Activity Recognition Based on an Accelerometer in a Smartphone Using an FFT-Based New Feature and Fusion Methods

摘要

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance. two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01, which results in an improvement in accuracy of 4.14 compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.