Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors
| Autor: | Georges Matar, Jean-Marc Lina, Georges Kaddoum |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Supine position Local binary patterns Computer science Polysomnography Posture Feature extraction Beds 02 engineering and technology 01 natural sciences Cross-validation Young Adult Cohen's kappa Health Information Management Pressure 0202 electrical engineering electronic engineering information engineering Humans Electrical and Electronic Engineering Pressure Ulcer Artificial neural network business.industry Textiles 020208 electrical & electronic engineering 010401 analytical chemistry Signal Processing Computer-Assisted Pattern recognition Backpropagation 0104 chemical sciences Computer Science Applications Histogram of oriented gradients Female Neural Networks Computer Artificial intelligence business Biotechnology |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 24:101-110 |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2019.2899070 |
| Popis: | Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video surveillance, the conventional method of HBLP monitoring, suffers from various limitations, such as subject's privacy, and field-of-view obstruction. We propose an autonomous method for classifying the four state-of-the-art HBLPs in healthy adults subjects: supine, prone, left and right lateral, with no sensors or cables attached on the body and no constraints imposed on the subject. Experiments have been conducted on 12 healthy adults (age 27.35 $\pm$ 5.39 years) using a collection of textile pressure sensors embedded in a cover placed under the bed sheet. Histogram of oriented gradients and local binary patterns were extracted and fed to a supervised artificial neural network classification model. The model was trained based on the scaled conjugate gradient backpropagation. A nested cross validation with an exhaustive outer validation loop was performed to validate the classification's generalization performance. A high testing prediction accuracy of 97.9% with a Cohen's Kappa coefficient of 97.2% has been interestingly obtained. Prone and supine postures were successfully separated in the classification, in contrast to the majority of previous similar works. We found that using the information of body weight distribution along with the shape and edges contributes to a better classification performance and the ability to separate supine and prone postures. The results are satisfactorily promising toward unobtrusively monitoring posture for ulcer prevention. The method can be used in sleep studies, post-surgical procedures, or applications requiring HBLP identification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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