A Phase Variable Approach for IMU-Based Locomotion Activity Recognition.

Abstract

This paper describes a gait classification method that utilizes measured motion of the thigh segment provided by an inertial measurement unit. 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. 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. The proposed method may provide improved classification accuracy for gait classification applications trained with nonsubject-specific data. 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.