Human motions always contain only motions of some body parts, but much of the existing methods on action recognition don’t take the motion intensity of each joint into account, which lower the accuracy of action recognition in some extent. To solve this problem, an adaptive joint weighting scheme is proposed to calculate the weight of each joint and combined the weights with dynamic time warping (DTW) to recognize actions. Firstly, the action sequence was segmented into several segments and some most violent joints in each segment are assigned higher weight while the remaining joints are evenly weighted. Then feature vectors of two action sequences were extracted and the distance between two action sequences were calculated by DTW. Finally the action recognition was achieved by K-nearest neighbor method. The experiments showed that the overall classification accuracy of the proposed method is higher, and the result is also good for some similar actions.%大多数动作仅包含部分关节的运动，现有方法未对运动剧烈的关节与几乎不参与运动的关节进行区分，一定程度上降低了动作识别精度。针对这个问题，提出一种自适应关节权重计算方法。结合动态时间规整(DTW)方法，利用获得的关节权重进行动作识别。首先对分类动作序列进行分段，每段动作序列中运动较剧烈的关节选择分配更高权重，其余关节平均分配权重；然后提取特征向量，计算两段动作序列的DTW距离；最后采用K近邻方法进行动作识别。实验结果表明，该算法的总体分类识别准确率较高，且对于较相似的动作也能获得较好的识别结果。
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