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All Journal Jurnal Rekayasa elektrika
Andi Majesta, I Made
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Design of Deep Learning-Based Pressure Injury Stage Classification Device Priharti, Wahmisari; Mukhtar, Husneni; Prastha Giriwara, I Made; Andi Majesta, I Made; Bena Bimantara, Wayan Abin
Jurnal Rekayasa Elektrika Vol 21, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v21i2.41353

Abstract

Pressure injury or pressure ulcers could occur due to continuous pressure on the bony prominence of the skin and tissue. Geriatric patients, especially those with limited mobility and several comorbidities, are more susceptible to pressure injury. Stage classification of pressure injury is currently carried out qualitatively and requires clear communication with the patient. This is often not possible in elderly patients due to lack perception of pain causing late detection of pressure injury until they have reached a severe level and can endanger the patient's life. This study proposes a non-contact device in the form of a camera integrated with a convolutional neural network (CNN) model with MobileNet architecture to classify the level of pressure injury. Testing showed a classification accuracy of 83.3% with an average classification duration of 2.24 s. This aiding device is considered to have great potential to improve faster and more accurate pressure injury assessment in clinical settings.
Co-Authors Bena Bimantara, Wayan Abin Husneni Mukhtar Prastha Giriwara, I Made Wahmisari Priharti