期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 65, 期 6, 页码 1330-1338出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2017.2750139
关键词
Classification algorithms; gait recognition; legged locomotion; patient monitoring
资金
- National Science Foundation Graduate Research Fellowship Program [1445197]
Objective: This paper describes a gait classification method that utilizes measured motion of the thigh segment provided by an inertial measurement unit. Methods: The classification method employs a phase-variable description of gait, and identifies a given activity based on the expected curvature characteristics of that activity over a gait cycle. The classification method was tested in experiments conducted with seven healthy subjects performing three different locomotor activities: level ground walking, stair descent, and stair ascent. Classification accuracy of the phase variable classification method was assessed for classifying each activity, and transitions between activities, and compared to a linear discriminant analysis (LDA) classifier as a benchmark. Results: For the subjects tested, the phase variable classification method outperformed LDA when using nonsubject-specific training data, while the LDA outperformed the phase variable approach when using subject-specific training. Conclusions: The proposed method may provide improved classification accuracy for gait classification applications trained with nonsubject-specific data. Significance: This paper offers a new method of gait classification based on a phase variable description. The method is shown to provide improved classification accuracy relative to an LDA pattern recognition framework when trained with nonsubject-specific data.
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