Human actions recognition from motion capture recordings using signal resampling and pattern recognition methods
该研究通过将运动捕捉数据转换为位置不变表示，并利用神经网络、随机森林和支持向量机等分类器，在健身房练习和空手道技术数据集上实现了97%至100%的动作识别准确率。
In this paper we will experimentally prove that after recalculating the motion capture (MoCap) data to position-invariant representation it can be directly used by classifier to successfully recognize various actions types. The assumption on classifier is that it is capable to deal with objects that are described by hundreds of numeric values. The second novelty of this paper is application of neural network trained with the parallel stochastic gradient descent, Random Forests and Support Vector Machine with Gaussian radial basis kernel to perform classification task on gym exercises and karate techniques MoCap datasets. We have tested our approach on two datasets using k-fold cross-validation method. Depending of the dataset we have obtained averaged recognition rate from 100 to 97 %. Our results presented in this work give very important hints for developing similar actions recognition systems because proposed features selection and classification setup seems to guarantee high efficiency and effectiveness.