Continuous eight-posture classification for bed-bound patients

Pressure ulcer is a prevalent complication for bed-bound patients who are not able to shift their body weights over time. Continuous monitoring of patient´s postures in the bed can be helpful for caregivers in order to keep track of patient´s movements and quality of their repositioning during a day. This information allows hospitals to plan an effective repositioning schedule for each patient. In this paper, a high speed and robust posture classification algorithm is proposed that can be employed in any pervasive patient´s monitoring system. First, a whole-body pressure image is recorded using a commercial pressure mat system. Image enhancement is then applied to the raw pressure images and a binary signature for each different posture is constructed. Finally, using a binary pattern matching technique, a given posture can be classified to one of the known posture classes. Our extensive experiments show that the proposed algorithm is able to predict in-bed postures with more than 97% average accuracy.