基于改进SVM分类器的动作识别方法

利用智能手机加速度传感器信号,提出一种改进的动作识别方法以降低传统动作识别方法的复杂程度,提高识别率。在特征提取时用盲选法,即用PCA(principal component analysis)进行特征值的降维和去除多维间的干扰,而所选特征没有对应的物理意义;并在分类识别中将遗传算法应用到SVM(support vector machine)分类器参数优化中。通过实验表明,该方法能够对日常的走路、站立、跑及上下楼等动作进行准确的识别。

A gesture-recognition algorithm based on improved SVM

An improved action recognition method is proposed based on the signals acquired by a smart phone acceleration sensor to reduce the complexity of the traditional action recognition method and enhance the recognition rate. The blind selection method is applied in feature extraction stage, which means using principal component analysis (PCA) method to reduce dimensionality and eliminate multi-dimensional interference, while the selected features have no corresponding physical significance. In classification and identification, the genetic algorithm is used to optimize support vector machine (SVM) classifier. Experimental results indicate that the proposed method can accurately recognize actions such as walking, standing, running and climbing stairs.