Adaptive multiple classifiers fusion for inertial sensor based human activity recognition

Aiming at the poor accuracy of single classifier in recognizing daily activities based on single accelerometer, this paper presents a method of daily activity recognition based on ensemble learning and full information matrix based fusion weight. Firstly, features from three attributes are extracted from the acceleration signals respectively. The three kinds of features can well describe the information of the activity, and they are relatively independent, which can reduce the interference caused by information redundancy in the process of fusion. Then three base classifiers of support vector machines are constructed based on three kinds of features respectively. Secondly, Euclidean distance between the test sample and every training sample for each type of feature vector is calculated to find out the k nearest neighbors of the test sample from the training set by the K-nearest neighbour method. The cluster analysis is used to compute the similarity between every neighbor and the test sample. Then, a proper threshold is utilized to remove the invalid neighbor whose similarity is less than the threshold. According to the effective neighbor, the full information matrix is constructed to calculate the accuracy. The weight of every single classifier is set dynamically according to the accuracy. Experiments showed that our proposed method get the best average recognition accuracy of 94.79% among several other weight functions when using majority voting method, besides, the time cost is also appealing.