Activity recognition from interactions with objects using dynamic Bayesian network

Authors:
Tomohito Inomata;Futoshi Naya;Noriaki Kuwahara;Fumio Hattori;Kiyoshi Kogure
Affiliations:
ATR Knowledge Science Laboratories, Kyoto, Japan and Ritsumeikan University, Shiga, Japan;ATR Knowledge Science Laboratories, Kyoto, Japan;Kyoto Institute of Technology, Kyoto-shi, Kyoto, Japan;Ritsumeikan University, Shiga, Japan;ATR Knowledge Science Laboratories, Kyoto, Japan
Venue:
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Year:
2009

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Abstract

A nursing activity recognition method from nurses' interactions with the tools and materials he/she touched has been developed for preventing the cause of medical accidents and incidents. The method detects an interaction between a nurse and a tool or material by using a RFID tag system. From interaction data, activities are recognized by using the Dynamic Bayesian Network (DBN) framework. This paper focuses on recognizing the twelve activity steps in the drip injection task. In an experiment, we obtained the 95.4% accuracy in recognizing these steps.