基于惯性传感器和深度学习的上肢动作识别研究

Upper limb motion recognition based on inertial senor and deep learning

基于传感器的上肢动作识别技术,研究的热点主要在于传感器的种类、数量,特征值的选择以及分类算法的设计上,这些因素决定了识别效果的优劣。针对这些问题,提出一种基于多个惯性传感器和深度学习的上肢动作识别方法。使用惯性传感器模块采集上肢动作时手腕和肘部的加速度、角速度数据,在预处理后提取如均值、最大(小)值、频率等9种特征值,然后采用主成分分析法对特征集进行降维处理,在减小计算量的同时使其具有更好的鲁棒性,最后使用深度信念网络训练数据,对6种上肢动作进行分类。实验结果表明,该方法可完成多种上肢动作的识别,以最少的传感器数量完整的描述了复杂的上肢动作,而深度信念网络相较于传统的支持向量机、人工神经网络等算法,有着更高的识别精度。

The research focus of sensor-based upper limb motion recognition technology mainly lies in the type and number of sensors,the selection of feature values and the design of classification algorithm.These factors determine the advantages and disadvantages of the recognition effect.Aiming at these problems,this paper proposes an upper limb motion recognition method based on multiple inertial sensors and deep learning.When using the inertial sensor module to collect upper limb movements of the wrist and elbow acceleration and angular velocity data,after the pretreatment extraction such as mean value,maximum(small),frequency of nine kinds of characteristic value,then USES the principal component analysis(pea)to dimension of feature set,in reducing the amount of calculation and at the same time make it has better robustness,finally using deep belief network training data,classify six upper limb movements.Experimental results show that this method can recognize a variety of upper limb movements,and describe complex upper limb movements wit-h the minimum number of sensors.Compared with traditional SVM and ANN algorithms,deep belief network has higher recognition accuracy.