictive analytics and adaptive control across a modular mist–edge–cloud architecture. The framework formulates task provisioning as a joint optimization problem that aims to minimize service latency and maximize delivery throughput. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices that generate both latency-sensitive and latency-tolerant service requests. Experimental results demonstrate that ML-RASPF achieves up to 20% lower latency, 18% higher service delivery rate, and 19% reduced energy consumption compared to leading baselines. Full article

(This article belongs to the Special Issue AI-Driven Solutions for Smart Systems in Engineering, Computing, Education, and Society)

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10 pages, 1371 KB

Open AccessArticle

Quality Characteristics and Acceptance Intention for Healthcare Kiosks: Perception of Elders from South Korea Based on the Extended Technology Acceptance Model

by Uk Kim, Taerin Chung and Eunsik Park

Int. J. Environ. Res. Public Health 2022, 19(24), 16485; https://doi.org/10.3390/ijerph192416485 - 8 Dec 2022

Cited by 7 | Viewed by 4055

Abstract

This study aimed to perform a path analysis to understand the effects of quality characteristics on perceived usefulness, perceived ease to use, involvement, and acceptance intention of healthcare kiosks in elderly using the extended technology acceptance model. We performed structural equation modeling (SEM) with data from 300 elderly. The following results were obtained. Firstly, elderly’s perceived quality characteristics of healthcare kiosks had a partial positive effect on perceived usefulness. Secondly, elderly’s perceived quality characteristics of healthcare kiosks had a partial positive effect on perceived ease to use. Thirdly, elderly’s perceived ease to use healthcare kiosks had a partial positive effect on perceived usefulness. In addition, elderly’s perceived usefulness of healthcare kiosks had a positive effect on acceptance intention. Lastly, elderly’s perceived ease to use healthcare kiosks had a positive effect on acceptance intention. Full article

(This article belongs to the Section Health Care Sciences & Services)

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