著者

村上 知子 鳥居 健太郎 長 健太 内平 直志

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.29, no.5, pp.427-435, 2014-09-01 (Released:2014-07-25)

参考文献数

33

被引用文献数

2 3 1

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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.

著者

村上 知子 瀬戸口 久雄 鳥居 健太郎 内平 直志 Murakami Tomoko Setoguchi Hisao Torii Kentaro Uchihira Naoshi

雑誌

SIG-DOCMAS = SIG-DOCMAS

巻号頁・発行日

no.B102, 2012-03-11

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In this paper we propose a method to estimate working activities from sensor datawithout user annotation. Estimation of working activities are much more difficult than that ofsimple activities such as ’walking’ and ’still’ in terms of their complexity and length. To addressthis issue, we assume that they are a probabilistic combination of various simple activities andpropose a method to discover working activities by multistage estimation, in which firstly classifysensor data into some basic activities and then estimate them by using topic model.We focusedon nursing service as one of professional working activities, in whicn visualization by estimation ofworking activities is critical, conducted the experiment to observe it in the hospital and verifiedour method.