论文标题
使用可穿戴IMU的卷积神经网络阵列用于手语识别
Convolutional Neural Network Array for Sign Language Recognition using Wearable IMUs
论文作者
论文摘要
手势识别算法的进步导致了手语翻译的显着增长。通过使用有效的智能模型，可以精确地识别标志。拟议的工作提出了一种新颖的一维卷积神经网络（CNN）阵列体系结构，以使用从定制设计的可穿戴IMU设备中录制的信号来识别印度手语的标志。 IMU设备利用三轴加速度计和陀螺仪。使用IMU设备记录的信号是根据其上下文隔离的，例如它们是否对应于签署一般句子或疑问句子。该数组由两个单独的CNN组成，一个分类为一般句子，另一个对疑问句进行了分类。将数组体系结构中各个CNN的性能与对未分类数据集进行分类的常规CNN进行了比较。一般句子的峰值分类精度为94.20％，而拟议中有CNN阵列实现的疑问句子为95.00％，而常规CNN的峰值句子准确性为93.50％，主张拟议方法的适用性。
Advancements in gesture recognition algorithms have led to a significant growth in sign language translation. By making use of efficient intelligent models, signs can be recognized with precision. The proposed work presents a novel one-dimensional Convolutional Neural Network (CNN) array architecture for recognition of signs from the Indian sign language using signals recorded from a custom designed wearable IMU device. The IMU device makes use of tri-axial accelerometer and gyroscope. The signals recorded using the IMU device are segregated on the basis of their context, such as whether they correspond to signing for a general sentence or an interrogative sentence. The array comprises of two individual CNNs, one classifying the general sentences and the other classifying the interrogative sentence. Performances of individual CNNs in the array architecture are compared to that of a conventional CNN classifying the unsegregated dataset. Peak classification accuracies of 94.20% for general sentences and 95.00% for interrogative sentences achieved with the proposed CNN array in comparison to 93.50% for conventional CNN assert the suitability of the proposed approach.
