title: In-bed posture classification using deep autoencoders.
abstract: Pressure ulcers are high prevalence complications among bed-bound patients which are not only extremely painful and difficult to treat, but also impose a great burden in our health-care system. We target automatic posture detection which is a key module in all pressure ulcer monitoring platforms. Using data collected from a commercially-available pressure mapping system, we applied deep neural networks to automatically classify in-bed posture using features extracted from the histogram of gradient technique. High accuracy of up to 98% was achieved in classifying five different in-bed postures for more than 60,000 pressure images.
authors.forename: Mehrdad
authors.initials: M
authors.lastname: Heydarzadeh
authors.forename: Mehrdad
authors.initials: M
authors.lastname: Nourani
authors.forename: Sarah
authors.initials: S
authors.lastname: Ostadabbas
mesh_terms: D001513:Beds; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D016571:Neural Networks, Computer; D011187:Posture; D003668:Pressure Ulcer / Q000175:diagnosis*