Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 1; pp. 101 - 110
• Main Authors: Matar, Georges, Lina, Jean-Marc, Kaddoum, Georges
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Adults; Artificial neural networks; Back propagation; Bed pressure ulcer prevention; Beds; body pressure mapping; Body weight; Cables; Classification; Data acquisition; Data models; Feature extraction; Female; Field of view; Hardware; histogram of gradients; Histograms; human body lying posture; Humans; local binary patterns; Male; Monitoring; neural network; Neural networks; Neural Networks, Computer; Polysomnography - methods; Posture; Posture - physiology; Pressure; pressure sensor mattress; Pressure sensors; Pressure Ulcer - prevention & control; Pressure ulcers; Prevention; Sensors; Signal Processing, Computer-Assisted; Sleep; supervised learning; Textiles; Ulcers; unobtrusive sleep monitoring; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2899070

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 ± 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.