Complementary Event Motion Classification for Robust Identification and Care Work Identification Using Hidden Semi-Markov Model

Abstract

In this paper, we propose a work description by elemental motion and a work identification method for creating detailed care records. This method describes tasks as a time-series of elemental motion and identifies care works. In order to achieve robust work identification against irregular motion and annotation of incorrect motion labels during a work, some elemental motion are integrated into one class as complementary event motion. Using the time-series data of elemental motion information, our proposed work identification method utilizing Hidden Semi-Markov Models can recognize performed care works based on the frequency distribution of state transitions. In the experiment, three simulated care works were measured and identified. In addition, simulations using artificial data were conducted to verify the robustness of the proposed method. Furthermore, we measured actual caregiving tasks at a nursing home to confirm whether the task detection was feasible. The results demonstrated the effectiveness of the proposed system.