论文标题
使用深神经网络对可穿戴IMU的手势分类
Classification of Hand Gestures from Wearable IMUs using Deep Neural Network
论文作者
论文摘要
IMU在手势分析，轨迹检测和运动学功能研究的领域中变得非常重要。惯性测量单元（IMU）由三轴加速度计和陀螺仪组成，陀螺仪可以一起用于形成分析。本文使用深神网络（DNN）提出了一种新型的分类方法，以分类从可穿戴IMU传感器获得的手势。为分类器设置了一个优化目标，以减少活动之间的相关性并使用最佳性能参数拟合信号集。网络的培训是通过输入特征的进率向前计算进行的，然后是错误的后传播。将预测的输出以分类精度的形式进行分析，然后将其与SVM和KNN的常规分类方案进行比较。在DNN分类的情况下，观察到精度的提高了3-5％。显示了记录的加速度计和陀螺仪信号以及所考虑的分类方案的结果。
IMUs are gaining significant importance in the field of hand gesture analysis, trajectory detection and kinematic functional study. An Inertial Measurement Unit (IMU) consists of tri-axial accelerometers and gyroscopes which can together be used for formation analysis. The paper presents a novel classification approach using a Deep Neural Network (DNN) for classifying hand gestures obtained from wearable IMU sensors. An optimization objective is set for the classifier in order to reduce correlation between the activities and fit the signal-set with best performance parameters. Training of the network is carried out by feed-forward computation of the input features followed by the back-propagation of errors. The predicted outputs are analyzed in the form of classification accuracies which are then compared to the conventional classification schemes of SVM and kNN. A 3-5% improvement in accuracies is observed in the case of DNN classification. Results are presented for the recorded accelerometer and gyroscope signals and the considered classification schemes.
