Benchmarking ‘Radio Exercises’ Recognition with a Three-axis AccelerometerReviewed
Akio Terumoto, Sozo Inoue, Yuichi Hattori,
SMC Workshop on Robust machine learning techniques for human activity recognition
In this paper, we introduce a method for recognizing actions in the 1st part of Radio Exercises, with a single smart phone equipped with three-axis accelerometers, which is placed in the breast pocket. Radio Exercises is a warm-up calisthenics performed with music and chants. We obtained the acceleration data of smart phones placed at the breast pockets to a fixed direction of 4 members. For gathered 44 action data, we extracted time windows, and calculated the Fourier transformation for each axis, and adopted the values of from 0.25Hz to 5Hz as a feature vector. Upon the feature vector, we applied principal component extraction, and applied several machine learning algorithms to from 1st to 8th principal components. The benchmarked value resulted in 64.10% of accuracy in the best method, and we could find out what action is easier or difficult to recognize.