A Vision-Based System for In-Bed Posture Tracking

Shuangjun Liu, Sarah Ostadabbas; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1373-1382

Tracking human sleeping postures over time provides critical information to biomedical research including studies on sleeping behaviors and bedsore prevention. In this paper, we introduce a vision-based tracking system for pervasive yet unobtrusive long-term monitoring of in-bed postures in different environments. Once trained, our system generates an in-bed posture tracking history (iPoTH) report by applying a hierarchical inference model on the top view videos collected from any regular off-the-shelf camera. Although being based on a supervised learning structure, our model is person-independent and can be trained off-line and applied to new users without additional training. Experiments were conducted in both a simulated hospital environment and a home-like setting. In the hospital setting, posture detection accuracy using several mannequins was up to 91.0%, while the test with actual human participants in a home-like setting showed an accuracy of 93.6%.