title: Classifying bed inclination using pressure images.
abstract: Pressure ulcer is one of the most prevalent problems for bed-bound patients in hospitals and nursing homes. Pressure ulcers are painful for patients and costly for healthcare systems. Accurate in-bed posture analysis can significantly help in preventing pressure ulcers. Specifically, bed inclination (back angle) is a factor contributing to pressure ulcer development. In this paper, an efficient methodology is proposed to classify bed inclination. Our approach uses pressure values collected from a commercial pressure mat system. Then, by applying a number of image processing and machine learning techniques, the approximate degree of bed is estimated and classified. The proposed algorithm was tested on 15 subjects with various sizes and weights. The experimental results indicate that our method predicts bed inclination in three classes with 80.3% average accuracy.
authors.forename: M
authors.initials: M
authors.lastname: Baran Pouyan
authors.forename: S
authors.initials: S
authors.lastname: Ostadabbas
authors.forename: M
authors.initials: M
authors.lastname: Nourani
authors.forename: M
authors.initials: M
authors.lastname: Pompeo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001513:Beds; D049628:Body Size; D001835:Body Weight; D006761:Hospitals; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009735:Nursing Homes; D011187:Posture; D011312:Pressure; D003668:Pressure Ulcer / Q000517:prevention & control*