Abstract
In recent years, sensor-based human activity recognition has attracted lots of studies. This paper presents a single wearable triaxial accelerometer-based human activity recognition system, which can be used in the real life of activity monitoring. The sensor is attached around different parts of the body: waist and left ankle, respectively. In order to improve the accuracy and reduce the computational complexity, the ensemble empirical mode decomposition (EEMD)-based features and the feature selection (FS) method are introduced, respectively. Considering the feature interaction, a game theory-based FS method is proposed to evaluate the features. Relevant and distinguished features that are robust to the placement of sensors are selected. In the experiment, the data acquired from the two different parts of the body, waist and ankle, are utilized to evaluate the proposed FS method. To verify the effectiveness of the proposed method, k-nearst neighbor and support vector machine are used to recognize the human activities from waist and ankle. Experiment results demonstrate the effectiveness of the introduced EEMD-based features for human activity recognition. Compared with the representative FS methods, including Relief-F and minimum-redundancy maximum-relevance, the proposed FS approach selects fewer features and provides higher accuracy. The results also show that the triaxial accelerometer around the waist produces optimal results.