AI Summary & Key Takeaways
AI InsightThus, it has been tried to prevent the person to apply pressure by lying on a single muscle group and tissue for a long time and to prevent the formation of pressure injuries over time.
📌 3 Key Takeaways
Abstract
Within the scope of this study, a wearable lying position tracking system equipped with IMU sensors has been developed to prevent the formation of pressure injuries in bedridden patients. Three IMU sensors were placed on the patient's chest, one on the right upper leg and the other on the left upper leg, and the angular orientation expressions of the limbs were calculated. Datasets were created for three different hospitalization positions, and machine learning and deep neural network models were used to classify the patient's hospitalization type. The success of the classifiers was compared by calculating the accuracy, sensitivity, specificity, precision and F1 score values. The average accuracy values in the lying position classification were obtained as 99.506%, 99.977%, 99.972%, 99.838%, and 99.967% respectively, using Linear discriminant analysis, K-Nearest neighbor, Decision Tree, Support Vector Machine and Random Forest classification methods. The highest accuracy rate was obtained as a result of the K-Nearest neighbor method with high variation. The time that the person remained fixed in the determined lying position was also calculated, and if it remained longer than the specified time, an audible warning signal was generated to change the position. Thus, it has been tried to prevent the person to apply pressure by lying on a single muscle group and tissue for a long time and to prevent the formation of pressure injuries over time.