In-Bed Posture Classification Using Pressure Data from a Sensor Sheet Under the Mattress - André Serra; Fernando Ribeiro; José Metrôlho (Information)
Monitoring and controlling the condition of bedridden individuals can help reduce health risks, as improper nocturnal habits or body positioning can exacerbate issues such as apnea, insomnia, sleep disorders, spinal problems, and pressure ulcers. Techniques using pressure maps from sensors placed on top of the mattress, along with machine learning (ML) algorithms to classify main postures (prone, supine, left side, right side), have achieved up to 99% accuracy. This study evaluated the feasibility of using a sensor sheet placed under the mattress to minimize patient discomfort. Experiments with ten commonly used ML algorithms achieved average accuracy values ranging from 79.14% to 98.93% using K-Fold cross-validation and from 80.03% to 97.14% using Leave-One-Group-Out (LOGO) for classifying the four main postures. The classification was extended to include 28 posture variations (7 variations for each of the 4 main postures), with the SVM algorithm achieving an accuracy of 65.18% in K-Fold validation, marking a significant improvement over previous studies, particularly regarding the number of postures considered. Comparisons with previous studies that used pressure sensors placed both under and on top of the mattress show that this approach achieves comparable accuracy to other methods, surpassing them with some algorithms and achieving the highest average accuracy. In conclusion, using sensors under the mattress is an effective and less invasive alternative for posture classification.