Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.

Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference

Human motion classification based on micro-Doppler effect has been widely used in various fields. However, the motion classification performance would be greatly degraded if the wireless environment has non-target micro-motion interference. In this case, the interference signal aliases with the signal of target human motions and then generates cross-terms, making the signals hard to be used to identify target human motions. Existing methods do not consider this non-target micro-motion interference and have poor resistance to such interference. In this paper, we propose a target human motion classification system that can work in the scenarios with non-target micro-motion interference. Specifically, we build a continuous wave radar transceiver working in a low-frequency radar band using the software defined radio equipment Universal Software Radio Peripheral (USRP) N210 to collect signals. Moreover, we use Empirical Mode Decomposition and S-transform successively to remove non-target micro-motion interference and improve the time-frequency resolution of the raw signal. Then, an Energy Aggregation method based on S-method is proposed, which can suppress cross-terms and background noise. Furthermore, we extract a set of features and classify four human motions by adopting Bagged Trees. Extensive experiments using the test-bed show that under the scenarios with non-target micro-motion interference, 97.3% classification accuracy can be achieved.