Indoor navigation app of healthcare facilities using machine learning algorithms

The rapid population growth in Jordan due to migration trends has placed significant pressure on the country’s healthcare infrastructure, further strained by the emergence of new diseases. To address the challenge of indoor navigation within hospitals, this research leverages Location-Based Services...

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Veröffentlicht in:Applied geomatics Jg. 17; H. 4; S. 771 - 789
Hauptverfasser: Alsaaideh, Maysa, Al-Bayari, Omar, Alsaaidah, Bayan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Artificial intelligence
Comparative analysis
Deep learning
Earth and Environmental Science
Efficiency
Geographical Information Systems/Cartography
Geography
Geophysics/Geodesy
Global positioning systems
GPS
Health care
Health facilities
Hospitals
Indoor navigation
Infrastructure
Layouts
Literature reviews
Localization
Location based services
Machine learning
Mapping
Measurement Science and Instrumentation
Navigation systems
Obstacle avoidance
Optimization
Planning
Population growth
Real time
Remote Sensing/Photogrammetry
Sensors
Smart cities
Smartphones
Surveying
User experience
Healthcare facilities navigation app
DQN
OpenAI gym
Indoor navigation
Location-based services
Dueling DQN
Deep reinforcement learning
DQL
ISSN:1866-9298, 1866-928X
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Zusammenfassung:The rapid population growth in Jordan due to migration trends has placed significant pressure on the country’s healthcare infrastructure, further strained by the emergence of new diseases. To address the challenge of indoor navigation within hospitals, this research leverages Location-Based Services (LBS) to develop an intelligent navigation application. The study focuses on optimizing pathfinding within the first floor of Al-Istishari Hospital in Amman using Deep Q-Learning (DQL) models to enhance accessibility and efficiency in hospital environments. The indoor navigation system was developed using Python’s Tkinter library and features a custom HospitalGrid environment. OpenAI Gym was integrated to simulate agent-environment interactions, enabling reinforcement learning agents to navigate hospital layouts efficiently while avoiding obstacles. A comparative analysis with Dueling Deep Q-Network (Dueling-DQN) was conducted under consistent hyperparameter settings. Results show that DQL provides more stable performance in structured environments, while Dueling-DQN offers improved learning efficiency in complex layouts due to its separation of state value and action advantage estimations. Although further optimization is needed in terms of Mean Squared Error (MSE) and return values, the proposed system demonstrates strong potential for hospital navigation and provides a foundation for future real-time healthcare applications.
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ISSN:1866-9298
1866-928X
DOI:10.1007/s12518-025-00648-0