Digital twin platform for neonatal incubator management
| dc.contributor.author | RAHMOUNI ikram | |
| dc.contributor.author | LAYADI sidahmed | |
| dc.contributor.author | BENYEZZA Hamza (Encadreur) | |
| dc.contributor.author | BOUHEDDA Mounir (Co-Encadreur) | |
| dc.date.accessioned | 2026-02-01T12:57:47Z | |
| dc.date.available | 2026-02-01T12:57:47Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This end of study project aims to create a digital twin for efficient neonatal incubator management. An ESP32 board is connected to environmental and biomedical sensors (temperature, humidity, oxygen, heart rate) to collect real-time data and sent it to Firebase. LSTM models are used to predict environmental conditions and infant comfort. An interactive 3D interface built with React and Blender allows intuitive data visualization. Test results demonstrate the proposed system’s effectiveness in enhancing neonatal care, particularly in resource-limited healthcare settings. | |
| dc.identifier.uri | https://dspace.univ-medea.dz/handle/123456789/14086 | |
| dc.language.iso | en | |
| dc.relation.ispartofseries | M25 98; M125 98 | |
| dc.title | Digital twin platform for neonatal incubator management | |
| dc.type | Thesis |