基于多信号特征和D-S证据理论融合的下肢运动模式识别  

Lower Limb Motion Pattern Recognition Based on Multi-signal Feature and D-S Evidence Theory Fusion

作　　者：陈伟 田一明 王喜太 

机构地区：[1]国家康复辅具研究中心北京市老年功能障碍康复辅助技术重点实验室民政部神经功能信息与康复工程重点实验室,北京100176

出　　处：《科技创新与应用》2018年第1期25-27,共3页Technology Innovation and Application

基　　金：国家科技支撑计划项目"专业化养老护理服务体系研究与应用示范"(编号:2015BAI06B03)

摘　　要：针对下肢运动模式识别中出现的单一信号特征识别方法不能准确、可靠地识别出下肢的运动模式,以及多信号特征识别方法所带来的维数灾难问题。提出一种利用神经网络特征级融合多信号特征以及D-S证据理论对预识别结果进行决策级融合的下肢运动模式识别方法。对采集到的下肢表面肌电信号以及髋关节信号进行特征提取,对两种来自不同信号源的特征向量分别建立各自的神经网络,利用神经网络的特征级融合属性得出不同信号源特征对下肢动作的预识别结果。最后利用D-S证据理论融合来自不同信号源特征对待识别动作的概率信度,以实现决策级融合的目的。通过文中方法对五种下肢常见动作的识别效果进行了验证。实验结果表明,文章方法相比于单一信号源特征的识别方法具有更高的可靠性和正确率。In view of the single signal feature recognition method which is adopted in the lower limb motion pattern recognition and can not accurately and reliably identify the lower limb motion pattern and the dimension disaster caused by the multi-signal fea-ture recognition method, a method of lower limb motion pattern recognition using neural network feature level fusion and D-S evi-dence theory for decision level fusion of pre-recognition results is proposed. The features of the lower limb surface muscle signals and hip joint signals were extracted, and the neural networks were established for the two kinds of feature vectors from different sig-nal sources. The feature level fusion attribute of neural network is used to obtain the pre-recognition results of different signal source features to lower limb motion. Finally, D-S evidence theory is used to fuse the probability reliability of different signal sources deal-ing with the recognition action, in order to achieve the purpose of decision level fusion. The recognition effect of five common lower extremity movements is verified by the method of this paper. The experimental results show that the proposed method is more reliable and accurate than the single signal source recognition method.

关 键 词：运动模式 数据融合 多源信息 神经网络 D-S证据理论 特征提取 

分 类 号：R318.04[医药卫生—生物医学工程]