センサデータと業務知識からのトピックモデルを用いた看護業務行動の推定

抄録

Thanks to the popularization of information and communication technology, the nurses work using mobile devices to communicate with co-workers and record nursing care at hospital. In this paper, aiming to facilitate nursing care, we propose a method to recognize nursing work activities by using topic models from acceleration data stored in mobile devices and knowledge of the work. In contrast to simple tasks such as walking or running, working activities are more difficult to recognize because of their complexity and length. To address this difficulty, we define the system composed of two layers, simple task recognition layer and working activity recognition layer, based on the assumption that work activities consist of a probabilistic combination of various simple tasks. In the simple task recognition layer, the system first recognizes simple task by applying supervised learning techniques to time-domain features extracted from sensor data. Then it recognizes working activities by applying topic models to simple tasks and annotation with knowledge of nursing work. We conducted an experiment at a hospital and collected nursing activity data for 96 hours by 12 nurses as a result. Using those data, we show that our method surpasses the conventional methods in recognizing nursing activities.