A deep learning-based real-time hypothermia and hyperthermia monitoring system with a simple body sensor.
Proc Inst Mech Eng H
; 238(7): 827-836, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-39104260
ABSTRACT
A real-time hypothermia and hyperthermia monitoring system with a simple body sensor based on a Convolutional Neural Network (CNN) is presented. The sensor is produced with 3D-printed thermochromic material. Due to the color change feature of thermochromic materials with temperature, 3D-printed thermochromic Polylactic Acid (PLA) material was used to monitor temperature changes visually. In this paper, we have used the transfer learning technique and fine-tuned the AlexNet CNN. Thirty images for each temperature class between 28-44°C and 510 image data were used in the algorithm. We used 80% and 20% of the data for training and validation. We achieved 96.1% accuracy of validation with a fine-tuned AlexNet CNN. The material's characteristics suggest that it could be employed in delicate temperature sensing and monitoring applications, particularly for hypothermia and hyperthermia.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Hipertermia
/
Hipotermia
Límite:
Humans
Idioma:
En
Revista:
Proc Inst Mech Eng H
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido